test-coprnicus-api-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86e5a787-c78e-40d9-8588-8f88a610bd41",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting copernicusmarine\n",
      "  Downloading copernicusmarine-1.0.4-py3-none-any.whl.metadata (20 kB)\n",
      "Requirement already satisfied: aiohttp>=3.8.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (3.9.1)\n",
      "Requirement already satisfied: boto3>=1.25 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (1.33.13)\n",
      "Collecting cachier>=2.2.1 (from copernicusmarine)\n",
      "  Using cached cachier-3.0.0-py3-none-any.whl.metadata (18 kB)\n",
      "Requirement already satisfied: click>=8.0.4 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (8.1.7)\n",
      "Requirement already satisfied: dask>=2022 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (2024.2.1)\n",
      "Requirement already satisfied: lxml>=4.9.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (5.1.0)\n",
      "Collecting motuclient>=1.8.4 (from copernicusmarine)\n",
      "  Using cached motuclient-3.0.0-py3-none-any.whl\n",
      "Requirement already satisfied: nest-asyncio>=1.5.8 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (1.5.8)\n",
      "Requirement already satisfied: netCDF4>=1.5.4 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (1.6.5)\n",
      "Requirement already satisfied: pydap>=3.2.2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (3.3.0)\n",
      "Requirement already satisfied: pystac>=1.8.3 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (1.9.0)\n",
      "Requirement already satisfied: requests>=2.27.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (2.31.0)\n",
      "Collecting semver>=3.0.2 (from copernicusmarine)\n",
      "  Using cached semver-3.0.2-py3-none-any.whl.metadata (5.0 kB)\n",
      "Requirement already satisfied: setuptools>=68.2.2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (68.2.2)\n",
      "Requirement already satisfied: tqdm>=4.65.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (4.66.1)\n",
      "Requirement already satisfied: xarray>=2023.4.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (2024.2.0)\n",
      "Requirement already satisfied: zarr>=2.13.3 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from copernicusmarine) (2.16.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from aiohttp>=3.8.5->copernicusmarine) (23.1.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from aiohttp>=3.8.5->copernicusmarine) (6.0.4)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from aiohttp>=3.8.5->copernicusmarine) (1.9.3)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from aiohttp>=3.8.5->copernicusmarine) (1.4.1)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from aiohttp>=3.8.5->copernicusmarine) (1.3.1)\n",
      "Requirement already satisfied: botocore<1.34.0,>=1.33.13 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from boto3>=1.25->copernicusmarine) (1.33.13)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from boto3>=1.25->copernicusmarine) (1.0.1)\n",
      "Requirement already satisfied: s3transfer<0.9.0,>=0.8.2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from boto3>=1.25->copernicusmarine) (0.8.2)\n",
      "Collecting watchdog>=2.3.1 (from cachier>=2.2.1->copernicusmarine)\n",
      "  Using cached watchdog-4.0.0-py3-none-manylinux2014_x86_64.whl.metadata (37 kB)\n",
      "Requirement already satisfied: portalocker>=2.3.2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from cachier>=2.2.1->copernicusmarine) (2.8.2)\n",
      "Requirement already satisfied: cloudpickle>=1.5.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from dask>=2022->copernicusmarine) (3.0.0)\n",
      "Requirement already satisfied: fsspec>=2021.09.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from dask>=2022->copernicusmarine) (2024.2.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from dask>=2022->copernicusmarine) (23.2)\n",
      "Requirement already satisfied: partd>=1.2.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from dask>=2022->copernicusmarine) (1.4.1)\n",
      "Requirement already satisfied: pyyaml>=5.3.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from dask>=2022->copernicusmarine) (6.0.1)\n",
      "Requirement already satisfied: toolz>=0.10.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from dask>=2022->copernicusmarine) (0.12.0)\n",
      "Requirement already satisfied: importlib-metadata>=4.13.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from dask>=2022->copernicusmarine) (7.0.0)\n",
      "Requirement already satisfied: python-dateutil in /srv/conda/envs/notebook/lib/python3.11/site-packages (from motuclient>=1.8.4->copernicusmarine) (2.8.2)\n",
      "Requirement already satisfied: cftime in /srv/conda/envs/notebook/lib/python3.11/site-packages (from netCDF4>=1.5.4->copernicusmarine) (1.6.3)\n",
      "Requirement already satisfied: certifi in /srv/conda/envs/notebook/lib/python3.11/site-packages (from netCDF4>=1.5.4->copernicusmarine) (2023.11.17)\n",
      "Requirement already satisfied: numpy in /srv/conda/envs/notebook/lib/python3.11/site-packages (from netCDF4>=1.5.4->copernicusmarine) (1.26.2)\n",
      "Requirement already satisfied: Webob in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pydap>=3.2.2->copernicusmarine) (1.8.7)\n",
      "Requirement already satisfied: Jinja2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pydap>=3.2.2->copernicusmarine) (3.1.2)\n",
      "Requirement already satisfied: docopt in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pydap>=3.2.2->copernicusmarine) (0.6.2)\n",
      "Requirement already satisfied: six>=1.4.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pydap>=3.2.2->copernicusmarine) (1.16.0)\n",
      "Requirement already satisfied: beautifulsoup4 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pydap>=3.2.2->copernicusmarine) (4.12.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from requests>=2.27.1->copernicusmarine) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from requests>=2.27.1->copernicusmarine) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from requests>=2.27.1->copernicusmarine) (1.26.18)\n",
      "Requirement already satisfied: pandas>=1.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from xarray>=2023.4.0->copernicusmarine) (2.1.4)\n",
      "Requirement already satisfied: asciitree in /srv/conda/envs/notebook/lib/python3.11/site-packages (from zarr>=2.13.3->copernicusmarine) (0.3.3)\n",
      "Requirement already satisfied: fasteners in /srv/conda/envs/notebook/lib/python3.11/site-packages (from zarr>=2.13.3->copernicusmarine) (0.17.3)\n",
      "Requirement already satisfied: numcodecs>=0.10.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from zarr>=2.13.3->copernicusmarine) (0.11.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from importlib-metadata>=4.13.0->dask>=2022->copernicusmarine) (3.17.0)\n",
      "Requirement already satisfied: entrypoints in /srv/conda/envs/notebook/lib/python3.11/site-packages (from numcodecs>=0.10.0->zarr>=2.13.3->copernicusmarine) (0.4)\n",
      "Requirement already satisfied: pytz>=2020.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pandas>=1.5->xarray>=2023.4.0->copernicusmarine) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from pandas>=1.5->xarray>=2023.4.0->copernicusmarine) (2023.3)\n",
      "Requirement already satisfied: locket in /srv/conda/envs/notebook/lib/python3.11/site-packages (from partd>=1.2.0->dask>=2022->copernicusmarine) (1.0.0)\n",
      "Requirement already satisfied: soupsieve>1.2 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from beautifulsoup4->pydap>=3.2.2->copernicusmarine) (2.5)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from Jinja2->pydap>=3.2.2->copernicusmarine) (2.1.3)\n",
      "Downloading copernicusmarine-1.0.4-py3-none-any.whl (83 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.4/83.4 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hUsing cached cachier-3.0.0-py3-none-any.whl (22 kB)\n",
      "Using cached semver-3.0.2-py3-none-any.whl (17 kB)\n",
      "Using cached watchdog-4.0.0-py3-none-manylinux2014_x86_64.whl (82 kB)\n",
      "Installing collected packages: watchdog, semver, motuclient, cachier, copernicusmarine\n",
      "Successfully installed cachier-3.0.0 copernicusmarine-1.0.4 motuclient-3.0.0 semver-3.0.2 watchdog-4.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install copernicusmarine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "691fde29-6d7d-4099-a57c-71967494bda2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0.4\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "import xarray as xr\n",
    "import copernicusmarine as copernicusmarine\n",
    "print(copernicusmarine.__version__)\n",
    "#only for the first time: \n",
    "#copernicusmarine.login() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50689c51-2a7d-4cb0-bd3a-1900c3c2bace",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Antarctic Sea Ice Extent from Reanalysis ANTARCTIC_OMI_SI_extent\n",
      "0 0 dataset_id antarctic_omi_si_extent antarctic_omi_si_extent\n",
      "1 Antarctic Monthly Sea Ice Extent from Observations Reprocessing ANTARCTIC_OMI_SI_extent_obs\n",
      "1 0 dataset_id antarctic_omi_si_extent_obs antarctic_omi_si_extent_obs\n",
      "2 Arctic Ocean Biogeochemistry Analysis and Forecast ARCTIC_ANALYSISFORECAST_BGC_002_004\n",
      "2 0 dataset_id cmems_mod_arc_bgc_anfc_ecosmo_P1D-m cmems_mod_arc_bgc_anfc_ecosmo_P1D-m\n",
      "2 1 dataset_id cmems_mod_arc_bgc_anfc_ecosmo_P1M-m Arctic Ocean Biogeochemistry Analysis and Forecast, 6.25 km monthly mean\n",
      "3 Arctic Ocean Physics Analysis and Forecast ARCTIC_ANALYSISFORECAST_PHY_002_001\n",
      "3 0 dataset_id cmems_mod_arc_phy_anfc_6km_detided_P1D-m Arctic Ocean Physics Analysis and Forecast, 6.25 km daily mean\n",
      "3 1 dataset_id cmems_mod_arc_phy_anfc_6km_detided_P1M-m Arctic Ocean Physics Analysis, 6.25km monthly mean\n",
      "3 2 dataset_id cmems_mod_arc_phy_anfc_6km_detided_PT1H-i cmems_mod_arc_phy_anfc_6km_detided_PT1H-i\n",
      "3 3 dataset_id cmems_mod_arc_phy_anfc_6km_detided_PT6H-m cmems_mod_arc_phy_anfc_6km_detided_PT6H-m\n",
      "4 Arctic Ocean Sea Ice Analysis and Forecast ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011\n",
      "4 0 dataset_id cmems_mod_arc_phy_anfc_nextsim_P1M-m cmems_mod_arc_phy_anfc_nextsim_P1M-m\n",
      "4 1 dataset_id cmems_mod_arc_phy_anfc_nextsim_hm cmems_mod_arc_phy_anfc_nextsim_hm\n",
      "5 Arctic Ocean Tidal Analysis and Forecast ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015\n",
      "5 0 dataset_id dataset-topaz6-arc-15min-3km-be dataset-topaz6-arc-15min-3km-be\n",
      "6 Arctic Ocean Physics Analysis and Forecast ARCTIC_ANALYSIS_FORECAST_PHYS_002_001_a\n",
      "6 0 dataset_id cmems_mod_arc_phy_anfc_topaz4_P1M-m Arctic Ocean Physics Analysis, 12.5km monthly mean\n",
      "6 1 dataset_id dataset-topaz4-arc-1hr-myoceanv2-be Arctic Ocean Physics Analysis and Forecast, 12.5km hourly instantaneous (dataset-topaz4-arc-1hr-myoceanv2-be)\n",
      "6 2 dataset_id dataset-topaz4-arc-myoceanv2-be Arctic Ocean Physics Analysis and Forecast, 12.5km daily mean (dataset-topaz4-arc-myoceanv2-be)\n",
      "7 Arctic Ocean Wave Analysis and Forecast ARCTIC_ANALYSIS_FORECAST_WAV_002_014\n",
      "7 0 dataset_id dataset-wam-arctic-1hr3km-be dataset-wam-arctic-1hr3km-be\n",
      "8 Arctic Ocean Biogeochemistry Reanalysis ARCTIC_MULTIYEAR_BGC_002_005\n",
      "8 0 dataset_id cmems_mod_arc_bgc_my_ecosmo_P1D-m Arctic Ocean Biogeochemistry Reanalysis, 25km surface daily mean\n",
      "8 1 dataset_id cmems_mod_arc_bgc_my_ecosmo_P1M Arctic Ocean Biogeochemistry Reanalysis, 25km monthly mean\n",
      "8 2 dataset_id cmems_mod_arc_bgc_my_ecosmo_P1Y Arctic Ocean Biogeochemistry Reanalysis, 25km yearly mean\n",
      "9 Arctic Ocean Physics Reanalysis ARCTIC_MULTIYEAR_PHY_002_003\n",
      "9 0 dataset_id cmems_mod_arc_phy_my_topaz4_P1D-m Arctic Ocean Physics Reanalysis\n",
      "9 1 dataset_id cmems_mod_arc_phy_my_topaz4_P1M Arctic Ocean Physics Reanalysis\n",
      "9 2 dataset_id cmems_mod_arc_phy_my_topaz4_P1Y Arctic Ocean Physics Reanalysis\n",
      "10 Arctic Ocean Wave Hindcast ARCTIC_MULTIYEAR_WAV_002_013\n",
      "10 0 dataset_id cmems_mod_arc_wav_my_3km-climatology_P1M-m cmems_mod_arc_wav_my_3km-climatology_P1M-m\n",
      "10 1 dataset_id cmems_mod_arc_wav_my_3km_PT1H-i cmems_mod_arc_wav_my_3km_PT1H-i\n",
      "11 Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis ARCTIC_OMI_SI_Transport_NordicSeas\n",
      "11 0 dataset_id arctic_omi_si_transport_nordicseas arctic_omi_si_transport_nordicseas\n",
      "12 Arctic Sea Ice Extent from Reanalysis ARCTIC_OMI_SI_extent\n",
      "12 0 dataset_id arctic_omi_si_extent arctic_omi_si_extent\n",
      "13 Arctic Monthly Mean Sea Ice Extent from Observations Reprocessing ARCTIC_OMI_SI_extent_obs\n",
      "13 0 dataset_id arctic_omi_si_extent_obs arctic_omi_si_extent_obs\n",
      "14 Arctic Freshwater Content from Reanalysis ARCTIC_OMI_TEMPSAL_FWC\n",
      "14 0 dataset_id arctic_omi_tempsal_fwc arctic_omi_tempsal_fwc\n",
      "15 Baltic Sea Biogeochemistry Analysis and Forecast BALTICSEA_ANALYSISFORECAST_BGC_003_007\n",
      "15 0 dataset_id cmems_mod_bal_bgc-pp_anfc_P1D-i cmems_mod_bal_bgc-pp_anfc_P1D-i\n",
      "15 1 dataset_id cmems_mod_bal_bgc_anfc_P1D-m cmems_mod_bal_bgc_anfc_P1D-m\n",
      "15 2 dataset_id cmems_mod_bal_bgc_anfc_P1M-m cmems_mod_bal_bgc_anfc_P1M-m\n",
      "15 3 dataset_id cmems_mod_bal_bgc_anfc_static cmems_mod_bal_bgc_anfc_static\n",
      "16 Baltic Sea Physics Analysis and Forecast BALTICSEA_ANALYSISFORECAST_PHY_003_006\n",
      "16 0 dataset_id cmems_mod_bal_phy-cur_anfc_detided_P1D-m CMEMS NEMO surface currents detided daily means\n",
      "16 1 dataset_id cmems_mod_bal_phy-ssh_anfc_detided_P1D-m CMEMS NEMO SSH detided daily means\n",
      "16 2 dataset_id cmems_mod_bal_phy_anfc_P1D-m cmems_mod_bal_phy_anfc_P1D-m\n",
      "16 3 dataset_id cmems_mod_bal_phy_anfc_P1M-m cmems_mod_bal_phy_anfc_P1M-m\n",
      "16 4 dataset_id cmems_mod_bal_phy_anfc_PT15M-i CMEMS NEMO 15 minutes surface model fields\n",
      "16 5 dataset_id cmems_mod_bal_phy_anfc_PT15m-i CMEMS NEMO 15 minutes surface model fields\n",
      "16 6 dataset_id cmems_mod_bal_phy_anfc_PT1H-i CMEMS NEMO hourly model fields\n",
      "16 7 dataset_id cmems_mod_bal_phy_anfc_PT1h-i CMEMS NEMO hourly model fields\n",
      "16 8 dataset_id cmems_mod_bal_phy_anfc_static cmems_mod_bal_phy_anfc_static\n",
      "17 Baltic Sea Wave Analysis and Forecast BALTICSEA_ANALYSISFORECAST_WAV_003_010\n",
      "17 0 dataset_id cmems_mod_bal_wav_anfc_PT1H-i CMEMS WAM model fields (hourly)\n",
      "17 1 dataset_id cmems_mod_bal_wav_anfc_PT1h-i CMEMS WAM model fields (hourly)\n",
      "17 2 dataset_id cmems_mod_bal_wav_anfc_static cmems_mod_bal_wav_anfc_static\n",
      "18 Baltic Sea Biogeochemistry Reanalysis BALTICSEA_MULTIYEAR_BGC_003_012\n",
      "18 0 dataset_id cmems_mod_bal_bgc_my_P1D-m CMEMS ERGOM daily integrated model fields\n",
      "18 1 dataset_id cmems_mod_bal_bgc_my_P1M-m CMEMS ERGOM monthly integrated model fields\n",
      "18 2 dataset_id cmems_mod_bal_bgc_my_P1Y-m CMEMS ERGOM annual integrated model fields\n",
      "18 3 dataset_id cmems_mod_bal_bgc_my_static cmems_mod_bal_bgc_my_static\n",
      "19 Baltic Sea Physics Reanalysis BALTICSEA_MULTIYEAR_PHY_003_011\n",
      "19 0 dataset_id cmems_mod_bal_phy_my_P1D-m CMEMS NEMO daily integrated model fields\n",
      "19 1 dataset_id cmems_mod_bal_phy_my_P1M-m CMEMS NEMO monthly integrated model fields\n",
      "19 2 dataset_id cmems_mod_bal_phy_my_P1Y-m CMEMS NEMO annual integrated model fields\n",
      "19 3 dataset_id cmems_mod_bal_phy_my_static cmems_mod_bal_phy_my_static\n",
      "20 Baltic Sea Wave Hindcast BALTICSEA_REANALYSIS_WAV_003_015\n",
      "20 0 dataset_id cmems_mod_bal_wav_my_2km-climatology_P1M-m cmems_mod_bal_wav_my_2km-climatology_P1M-m\n",
      "20 1 dataset_id cmems_mod_bal_wav_my_static Bathymetry\n",
      "20 2 dataset_id dataset-bal-reanalysis-wav-hourly CMEMS WAM hindcast (hourly)\n",
      "21 Baltic Sea Cod Reproductive Volume from Reanalysis BALTIC_OMI_HEALTH_codt_volume\n",
      "21 0 dataset_id baltic_omi_health_codt_volume baltic_omi_health_codt_volume\n",
      "22 Baltic Sea Ocean Heat Content Anomaly (0-300m) from Reanalysis BALTIC_OMI_OHC_area_averaged_anomalies\n",
      "22 0 dataset_id baltic_omi_ohc_area_averaged_anomalies baltic_omi_ohc_area_averaged_anomalies\n",
      "23 Baltic Sea Ice Extent from Observations Reprocessing BALTIC_OMI_SI_extent\n",
      "23 0 dataset_id baltic_omi_si_extent baltic_omi_si_extent\n",
      "23 1 dataset_id baltic_omi_si_extent_yday baltic_omi_si_extent_yday\n",
      "24 Baltic Sea Ice Volume from Observations Reprocessing BALTIC_OMI_SI_volume\n",
      "24 0 dataset_id baltic_omi_si_volume baltic_omi_si_volume\n",
      "24 1 dataset_id baltic_omi_si_volume_yday baltic_omi_si_volume_yday\n",
      "25 Baltic Sea Subsurface Salinity trend from Reanalysis BALTIC_OMI_TEMPSAL_Stz_trend\n",
      "25 0 dataset_id baltic_omi_tempsal_Stz_area_averaged_trend baltic_omi_tempsal_Stz_area_averaged_trend\n",
      "26 Baltic Sea Subsurface Temperature trend from Reanalysis BALTIC_OMI_TEMPSAL_Ttz_trend\n",
      "26 0 dataset_id baltic_omi_tempsal_Ttz_area_averaged_trend baltic_omi_tempsal_Ttz_area_averaged_trend\n",
      "27 Baltic Sea Major Baltic Inflow: bottom salinity from Reanalysis BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm\n",
      "27 0 dataset_id baltic_omi_wmhe_mbi_bottom_salinity_arkona_bornholm baltic_omi_wmhe_mbi_bottom_salinity_arkona_bornholm\n",
      "28 Baltic Sea Major Baltic Inflow: time/depth evolution S,T,O2 from Observations Reprocessing BALTIC_OMI_WMHE_mbi_sto2tz_gotland\n",
      "28 0 dataset_id baltic_omi_wmhe_mbi_sto2tz_gotland baltic_omi_wmhe_mbi_sto2tz_gotland\n",
      "29 Black Sea Biogeochemistry Analysis and Forecast BLKSEA_ANALYSISFORECAST_BGC_007_010\n",
      "29 0 dataset_id cmems_mod_blk_bgc-bio_anfc_3km_P1D-m cmems_mod_blk_bgc-bio_anfc_3km_P1D-m\n",
      "29 1 dataset_id cmems_mod_blk_bgc-bio_anfc_3km_P1M-m cmems_mod_blk_bgc-bio_anfc_3km_P1M-m\n",
      "29 2 dataset_id cmems_mod_blk_bgc-car_anfc_3km_P1D-m cmems_mod_blk_bgc-car_anfc_3km_P1D-m\n",
      "29 3 dataset_id cmems_mod_blk_bgc-car_anfc_3km_P1M-m cmems_mod_blk_bgc-car_anfc_3km_P1M-m\n",
      "29 4 dataset_id cmems_mod_blk_bgc-co2_anfc_3km_P1D-m cmems_mod_blk_bgc-co2_anfc_3km_P1D-m\n",
      "29 5 dataset_id cmems_mod_blk_bgc-co2_anfc_3km_P1M-m cmems_mod_blk_bgc-co2_anfc_3km_P1M-m\n",
      "29 6 dataset_id cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m\n",
      "29 7 dataset_id cmems_mod_blk_bgc-nut_anfc_3km_P1D-m cmems_mod_blk_bgc-nut_anfc_3km_P1D-m\n",
      "29 8 dataset_id cmems_mod_blk_bgc-nut_anfc_3km_P1M-m cmems_mod_blk_bgc-nut_anfc_3km_P1M-m\n",
      "29 9 dataset_id cmems_mod_blk_bgc-opt_anfc_3km_P1D-m cmems_mod_blk_bgc-opt_anfc_3km_P1D-m\n",
      "29 10 dataset_id cmems_mod_blk_bgc-opt_anfc_3km_P1M-m cmems_mod_blk_bgc-opt_anfc_3km_P1M-m\n",
      "29 11 dataset_id cmems_mod_blk_bgc-pft_anfc_3km_P1D-m cmems_mod_blk_bgc-pft_anfc_3km_P1D-m\n",
      "29 12 dataset_id cmems_mod_blk_bgc-pft_anfc_3km_P1M-m cmems_mod_blk_bgc-pft_anfc_3km_P1M-m\n",
      "29 13 dataset_id cmems_mod_blk_bgc_anfc_3km_static cmems_mod_blk_bgc_anfc_3km_static\n",
      "30 Black Sea Physics Analysis and Forecast BLKSEA_ANALYSISFORECAST_PHY_007_001\n",
      "30 0 dataset_id cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m\n",
      "30 1 dataset_id cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m\n",
      "30 2 dataset_id cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i Horizontal Surface Velocity (2D) - 15 Minutes Instantaneous\n",
      "30 3 dataset_id cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m\n",
      "30 4 dataset_id cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m Horizontal Velocity (2D) - Daily Mean\n",
      "30 5 dataset_id cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m Horizontal Velocity (3D) - Daily Mean\n",
      "30 6 dataset_id cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i Horizontal Velocity (3D) - Hourly Mean\n",
      "30 7 dataset_id cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m\n",
      "30 8 dataset_id cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m\n",
      "30 9 dataset_id cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m\n",
      "30 10 dataset_id cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m\n",
      "30 11 dataset_id cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m\n",
      "30 12 dataset_id cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m\n",
      "30 13 dataset_id cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m Salinity (3D) - Daily Mean\n",
      "30 14 dataset_id cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i Salinity (3D) - Hourly Mean\n",
      "30 15 dataset_id cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m\n",
      "30 16 dataset_id cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m\n",
      "30 17 dataset_id cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i Sea Surface Height (2D) - 15 Minutes Instantaneous\n",
      "30 18 dataset_id cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m\n",
      "30 19 dataset_id cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m Sea Surface Height (2D) - Daily Mean\n",
      "30 20 dataset_id cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m Sea Surface Height (2D) - Daily Mean\n",
      "30 21 dataset_id cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i Sea Surface Height (2D) - Hourly Mean\n",
      "30 22 dataset_id cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m\n",
      "30 23 dataset_id cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m\n",
      "30 24 dataset_id cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m\n",
      "30 25 dataset_id cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m Potential Temperature (3D) - Daily Mean\n",
      "30 26 dataset_id cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i Potential Temperature (3D) - Hourly Mean\n",
      "30 27 dataset_id cmems_mod_blk_phy_anfc_2.5km_static cmems_mod_blk_phy_anfc_2.5km_static\n",
      "30 28 dataset_id cmems_mod_blk_phy_anfc_mrm-500m_static Bathymetry\n",
      "31 Black Sea Waves Analysis and Forecast BLKSEA_ANALYSISFORECAST_WAV_007_003\n",
      "31 0 dataset_id cmems_mod_blk_wav_anfc_2.5km_PT1H-i cmems_mod_blk_wav_anfc_2.5km_PT1H-i\n",
      "31 1 dataset_id cmems_mod_blk_wav_anfc_2.5km_static cmems_mod_blk_wav_anfc_2.5km_static\n",
      "32 Black Sea Biogeochemistry Reanalysis BLKSEA_MULTIYEAR_BGC_007_005\n",
      "32 0 dataset_id cmems_mod_blk_bgc-bio_my_2.5km_P1D-m Net Primary Production, Dissolved Oxygen (3D), Bottom Dissolved Oxygen (2D) - Daily Mean\n",
      "32 1 dataset_id cmems_mod_blk_bgc-bio_my_2.5km_P1M-m Net Primary Production, Dissolved Oxygen (3D), Bottom Dissolved Oxygen (2D) - Monthly Mean\n",
      "32 2 dataset_id cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m Net Primary Production, Dissolved Oxygen (3D), Bottom Dissolved Oxygen (2D) - Yearly Mean\n",
      "32 3 dataset_id cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m\n",
      "32 4 dataset_id cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m Net Primary Production, Dissolved Oxygen (3D), Bottom Dissolved Oxygen (2D) - Monthly Mean\n",
      "32 5 dataset_id cmems_mod_blk_bgc-car_my_2.5km_P1D-m Dissolved Inorganic Carbon, Alkalinity, pH (3D) - Daily Mean\n",
      "32 6 dataset_id cmems_mod_blk_bgc-car_my_2.5km_P1M-m Dissolved Inorganic Carbon, Alkalinity, pH (3D) - Monthly Mean\n",
      "32 7 dataset_id cmems_mod_blk_bgc-car_my_2.5km_P1Y-m Dissolved Inorganic Carbon, Alkalinity, pH (3D) - Yearly Mean\n",
      "32 8 dataset_id cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m\n",
      "32 9 dataset_id cmems_mod_blk_bgc-car_myint_2.5km_P1M-m Dissolved Inorganic Carbon, Alkalinity, pH (3D) - Monthly Mean\n",
      "32 10 dataset_id cmems_mod_blk_bgc-co2_my_2.5km_P1D-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Daily Mean\n",
      "32 11 dataset_id cmems_mod_blk_bgc-co2_my_2.5km_P1M-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Monthly Mean\n",
      "32 12 dataset_id cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Yearly Mean\n",
      "32 13 dataset_id cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m\n",
      "32 14 dataset_id cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Monthly Mean\n",
      "32 15 dataset_id cmems_mod_blk_bgc-nut_my_2.5km_P1D-m Nitrate and Phosphate (3D) - Daily Mean\n",
      "32 16 dataset_id cmems_mod_blk_bgc-nut_my_2.5km_P1M-m Nitrate and Phosphate (3D) - Monthly Mean\n",
      "32 17 dataset_id cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m Nitrate and Phosphate (3D) - Yearly Mean\n",
      "32 18 dataset_id cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m\n",
      "32 19 dataset_id cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m Nitrate and Phosphate (3D) - Monthly Mean\n",
      "32 20 dataset_id cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Daily Mean\n",
      "32 21 dataset_id cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Monthly Mean\n",
      "32 22 dataset_id cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Yearly Mean\n",
      "32 23 dataset_id cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m\n",
      "32 24 dataset_id cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Monthly Mean\n",
      "32 25 dataset_id cmems_mod_blk_bgc_my_2.5km_static cmems_mod_blk_bgc_my_2.5km_static\n",
      "33 Black Sea Physics Reanalysis BLKSEA_MULTIYEAR_PHY_007_004\n",
      "33 0 dataset_id cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m\n",
      "33 1 dataset_id cmems_mod_blk_phy-cur_my_2.5km_P1D-m Horizontal Velocity (3D) - Daily Mean\n",
      "33 2 dataset_id cmems_mod_blk_phy-cur_my_2.5km_P1M-m Horizontal Velocity (3D) - Monthly Mean\n",
      "33 3 dataset_id cmems_mod_blk_phy-cur_my_2.5km_P1Y-m Horizontal Velocity (3D) - Yearly Mean\n",
      "33 4 dataset_id cmems_mod_blk_phy-cur_myint_2.5km_P1M-m Horizontal Velocity (3D) - Monthly Mean\n",
      "33 5 dataset_id cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m\n",
      "33 6 dataset_id cmems_mod_blk_phy-mld_my_2.5km_P1D-m Mixed Layer Depth (2D) - Daily Mean\n",
      "33 7 dataset_id cmems_mod_blk_phy-mld_my_2.5km_P1M-m Mixed Layer Depth (2D) - Monthly Mean\n",
      "33 8 dataset_id cmems_mod_blk_phy-mld_my_2.5km_P1Y-m Mixed Layer Depth (2D) - Yearly Mean\n",
      "33 9 dataset_id cmems_mod_blk_phy-mld_myint_2.5km_P1M-m Mixed Layer Depth (2D) - Monthly Mean\n",
      "33 10 dataset_id cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m\n",
      "33 11 dataset_id cmems_mod_blk_phy-sal_my_2.5km_P1D-m Salinity (3D) - Daily Mean\n",
      "33 12 dataset_id cmems_mod_blk_phy-sal_my_2.5km_P1M-m Salinity (3D) - Monthly Mean\n",
      "33 13 dataset_id cmems_mod_blk_phy-sal_my_2.5km_P1Y-m Salinity (3D) - Yearly Mean\n",
      "33 14 dataset_id cmems_mod_blk_phy-sal_myint_2.5km_P1M-m Salinity (3D) - Monthly Mean\n",
      "33 15 dataset_id cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m\n",
      "33 16 dataset_id cmems_mod_blk_phy-ssh_my_2.5km_P1D-m Sea Surface Height (2D) - Daily Mean\n",
      "33 17 dataset_id cmems_mod_blk_phy-ssh_my_2.5km_P1M-m Sea Surface Height (2D) - Monthly Mean\n",
      "33 18 dataset_id cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m Sea Surface Height (2D) - Yearly Mean\n",
      "33 19 dataset_id cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m Sea Surface Height (2D) - Monthly Mean\n",
      "33 20 dataset_id cmems_mod_blk_phy-tem_my_2.5km-climatology_P1M-m cmems_mod_blk_phy-tem_my_2.5km-climatology_P1M-m\n",
      "33 21 dataset_id cmems_mod_blk_phy-tem_my_2.5km_P1D-m Potential Temperature (3D), Bottom Temperature (2D) - Daily Mean\n",
      "33 22 dataset_id cmems_mod_blk_phy-tem_my_2.5km_P1M-m Potential Temperature (3D), Bottom Temperature (2D) - Monthly Mean\n",
      "33 23 dataset_id cmems_mod_blk_phy-tem_my_2.5km_P1Y-m Potential Temperature (3D), Bottom Temperature (2D) - Yearly Mean\n",
      "33 24 dataset_id cmems_mod_blk_phy-tem_myint_2.5km_P1M-m Potential Temperature (3D), Bottom Temperature (2D) - Monthly Mean\n",
      "33 25 dataset_id cmems_mod_blk_phy_my_2.5km_static cmems_mod_blk_phy_my_2.5km_static\n",
      "34 Black Sea Waves Reanalysis BLKSEA_MULTIYEAR_WAV_007_006\n",
      "34 0 dataset_id cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m\n",
      "34 1 dataset_id cmems_mod_blk_wav_my_2.5km_PT1H-i cmems_mod_blk_wav_my_2.5km_PT1H-i\n",
      "34 2 dataset_id cmems_mod_blk_wav_my_2.5km_static cmems_mod_blk_wav_my_2.5km_static\n",
      "34 3 dataset_id cmems_mod_blk_wav_myint_2.5km_PT1H-i cmems_mod_blk_wav_myint_2.5km_PT1H-i\n",
      "35 Black Sea Oxygen Trend from Observations Reprocessing BLKSEA_OMI_HEALTH_oxygen_trend\n",
      "35 0 dataset_id blksea_omi_health_oxygen_trend_annual blksea_omi_health_oxygen_trend_annual\n",
      "35 1 dataset_id blksea_omi_health_oxygen_trend_monthly blksea_omi_health_oxygen_trend_monthly\n",
      "36 Black Sea Significant Wave Height extreme from Reanalysis BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly\n",
      "36 0 dataset_id blksea_omi_seastate_extreme_var_swh_mean_and_anomaly blksea_omi_seastate_extreme_var_swh_mean_and_anomaly\n",
      "37 Black Sea Surface Temperature extreme from Reanalysis BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly\n",
      "37 0 dataset_id blksea_omi_tempsal_extreme_var_temp_mean_and_anomaly blksea_omi_tempsal_extreme_var_temp_mean_and_anomaly\n",
      "38 Black Sea Surface Temperature time series and trend from Observations Reprocessing BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies\n",
      "38 0 dataset_id blksea_omi_tempsal_sst_area_averaged_anomalies blksea_omi_tempsal_sst_area_averaged_anomalies\n",
      "39 Black Sea Surface Temperature cumulative trend map from Observations Reprocessing BLKSEA_OMI_TEMPSAL_sst_trend\n",
      "39 0 dataset_id blksea_omi_tempsal_sst_trend blksea_omi_tempsal_sst_trend\n",
      "40 Black Sea Biogeochemistry Reanalysis BLKSEA_REANALYSIS_BIO_007_005\n",
      "40 0 dataset_id BLKSEA_REANALYSIS_BIO_007_005-statics BLKSEA_REANALYSIS_BIO_007_005-statics\n",
      "40 1 dataset_id bs-ulg-bio-int-m Net Primary Production, Dissolved Oxygen (3D), Bottom Dissolved Oxygen (2D) - Daily Mean\n",
      "40 2 dataset_id bs-ulg-bio-rean-d Net Primary Production, Dissolved Oxygen (3D), Bottom Dissolved Oxygen (2D) - Daily Mean\n",
      "40 3 dataset_id bs-ulg-bio-rean-m Net Primary Production, Dissolved Oxygen (3D), Bottom Dissolved Oxygen (2D) - Daily Mean\n",
      "40 4 dataset_id bs-ulg-car-int-m Dissolved Inorganic Carbon, Alkalinity, pH (3D) - Daily Mean\n",
      "40 5 dataset_id bs-ulg-car-rean-d Dissolved Inorganic Carbon, Alkalinity, pH (3D) - Daily Mean\n",
      "40 6 dataset_id bs-ulg-car-rean-m Dissolved Inorganic Carbon, Alkalinity, pH (3D) - Daily Mean\n",
      "40 7 dataset_id bs-ulg-co2-int-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Daily Mean\n",
      "40 8 dataset_id bs-ulg-co2-rean-d Surface partial pressure of CO2 and Surface CO2 flux (2D) - Daily Mean\n",
      "40 9 dataset_id bs-ulg-co2-rean-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Daily Mean\n",
      "40 10 dataset_id bs-ulg-nut-int-m Nitrate and Phosphate (3D) - Daily Mean\n",
      "40 11 dataset_id bs-ulg-nut-rean-d Nitrate and Phosphate (3D) - Daily Mean\n",
      "40 12 dataset_id bs-ulg-nut-rean-m Nitrate and Phosphate (3D) - Daily Mean\n",
      "40 13 dataset_id bs-ulg-pft-int-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Daily Mean\n",
      "40 14 dataset_id bs-ulg-pft-rean-d Phytoplankton Carbon Biomass and Chlorophyll (3D) - Daily Mean\n",
      "40 15 dataset_id bs-ulg-pft-rean-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Daily Mean\n",
      "41 Global Ocean Biogeochemistry Analysis and Forecast GLOBAL_ANALYSISFORECAST_BGC_001_028\n",
      "41 0 dataset_id cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m daily mean fields from Global Ocean Biogeochemistry Analysis and Forecast\n",
      "41 1 dataset_id cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSIS_FORECAST_BIO_001_028\n",
      "41 2 dataset_id cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m daily mean fields from Global Ocean Biogeochemistry Analysis and Forecast\n",
      "41 3 dataset_id cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSIS_FORECAST_BIO_001_028\n",
      "41 4 dataset_id cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m daily mean fields from Global Ocean Biogeochemistry Analysis and Forecast\n",
      "41 5 dataset_id cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSIS_FORECAST_BIO_001_028\n",
      "41 6 dataset_id cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m daily mean fields from Global Ocean Biogeochemistry Analysis and Forecast\n",
      "41 7 dataset_id cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSIS_FORECAST_BIO_001_028\n",
      "41 8 dataset_id cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m daily mean fields from Global Ocean Biogeochemistry Analysis and Forecast\n",
      "41 9 dataset_id cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m\n",
      "41 10 dataset_id cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m daily mean fields from Global Ocean Biogeochemistry Analysis and Forecast\n",
      "41 11 dataset_id cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSIS_FORECAST_BIO_001_028\n",
      "41 12 dataset_id cmems_mod_glo_bgc_anfc_0.25deg_static cmems_mod_glo_bgc_anfc_0.25deg_static\n",
      "42 Global Ocean Physics Analysis and Forecast GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 0 dataset_id cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "42 1 dataset_id cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 2 dataset_id cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i Instantaneous fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 3 dataset_id cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "42 4 dataset_id cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 5 dataset_id cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i Instantaneous fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 6 dataset_id cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "42 7 dataset_id cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 8 dataset_id cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i Instantaneous fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 9 dataset_id cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "42 10 dataset_id cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 11 dataset_id cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m\n",
      "42 12 dataset_id cmems_mod_glo_phy_anfc_0.083deg_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "42 13 dataset_id cmems_mod_glo_phy_anfc_0.083deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "42 14 dataset_id cmems_mod_glo_phy_anfc_0.083deg_PT1H-m hourly mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "42 15 dataset_id cmems_mod_glo_phy_anfc_0.083deg_static cmems_mod_glo_phy_anfc_0.083deg_static\n",
      "42 16 dataset_id cmems_mod_glo_phy_anfc_merged-uv_PT1H-i hourly mean merged surface currents from oceanic circulation, tides and waves\n",
      "43 Global Ocean Waves Analysis and Forecast GLOBAL_ANALYSISFORECAST_WAV_001_027\n",
      "43 0 dataset_id cmems_mod_glo_wav_anfc_0.083deg_PT3H-i cmems_mod_glo_wav_anfc_0.083deg_PT3H-i\n",
      "43 1 dataset_id cmems_mod_wav_anfc_0.083deg_static Bathymetry\n",
      "44 Global Ocean Biogeochemistry Analysis and Forecast GLOBAL_ANALYSIS_FORECAST_BIO_001_028\n",
      "44 0 dataset_id global-analysis-forecast-bio-001-028-daily daily mean fields from Global Ocean Biogeochemistry Analysis and Forecast\n",
      "44 1 dataset_id global-analysis-forecast-bio-001-028-monthly Monthly mean fields for product GLOBAL_ANALYSIS_FORECAST_BIO_001_028\n",
      "44 2 dataset_id global-analysis-forecast-bio-001-028-statics global-analysis-forecast-bio-001-028-statics\n",
      "45 Global Ocean Biogeochemistry Hindcast GLOBAL_MULTIYEAR_BGC_001_029\n",
      "45 0 dataset_id cmems_mod_glo_bgc_my_0.25_P1D-m Daily mean fields for product GLOBAL_REANALYSIS_BIO_001_029\n",
      "45 1 dataset_id cmems_mod_glo_bgc_my_0.25_P1M-m Monthly mean fields for product GLOBAL_REANALYSIS_BIO_001_029\n",
      "45 2 dataset_id cmems_mod_glo_bgc_my_0.25_static cmems_mod_glo_bgc_my_0.25_static\n",
      "46 Global ocean low and mid trophic levels biomass content hindcast GLOBAL_MULTIYEAR_BGC_001_033\n",
      "46 0 dataset_id cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i Physical forcings of global ocean low and mid trophic levels biomass content hindcast\n",
      "46 1 dataset_id cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i Global ocean low and mid trophic levels biomass content hindcast\n",
      "47 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "47 0 dataset_id cmems_mod_glo_phy_my_0.083-climatology_P1M-m Monthly climatology fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "47 1 dataset_id cmems_mod_glo_phy_my_0.083_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "47 2 dataset_id cmems_mod_glo_phy_my_0.083_P1M-m Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "47 3 dataset_id cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m Monthly climatology fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "47 4 dataset_id cmems_mod_glo_phy_my_0.083deg_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "47 5 dataset_id cmems_mod_glo_phy_my_0.083deg_P1M-m Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "47 6 dataset_id cmems_mod_glo_phy_my_0.083deg_static cmems_mod_glo_phy_my_0.083deg_static\n",
      "47 7 dataset_id cmems_mod_glo_phy_myint_0.083deg_P1D-m cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311\n",
      "47 8 dataset_id cmems_mod_glo_phy_myint_0.083deg_P1M-m Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "48 Global Ocean Ensemble Physics Reanalysis GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "48 0 dataset_id cmems_mod_glo_phy-all_my_0.25deg_P1D-m cmems_mod_glo_phy-all_my_0.25deg_P1D-m\n",
      "48 1 dataset_id cmems_mod_glo_phy-all_my_0.25deg_P1M-m Monthly mean fields for product GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "48 2 dataset_id cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m Daily mean fields for product GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "48 3 dataset_id cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m Monthly mean fields for product GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "49 Global Ocean Waves Reanalysis GLOBAL_MULTIYEAR_WAV_001_032\n",
      "49 0 dataset_id cmems_mod_glo_wav_my_0.2_PT3H-i Mean fields from global wave model MFWAM of Meteo-France with ECMWF forcing\n",
      "49 1 dataset_id cmems_mod_glo_wav_my_0.2_static Bathymetry\n",
      "49 2 dataset_id cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m\n",
      "49 3 dataset_id cmems_mod_glo_wav_my_0.2deg_PT3H-i Mean fields from global wave model MFWAM of Meteo-France with ECMWF forcing\n",
      "49 4 dataset_id cmems_mod_glo_wav_my_0.2deg_static Bathymetry\n",
      "49 5 dataset_id cmems_mod_glo_wav_myint_0.2deg_PT3H-i Mean fields from global wave model MFWAM of Meteo-France with ECMWF forcing\n",
      "50 Nino 3.4 Temporal Evolution of Vertical Profile of Temperature from Reanalysis GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly\n",
      "50 0 dataset_id global_omi_climate-variability_nino34_tzt_anom global_omi_climate-variability_nino34_tzt_anom\n",
      "51 Nino 3.4 Sea Surface Temperature time series from Reanalysis GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies\n",
      "51 0 dataset_id global_omi_climate-variability_nino34_sst_anom global_omi_climate-variability_nino34_sst_anom\n",
      "52 Global Ocean Yearly CO2 Sink from Multi-Observations Reprocessing GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated\n",
      "52 0 dataset_id global_omi_health_carbon_co2_flux_integrated global_omi_health_carbon_co2_flux_integrated\n",
      "53 Global Ocean acidification - mean sea water pH time series and trend from Multi-Observations Reprocessing GLOBAL_OMI_HEALTH_carbon_ph_area_averaged\n",
      "53 0 dataset_id global_omi_health_carbon_ph_area_averaged global_omi_health_carbon_ph_area_averaged\n",
      "54 Global ocean acidification - mean sea water pH trend map from Multi-Observations Reprocessing GLOBAL_OMI_HEALTH_carbon_ph_trend\n",
      "54 0 dataset_id global_omi_health_carbon_ph_trend global_omi_health_carbon_ph_trend\n",
      "55 Atlantic Meridional Overturning Circulation AMOC profile at 26N from Reanalysis GLOBAL_OMI_NATLANTIC_amoc_26N_profile\n",
      "55 0 dataset_id global_omi_natlantic_amoc_26N_profile global_omi_natlantic_amoc_26N_profile\n",
      "56 Atlantic Meridional Overturning Circulation AMOC timeseries at 26N from Reanalysis GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries\n",
      "56 0 dataset_id global_omi_natlantic_amoc_max26N_timeseries global_omi_natlantic_amoc_max26N_timeseries\n",
      "57 Global Ocean Heat Content (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000\n",
      "57 0 dataset_id global_omi_ohc_area_averaged_anomalies_0_2000 global_omi_ohc_area_averaged_anomalies_0_2000\n",
      "58 Global Ocean Heat Content (0-300m) from Reanalysis & Multi-Observations Reprocessing GLOBAL_OMI_OHC_area_averaged_anomalies_0_300\n",
      "58 0 dataset_id global_omi_ohc_area_averaged_anomalies_0_300 global_omi_ohc_area_averaged_anomalies_0_300\n",
      "59 Global Ocean Heat Content (0-700m) from Reanalysis & Multi-Observations Reprocessing GLOBAL_OMI_OHC_area_averaged_anomalies_0_700\n",
      "59 0 dataset_id global_omi_ohc_area_averaged_anomalies_0_700 global_omi_ohc_area_averaged_anomalies_0_700\n",
      "60 Global Ocean Heat Content trend map from Reanalysis & Multi-Observations Reprocessing GLOBAL_OMI_OHC_trend\n",
      "60 0 dataset_id global_omi_ohc_trend global_omi_ohc_trend\n",
      "61 Global Ocean Thermosteric Sea Level anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000\n",
      "61 0 dataset_id global_omi_sl_thsl_area_averaged_anomalies_0_2000 global_omi_sl_thsl_area_averaged_anomalies_0_2000\n",
      "62 Global Ocean Thermosteric Sea Level anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700\n",
      "62 0 dataset_id global_omi_sl_thsl_area_averaged_anomalies_0_700 global_omi_sl_thsl_area_averaged_anomalies_0_700\n",
      "63 Global Ocean Thermosteric Sea Level trend map from Reanalysis & Multi-Observations Reprocessing GLOBAL_OMI_SL_thsl_trend\n",
      "63 0 dataset_id global_omi_sl_thsl_trend global_omi_sl_thsl_trend\n",
      "64 Global Ocean Zonal Mean Subsurface Temperature cumulative trend from Multi-Observations Reprocessing GLOBAL_OMI_TEMPSAL_Tyz_trend\n",
      "64 0 dataset_id global_omi_tempsal_tyz_trend global_omi_tempsal_tyz_trend\n",
      "65 Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies\n",
      "65 0 dataset_id global_omi_tempsal_sst_area_averaged_anomalies global_omi_tempsal_sst_area_averaged_anomalies\n",
      "66 Global Ocean Sea Surface Temperature trend map from Observations Reprocessing GLOBAL_OMI_TEMPSAL_sst_trend\n",
      "66 0 dataset_id global_omi_tempsal_sst_trend global_omi_tempsal_sst_trend\n",
      "67 Mean Heat Transport across sections from Reanalysis GLOBAL_OMI_WMHE_heattrp\n",
      "67 0 dataset_id global_omi_wmhe_heattrp global_omi_wmhe_heattrp\n",
      "68 Northward Heat Transport for Global Ocean, Atlantic and Indian+Pacific basins from Reanalysis GLOBAL_OMI_WMHE_northward_mht\n",
      "68 0 dataset_id global_omi_wmhe_northward_mht global_omi_wmhe_northward_mht\n",
      "69 Mean Volume Transport across sections from Reanalysis GLOBAL_OMI_WMHE_voltrp\n",
      "69 0 dataset_id global_omi_wmhe_voltrp global_omi_wmhe_voltrp\n",
      "70 Global Ocean Ensemble Physics Reanalysis - Low resolution GLOBAL_REANALYSIS_PHY_001_026\n",
      "70 0 dataset_id global-reanalysis-phy-001-026-grepv1-ice-monthly monthly mean from reanalyses\n",
      "70 1 dataset_id global-reanalysis-phy-001-026-grepv1-monthly monthly mean from reanalyses\n",
      "70 2 dataset_id global-reanalysis-phy-001-026-grepv1-uv-monthly monthly mean from reanalyses\n",
      "71 Global Ocean Ensemble Physics Reanalysis GLOBAL_REANALYSIS_PHY_001_031\n",
      "71 0 dataset_id global-reanalysis-phy-001-031-grepv2-daily Daily mean fields for product GLOBAL_REANALYSIS_PHY_001_031\n",
      "71 1 dataset_id global-reanalysis-phy-001-031-grepv2-mnstd-daily Daily mean fields for product GLOBAL_REANALYSIS_PHY_001_031\n",
      "71 2 dataset_id global-reanalysis-phy-001-031-grepv2-mnstd-monthly Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_031\n",
      "71 3 dataset_id global-reanalysis-phy-001-031-grepv2-monthly Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_031\n",
      "72 Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast IBI_ANALYSISFORECAST_BGC_005_004\n",
      "72 0 dataset_id cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m Biogeochemical 3D daily mean fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "72 1 dataset_id cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m Biogeochemical 3D monthly mean fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "72 2 dataset_id cmems_mod_ibi_bgc_anfc_0.027deg-3D_static cmems_mod_ibi_bgc_anfc_0.027deg-3D_static\n",
      "73 Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast IBI_ANALYSISFORECAST_PHY_005_001\n",
      "73 0 dataset_id cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i Ocean surface 15-minutes instantaneous fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "73 1 dataset_id cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m Ocean surface hourly mean fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "73 2 dataset_id cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m Ocean 3D daily mean fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "73 3 dataset_id cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m Ocean 3D monthly mean fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "73 4 dataset_id cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m Ocean 3D IBI hourly mean fields\n",
      "73 5 dataset_id cmems_mod_ibi_phy_anfc_0.027deg-3D_static cmems_mod_ibi_phy_anfc_0.027deg-3D_static\n",
      "74 Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast IBI_ANALYSISFORECAST_WAV_005_005\n",
      "74 0 dataset_id cmems_mod_ibi_wav_anfc_0.05deg_PT1H-i Wave hourly instantaneous fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "74 1 dataset_id cmems_mod_ibi_wav_anfc_0.05deg_static cmems_mod_ibi_wav_anfc_0.05deg_static\n",
      "75 Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast IBI_ANALYSIS_FORECAST_WAV_005_005\n",
      "75 0 dataset_id dataset-ibi-analysis-forecast-wav-005-005-hourly Wave hourly instantaneous fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "75 1 dataset_id dataset-ibi-analysis-forecast-wav-005-005-statics dataset-ibi-analysis-forecast-wav-005-005-statics\n",
      "76 Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast IBI_MULTIYEAR_BGC_005_003\n",
      "76 0 dataset_id cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m Monthly climatology fields for product IBI_MULTIYEAR_BGC_005_003\n",
      "76 1 dataset_id cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m CMEMS IBI REANALYSIS: DAILY BIOGEOCHEMICAL PRODUCTS \n",
      "76 2 dataset_id cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m CMEMS IBI REANALYSIS: MONTHLY BIOGEOCHEMICAL PRODUCTS \n",
      "76 3 dataset_id cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m CMEMS IBI REANALYSIS: YEARLY BIOGEOCHEMICAL PRODUCTS\n",
      "76 4 dataset_id cmems_mod_ibi_bgc_my_0.083deg-3D_static cmems_mod_ibi_bgc_my_0.083deg-3D_static\n",
      "77 Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis IBI_MULTIYEAR_PHY_005_002\n",
      "77 0 dataset_id cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m CMEMS IBI REANALYSIS: HOURLY PHYSICAL PRODUCTS \n",
      "77 1 dataset_id cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m Monthly climatology fields for product IBI_MULTIYEAR_PHY_005_002\n",
      "77 2 dataset_id cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m CMEMS IBI REANALYSIS: DAILY PHYSICAL PRODUCTS \n",
      "77 3 dataset_id cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m CMEMS IBI REANALYSIS: MONTHLY PHYSICAL PRODUCTS \n",
      "77 4 dataset_id cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m CMEMS IBI REANALYSIS: YEARLY PHYSICAL PRODUCTS\n",
      "77 5 dataset_id cmems_mod_ibi_phy_my_0.083deg-3D_static cmems_mod_ibi_phy_my_0.083deg-3D_static\n",
      "78 Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis IBI_MULTIYEAR_WAV_005_006\n",
      "78 0 dataset_id cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m\n",
      "78 1 dataset_id cmems_mod_ibi_wav_my_0.027deg_PT1H-i CMEMS IBI MULTI-YEAR REANALYSIS: HOURLY WAVE PRODUCTS\n",
      "78 2 dataset_id cmems_mod_ibi_wav_my_0.027deg_static cmems_mod_ibi_wav_my_0.027deg_static\n",
      "78 3 dataset_id cmems_mod_ibi_wav_my_0.05deg-2D_PT1H-i CMEMS IBI MULTI-YEAR REANALYSIS: HOURLY WAVE PRODUCTS\n",
      "78 4 dataset_id cmems_mod_ibi_wav_my_0.05deg-2D_static cmems_mod_ibi_wav_my_0.05deg-2D_static\n",
      "79 Iberia Biscay Ireland Coastal Upwelling Index from Reanalysis IBI_OMI_CURRENTS_cui\n",
      "79 0 dataset_id ibi_omi_currents_cui_index ibi_omi_currents_cui_index\n",
      "79 1 dataset_id ibi_omi_currents_cui_trend ibi_omi_currents_cui_trend\n",
      "80 Iberia Biscay Ireland Significant Wave Height extreme from Reanalysis IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly\n",
      "80 0 dataset_id ibi_omi_seastate_extreme_var_swh_mean_and_anomaly ibi_omi_seastate_extreme_var_swh_mean_and_anomaly\n",
      "81 Iberia Biscay Ireland Strong Wave Incidence index from Reanalysis IBI_OMI_SEASTATE_swi\n",
      "81 0 dataset_id ibi_omi_seastate_swi_anomaly ibi_omi_seastate_swi_anomaly\n",
      "81 1 dataset_id ibi_omi_seastate_swi_mean_percentile ibi_omi_seastate_swi_mean_percentile\n",
      "82 Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly\n",
      "82 0 dataset_id ibi_omi_tempsal_extreme_var_temp_mean_and_anomaly ibi_omi_tempsal_extreme_var_temp_mean_and_anomaly\n",
      "83 Mediterrranean Outflow Water Index from Reanalysis & Multi-Observations Reprocessing IBI_OMI_WMHE_mow\n",
      "83 0 dataset_id ibi_omi_wmhe_mow_anomalies_north ibi_omi_wmhe_mow_anomalies_north\n",
      "83 1 dataset_id ibi_omi_wmhe_mow_anomalies_reservoir ibi_omi_wmhe_mow_anomalies_reservoir\n",
      "83 2 dataset_id ibi_omi_wmhe_mow_anomalies_south ibi_omi_wmhe_mow_anomalies_south\n",
      "83 3 dataset_id ibi_omi_wmhe_mow_anomalies_west ibi_omi_wmhe_mow_anomalies_west\n",
      "84 Arctic Ocean- In Situ Near Real Time Observations INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031\n",
      "84 0 dataset_id cmems_obs-ins_arc_phybgcwav_mynrt_na_irr Latest collection\n",
      "84 1 dataset_id cmems_obs-ins_arc_phybgcwav_mynrt_pointserie-latest_irr cmems_obs-ins_arc_phybgcwav_mynrt_pointserie-latest_irr\n",
      "84 2 dataset_id cmems_obs-ins_arc_phybgcwav_mynrt_profile-latest_irr cmems_obs-ins_arc_phybgcwav_mynrt_profile-latest_irr\n",
      "84 3 dataset_id cmems_obs-ins_arc_phybgcwav_mynrt_trajectory-latest_irr cmems_obs-ins_arc_phybgcwav_mynrt_trajectory-latest_irr\n",
      "85 Baltic Sea- In Situ Near Real Time Observations INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032\n",
      "85 0 dataset_id cmems_obs-ins_bal_phybgcwav_mynrt_na_irr cmems_obs-ins_bal_phybgcwav_mynrt_na_irr\n",
      "85 1 dataset_id cmems_obs-ins_bal_phybgcwav_mynrt_pointserie-latest_irr cmems_obs-ins_bal_phybgcwav_mynrt_pointserie-latest_irr\n",
      "85 2 dataset_id cmems_obs-ins_bal_phybgcwav_mynrt_profile-latest_irr cmems_obs-ins_bal_phybgcwav_mynrt_profile-latest_irr\n",
      "85 3 dataset_id cmems_obs-ins_bal_phybgcwav_mynrt_trajectory-latest_irr cmems_obs-ins_bal_phybgcwav_mynrt_trajectory-latest_irr\n",
      "86 Black Sea- In-Situ Near Real Time Observations INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034\n",
      "86 0 dataset_id cmems_obs-ins_blk_phybgcwav_mynrt_na_irr cmems_obs-ins_blk_phybgcwav_mynrt_na_irr\n",
      "86 1 dataset_id cmems_obs-ins_blk_phybgcwav_mynrt_pointserie-latest_irr cmems_obs-ins_blk_phybgcwav_mynrt_pointserie-latest_irr\n",
      "86 2 dataset_id cmems_obs-ins_blk_phybgcwav_mynrt_profile-latest_irr cmems_obs-ins_blk_phybgcwav_mynrt_profile-latest_irr\n",
      "86 3 dataset_id cmems_obs-ins_blk_phybgcwav_mynrt_trajectory-latest_irr cmems_obs-ins_blk_phybgcwav_mynrt_trajectory-latest_irr\n",
      "87 Global Ocean - In Situ reprocessed carbon observations - SOCATv2022 / GLODAPv2.2022 INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050\n",
      "87 0 dataset_id cmems_obs-ins_glo_bgc-car_my_glodap-gridded_irr cmems_obs-ins_glo_bgc-car_my_glodap-gridded_irr\n",
      "87 1 dataset_id cmems_obs-ins_glo_bgc-car_my_glodap-obs_irr cmems_obs-ins_glo_bgc-car_my_glodap-obs_irr\n",
      "87 2 dataset_id cmems_obs-ins_glo_bgc-car_my_socat-gridded_irr cmems_obs-ins_glo_bgc-car_my_socat-gridded_irr\n",
      "87 3 dataset_id cmems_obs-ins_glo_bgc-car_my_socat-obs_irr cmems_obs-ins_glo_bgc-car_my_socat-obs_irr\n",
      "88 Global Ocean - Delayed Mode Biogeochemical product INSITU_GLO_BGC_DISCRETE_MY_013_046\n",
      "88 0 dataset_id cmems_obs-ins_glo_bgc-chl_my_na_irr cmems_obs-ins_glo_bgc-chl_my_na_irr\n",
      "88 1 dataset_id cmems_obs-ins_glo_bgc-nut_my_na_irr cmems_obs-ins_glo_bgc-nut_my_na_irr\n",
      "88 2 dataset_id cmems_obs-ins_glo_bgc-ox_my_na_irr cmems_obs-ins_glo_bgc-ox_my_na_irr\n",
      "89 Global Ocean- In-Situ Near-Real-Time Observations INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030\n",
      "89 0 dataset_id cmems_obs-ins_glo_phybgcwav_mynrt_na_irr Latest collection\n",
      "89 1 dataset_id cmems_obs-ins_glo_phybgcwav_mynrt_pointserie-latest_irr cmems_obs-ins_glo_phybgcwav_mynrt_pointserie-latest_irr\n",
      "89 2 dataset_id cmems_obs-ins_glo_phybgcwav_mynrt_profile-latest_irr cmems_obs-ins_glo_phybgcwav_mynrt_profile-latest_irr\n",
      "89 3 dataset_id cmems_obs-ins_glo_phybgcwav_mynrt_trajectory-latest_irr cmems_obs-ins_glo_phybgcwav_mynrt_trajectory-latest_irr\n",
      "90 Global Ocean - Delayed Mode Sea level product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053\n",
      "90 0 dataset_id cmems_obs-ins_glo_phy-ssh_my_na_PT1H History collection\n",
      "90 1 dataset_id cmems_obs-ins_glo_phy-ssh_my_na_irr History collection\n",
      "90 2 dataset_id cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H History collection\n",
      "91 Global Ocean- CORA- In-situ Observations Yearly Delivery in Delayed Mode INSITU_GLO_PHY_TS_DISCRETE_MY_013_001\n",
      "91 0 dataset_id cmems_obs-ins_glo_phy-temp-sal_my_cora_irr cmems_obs-ins_glo_phy-temp-sal_my_cora_irr\n",
      "91 1 dataset_id cmems_obs-ins_glo_phy-temp-sal_my_easycora_irr cmems_obs-ins_glo_phy-temp-sal_my_easycora_irr\n",
      "92 Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode INSITU_GLO_PHY_TS_OA_MY_013_052\n",
      "92 0 dataset_id cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M Global Ocean - Coriolis Observation Re-Analysis CORA5.2 \n",
      "93 Global Ocean- Real time in-situ observations objective analysis INSITU_GLO_PHY_TS_OA_NRT_013_002\n",
      "93 0 dataset_id cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M Global Ocean - Coriolis Analysis - Near Real Time\n",
      "94 Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and sub-surface (vessel-mounted ADCPs) water velocity INSITU_GLO_PHY_UV_DISCRETE_MY_013_044\n",
      "94 0 dataset_id cmems_obs-ins_glo_phy-cur_my_adcp_irr History collection\n",
      "94 1 dataset_id cmems_obs-ins_glo_phy-cur_my_argo_irr History collection\n",
      "94 2 dataset_id cmems_obs-ins_glo_phy-cur_my_drifter_PT6H History collection\n",
      "94 3 dataset_id cmems_obs-ins_glo_phy-cur_my_radar-radial_irr cmems_obs-ins_glo_phy-cur_my_radar-radial_irr\n",
      "94 4 dataset_id cmems_obs-ins_glo_phy-cur_my_radar-total_irr cmems_obs-ins_glo_phy-cur_my_radar-total_irr\n",
      "95 Global Ocean- in-situ Near real time observations of ocean currents INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048\n",
      "95 0 dataset_id cmems_obs-ins_glo_phy-cur_nrt_argo_irr cmems_obs-ins_glo_phy-cur_nrt_argo_irr\n",
      "95 1 dataset_id cmems_obs-ins_glo_phy-cur_nrt_drifter_irr cmems_obs-ins_glo_phy-cur_nrt_drifter_irr\n",
      "95 2 dataset_id cmems_obs-ins_glo_phy-cur_nrt_radar-radial_irr cmems_obs-ins_glo_phy-cur_nrt_radar-radial_irr\n",
      "95 3 dataset_id cmems_obs-ins_glo_phy-cur_nrt_radar-total_irr cmems_obs-ins_glo_phy-cur_nrt_radar-total_irr\n",
      "96 Global Ocean - Delayed Mode Wave product INSITU_GLO_WAV_DISCRETE_MY_013_045\n",
      "96 0 dataset_id cmems_obs-ins_glo_wav_my_na_PT1H cmems_obs-ins_glo_wav_my_na_PT1H\n",
      "96 1 dataset_id cmems_obs-ins_glo_wav_my_na_irr cmems_obs-ins_glo_wav_my_na_irr\n",
      "97 Atlantic Iberian Biscay Irish Ocean- In-Situ Near Real Time Observations INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033\n",
      "97 0 dataset_id cmems_obs-ins_ibi_phybgcwav_mynrt_na_irr Latest collection\n",
      "97 1 dataset_id cmems_obs-ins_ibi_phybgcwav_mynrt_pointserie-latest_irr cmems_obs-ins_ibi_phybgcwav_mynrt_pointserie-latest_irr\n",
      "97 2 dataset_id cmems_obs-ins_ibi_phybgcwav_mynrt_profile-latest_irr cmems_obs-ins_ibi_phybgcwav_mynrt_profile-latest_irr\n",
      "97 3 dataset_id cmems_obs-ins_ibi_phybgcwav_mynrt_trajectory-latest_irr cmems_obs-ins_ibi_phybgcwav_mynrt_trajectory-latest_irr\n",
      "98 Mediterranean Sea- In-Situ Near Real Time Observations INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035\n",
      "98 0 dataset_id cmems_obs-ins_med_phybgcwav_mynrt_na_irr Latest collection\n",
      "98 1 dataset_id cmems_obs-ins_med_phybgcwav_mynrt_pointserie-latest_irr cmems_obs-ins_med_phybgcwav_mynrt_pointserie-latest_irr\n",
      "98 2 dataset_id cmems_obs-ins_med_phybgcwav_mynrt_profile-latest_irr cmems_obs-ins_med_phybgcwav_mynrt_profile-latest_irr\n",
      "98 3 dataset_id cmems_obs-ins_med_phybgcwav_mynrt_trajectory-latest_irr cmems_obs-ins_med_phybgcwav_mynrt_trajectory-latest_irr\n",
      "99 Atlantic- European North West Shelf- Ocean In-Situ Near Real Time observations INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036\n",
      "99 0 dataset_id cmems_obs-ins_nws_phybgcwav_mynrt_na_irr Latest collection\n",
      "99 1 dataset_id cmems_obs-ins_nws_phybgcwav_mynrt_pointserie-latest_irr cmems_obs-ins_nws_phybgcwav_mynrt_pointserie-latest_irr\n",
      "99 2 dataset_id cmems_obs-ins_nws_phybgcwav_mynrt_profile-latest_irr cmems_obs-ins_nws_phybgcwav_mynrt_profile-latest_irr\n",
      "99 3 dataset_id cmems_obs-ins_nws_phybgcwav_mynrt_trajectory-latest_irr cmems_obs-ins_nws_phybgcwav_mynrt_trajectory-latest_irr\n",
      "100 Mediterranean Sea Biogeochemistry Analysis and Forecast MEDSEA_ANALYSISFORECAST_BGC_006_014\n",
      "100 0 dataset_id cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m Primary Production and Oxygen (3D) - Daily Mean\n",
      "100 1 dataset_id cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m Primary Production and Oxygen (3D) - Monthly Mean\n",
      "100 2 dataset_id cmems_mod_med_bgc-car_anfc_4.2km_P1D-m Dissolved Inorganic Carbon, pH and Alkalinity (3D) - Daily Mean\n",
      "100 3 dataset_id cmems_mod_med_bgc-car_anfc_4.2km_P1M-m Dissolved Inorganic Carbon, pH and Alkalinity (3D) - Monthly Mean\n",
      "100 4 dataset_id cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Daily Mean\n",
      "100 5 dataset_id cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Monthly Mean\n",
      "100 6 dataset_id cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m Nitrate, Phosphate, Ammonium and Silicate (3D) - Daily Mean\n",
      "100 7 dataset_id cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m Nitrate, Phosphate, Ammonium and Silicate (3D) - Monthly Mean\n",
      "100 8 dataset_id cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m Attenuation coefficient of downwelling radiative flux (2D) - Daily Mean\n",
      "100 9 dataset_id cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m Attenuation coefficient of downwelling radiative flux (2D) - Monthly Mean\n",
      "100 10 dataset_id cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m\n",
      "100 11 dataset_id cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m\n",
      "100 12 dataset_id cmems_mod_med_bgc_anfc_4.2km_static cmems_mod_med_bgc_anfc_4.2km_static\n",
      "101 Mediterranean Sea Physics Analysis and Forecast MEDSEA_ANALYSISFORECAST_PHY_006_013\n",
      "101 0 dataset_id cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m Sea Surface Horizontal Velocity (2D) - Hourly Mean\n",
      "101 1 dataset_id cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m Horizontal Velocity (3D) - Hourly Mean\n",
      "101 2 dataset_id cmems_mod_med_phy-cur_anfc_4.2km_P1D-m cmems_mod_med_phy-cur_anfc_4.2km_P1D-m\n",
      "101 3 dataset_id cmems_mod_med_phy-cur_anfc_4.2km_P1M-m cmems_mod_med_phy-cur_anfc_4.2km_P1M-m\n",
      "101 4 dataset_id cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i\n",
      "101 5 dataset_id cmems_mod_med_phy-cur_anfc_4.2km_PT1H-m Horizontal Velocity (3D) - Hourly Mean\n",
      "101 6 dataset_id cmems_mod_med_phy-cur_anfc_4.2km_PT1HTS-m Horizontal Velocity (3D) - Hourly Mean - time series\n",
      "101 7 dataset_id cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m Sea Surface Horizontal Velocity Assuming No Tide (2D) - Daily Mean\n",
      "101 8 dataset_id cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m Mixed Layer Depth (2D) - Hourly Mean\n",
      "101 9 dataset_id cmems_mod_med_phy-mld_anfc_4.2km_P1D-m cmems_mod_med_phy-mld_anfc_4.2km_P1D-m\n",
      "101 10 dataset_id cmems_mod_med_phy-mld_anfc_4.2km_P1M-m cmems_mod_med_phy-mld_anfc_4.2km_P1M-m\n",
      "101 11 dataset_id cmems_mod_med_phy-mld_anfc_4.2km_PT1HTS-m Mixed Layer Depth (2D) - Hourly Mean - time series\n",
      "101 12 dataset_id cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m Sea Surface Salinity (2D) - Hourly Mean\n",
      "101 13 dataset_id cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m Salinity (3D) - Hourly Mean\n",
      "101 14 dataset_id cmems_mod_med_phy-sal_anfc_4.2km_P1D-m cmems_mod_med_phy-sal_anfc_4.2km_P1D-m\n",
      "101 15 dataset_id cmems_mod_med_phy-sal_anfc_4.2km_P1M-m cmems_mod_med_phy-sal_anfc_4.2km_P1M-m\n",
      "101 16 dataset_id cmems_mod_med_phy-sal_anfc_4.2km_PT1H-m Salinity (3D) - Hourly Mean\n",
      "101 17 dataset_id cmems_mod_med_phy-sal_anfc_4.2km_PT1HTS-m Salinity (3D) - Hourly Mean - time series\n",
      "101 18 dataset_id cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m Sea Surface Height (2D) - Hourly Mean\n",
      "101 19 dataset_id cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m\n",
      "101 20 dataset_id cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m\n",
      "101 21 dataset_id cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i\n",
      "101 22 dataset_id cmems_mod_med_phy-ssh_anfc_4.2km_PT1HTS-m Sea Surface Height (2D) - Hourly Mean - time series\n",
      "101 23 dataset_id cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m Sea Surface Height Assuming No Tide (2D) - Daily Mean\n",
      "101 24 dataset_id cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m Sea Surface Temperature (2D) - Hourly Mean\n",
      "101 25 dataset_id cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m Sea Temperature (3D) - Hourly Mean\n",
      "101 26 dataset_id cmems_mod_med_phy-tem_anfc_4.2km_P1D-m cmems_mod_med_phy-tem_anfc_4.2km_P1D-m\n",
      "101 27 dataset_id cmems_mod_med_phy-tem_anfc_4.2km_P1M-m cmems_mod_med_phy-tem_anfc_4.2km_P1M-m\n",
      "101 28 dataset_id cmems_mod_med_phy-tem_anfc_4.2km_PT1H-m Sea Temperature (3D) - Hourly Mean\n",
      "101 29 dataset_id cmems_mod_med_phy-tem_anfc_4.2km_PT1HTS-m Sea Temperature (3D) - Hourly Mean - time series\n",
      "101 30 dataset_id cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m\n",
      "101 31 dataset_id cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m\n",
      "101 32 dataset_id cmems_mod_med_phy_anfc_4.2km_static cmems_mod_med_phy_anfc_4.2km_static\n",
      "102 Mediterranean Sea Waves Analysis and Forecast MEDSEA_ANALYSISFORECAST_WAV_006_017\n",
      "102 0 dataset_id MEDSEA_ANALYSISFORECAST_WAV_006_017-statics MEDSEA_ANALYSISFORECAST_WAV_006_017-statics\n",
      "102 1 dataset_id cmems_mod_med_wav_anfc_4.2km_PT1H-i Wave fields (2D) - Hourly Instantaneous\n",
      "102 2 dataset_id cmems_mod_med_wav_anfc_4.2km_static cmems_mod_med_wav_anfc_4.2km_static\n",
      "102 3 dataset_id med-hcmr-wav-an-fc-h Wave fields (2D) - Hourly Instantaneous\n",
      "103 Mediterranean Sea Biogechemistry Reanalysis MEDSEA_MULTIYEAR_BGC_006_008\n",
      "103 0 dataset_id cmems_mod_med_bgc-bio_my_4.2km_P1Y-m Primary Production and Oxygen (3D) - Yearly Mean\n",
      "103 1 dataset_id cmems_mod_med_bgc-bio_myint_4.2km_P1M-m Primary Production and Oxygen (3D) - Monthly Mean\n",
      "103 2 dataset_id cmems_mod_med_bgc-car_my_4.2km_P1Y-m Dissolved Inorganic Carbon, pH and Alkalinity (3D) - Yearly Mean\n",
      "103 3 dataset_id cmems_mod_med_bgc-car_myint_4.2km_P1M-m Dissolved Inorganic Carbon, pH and Alkalinity (3D) - Monthly Mean\n",
      "103 4 dataset_id cmems_mod_med_bgc-co2_my_4.2km_P1Y-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Yearly Mean\n",
      "103 5 dataset_id cmems_mod_med_bgc-co2_myint_4.2km_P1M-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Monthly Mean\n",
      "103 6 dataset_id cmems_mod_med_bgc-nut_my_4.2km_P1Y-m Nitrate, Phosphate and Ammonium (3D) - Yearly Mean\n",
      "103 7 dataset_id cmems_mod_med_bgc-nut_myint_4.2km_P1M-m Nitrate, Phosphate and Ammonium (3D) - Monthly Mean\n",
      "103 8 dataset_id cmems_mod_med_bgc-pft_myint_4.2km_P1M-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Monthly Mean\n",
      "103 9 dataset_id cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Yearly Mean\n",
      "103 10 dataset_id cmems_mod_med_bgc_my_4.2km-climatology_P1M-m cmems_mod_med_bgc_my_4.2km-climatology_P1M-m\n",
      "103 11 dataset_id cmems_mod_med_bgc_my_4.2km_static cmems_mod_med_bgc_my_4.2km_static\n",
      "103 12 dataset_id med-ogs-bio-rean-d Primary Production and Oxygen (3D) - Daily Mean\n",
      "103 13 dataset_id med-ogs-bio-rean-m Primary Production and Oxygen (3D) - Monthly Mean\n",
      "103 14 dataset_id med-ogs-car-rean-d Dissolved Inorganic Carbon, pH and Alkalinity (3D) - Daily Mean\n",
      "103 15 dataset_id med-ogs-car-rean-m Dissolved Inorganic Carbon, pH and Alkalinity (3D) - Monthly Mean\n",
      "103 16 dataset_id med-ogs-co2-rean-d Surface partial pressure of CO2 and Surface CO2 flux (2D) - Daily Mean\n",
      "103 17 dataset_id med-ogs-co2-rean-m Surface partial pressure of CO2 and Surface CO2 flux (2D) - Monthly Mean\n",
      "103 18 dataset_id med-ogs-nut-rean-d Nitrate, Phosphate and Ammonium (3D) - Daily Mean\n",
      "103 19 dataset_id med-ogs-nut-rean-m Nitrate, Phosphate and Ammonium (3D) - Monthly Mean\n",
      "103 20 dataset_id med-ogs-pft-rean-d Phytoplankton Carbon Biomass and Chlorophyll (3D) - Daily Mean\n",
      "103 21 dataset_id med-ogs-pft-rean-m Phytoplankton Carbon Biomass and Chlorophyll (3D) - Monthly Mean\n",
      "104 Mediterranean Sea Physics Reanalysis MEDSEA_MULTIYEAR_PHY_006_004\n",
      "104 0 dataset_id cmems_mod_med_phy-cur_my_4.2km_P1Y-m Horizontal Velocity (3D) - Yearly Mean\n",
      "104 1 dataset_id cmems_mod_med_phy-mld_my_4.2km_P1Y-m Mixed Layer Depth (2D) - Yearly Mean\n",
      "104 2 dataset_id cmems_mod_med_phy-sal_my_4.2km_P1Y-m Salinity (3D) - Yearly\n",
      "104 3 dataset_id cmems_mod_med_phy-ssh_my_4.2km_P1Y-m Sea Surface Height (2D) - Yearly Mean\n",
      "104 4 dataset_id cmems_mod_med_phy-tem_my_4.2km_P1Y-m Potential Temperature (3D) - Yearly Mean\n",
      "104 5 dataset_id cmems_mod_med_phy_my_4.2km-climatology_P1M-m cmems_mod_med_phy_my_4.2km-climatology_P1M-m\n",
      "104 6 dataset_id cmems_mod_med_phy_my_4.2km_static cmems_mod_med_phy_my_4.2km_static\n",
      "104 7 dataset_id med-cmcc-cur-int-m Horizontal Velocity (3D) - Monthly Mean\n",
      "104 8 dataset_id med-cmcc-cur-rean-d Horizontal Velocity (3D) - Daily Mean\n",
      "104 9 dataset_id med-cmcc-cur-rean-h Horizontal Surface Velocity (2D) - Hourly Mean\n",
      "104 10 dataset_id med-cmcc-cur-rean-m Horizontal Velocity (3D) - Monthly Mean\n",
      "104 11 dataset_id med-cmcc-mld-int-m Mixed Layer Depth (2D) - Monthly Mean\n",
      "104 12 dataset_id med-cmcc-mld-rean-d Mixed Layer Depth (2D) - Daily Mean\n",
      "104 13 dataset_id med-cmcc-mld-rean-m Mixed Layer Depth (2D) - Monthly Mean\n",
      "104 14 dataset_id med-cmcc-sal-int-m Salinity (3D) - Monthly Mean\n",
      "104 15 dataset_id med-cmcc-sal-rean-d Salinity (3D) - Daily Mean\n",
      "104 16 dataset_id med-cmcc-sal-rean-m Salinity (3D) - Monthly Mean\n",
      "104 17 dataset_id med-cmcc-ssh-int-m Sea Surface Height (2D) - Monthly Mean\n",
      "104 18 dataset_id med-cmcc-ssh-rean-d Sea Surface Height (2D) - Daily Mean\n",
      "104 19 dataset_id med-cmcc-ssh-rean-h Sea Surface Height (2D) - Hourly Mean\n",
      "104 20 dataset_id med-cmcc-ssh-rean-m Sea Surface Height (2D) - Monthly Mean\n",
      "104 21 dataset_id med-cmcc-tem-int-m Potential Temperature (3D) - Monthly Mean\n",
      "104 22 dataset_id med-cmcc-tem-rean-d Potential Temperature (3D) - Daily Mean\n",
      "104 23 dataset_id med-cmcc-tem-rean-m Potential Temperature (3D) - Monthly Mean\n",
      "105 Mediterranean Sea Waves Reanalysis MEDSEA_MULTIYEAR_WAV_006_012\n",
      "105 0 dataset_id cmems_mod_med_wav_my_4.2km-climatology_P1M-m cmems_mod_med_wav_my_4.2km-climatology_P1M-m\n",
      "105 1 dataset_id cmems_mod_med_wav_my_4.2km_static cmems_mod_med_wav_my_4.2km_static\n",
      "105 2 dataset_id cmems_mod_med_wav_myint_4.2km_PT1H-i Wave fields (2D) - Hourly Instantaneous\n",
      "105 3 dataset_id med-hcmr-wav-rean-h Wave fields (2D) - Hourly Instantaneous\n",
      "106 Mediterranean Ocean Heat Content Anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing MEDSEA_OMI_OHC_area_averaged_anomalies\n",
      "106 0 dataset_id medsea_omi_ohc_area_averaged_anomalies medsea_omi_ohc_area_averaged_anomalies\n",
      "107 Mediterranean Sea Significant Wave Height extreme from Reanalysis MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly\n",
      "107 0 dataset_id medsea_omi_seastate_extreme_var_swh_mean_and_anomaly medsea_omi_seastate_extreme_var_swh_mean_and_anomaly\n",
      "108 Mediterranean Sea Surface Temperature extreme from Reanalysis MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly\n",
      "108 0 dataset_id medsea_omi_tempsal_extreme_var_temp_mean_and_anomaly medsea_omi_tempsal_extreme_var_temp_mean_and_anomaly\n",
      "109 Mediterranean Sea Surface Temperature time series and trend from Observations Reprocessing MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies\n",
      "109 0 dataset_id medsea_omi_tempsal_sst_area_averaged_anomalies medsea_omi_tempsal_sst_area_averaged_anomalies\n",
      "110 Mediterranean Sea Surface Temperature cumulative trend map from Observations Reprocessing MEDSEA_OMI_TEMPSAL_sst_trend\n",
      "110 0 dataset_id medsea_omi_tempsal_sst_trend medsea_omi_tempsal_sst_trend\n",
      "111 Nutrient and carbon profiles vertical distribution MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009\n",
      "111 0 dataset_id cmems_obs-mob_glo_bgc-nut-car_mynrt_irr_i cmems_obs-mob_glo_bgc-nut-car_mynrt_irr_i\n",
      "112 Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon MULTIOBS_GLO_BIO_BGC_3D_REP_015_010\n",
      "112 0 dataset_id cmems_obs_glo_bgc3d_rep_clim cmems_obs_glo_bgc3d_rep_clim\n",
      "112 1 dataset_id cmems_obs_glo_bgc3d_rep_weekly Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coeffient and Particulate Organic Carbon\n",
      "113 Global Ocean Surface Carbon MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008\n",
      "113 0 dataset_id dataset-carbon-rep-monthly dataset-carbon-rep-monthly\n",
      "114 Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m MULTIOBS_GLO_PHY_MYNRT_015_003\n",
      "114 0 dataset_id cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m\n",
      "114 1 dataset_id cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m\n",
      "114 2 dataset_id cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i\n",
      "114 3 dataset_id cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m\n",
      "114 4 dataset_id cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m\n",
      "114 5 dataset_id cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i\n",
      "114 6 dataset_id cmems_obs_mob_glo_phy-cur_my_0.25deg_P1D-m Daily mean total surface and 15m velocities\n",
      "114 7 dataset_id cmems_obs_mob_glo_phy-cur_my_0.25deg_P1M-m Monthly mean total surface and 15m velocities\n",
      "114 8 dataset_id cmems_obs_mob_glo_phy-cur_my_0.25deg_PT1H-i Total surface and 15m velocities\n",
      "114 9 dataset_id cmems_obs_mob_glo_phy-cur_nrt_0.25deg_P1D-m Daily mean total surface and 15m velocities\n",
      "114 10 dataset_id cmems_obs_mob_glo_phy-cur_nrt_0.25deg_P1M-m Monthly mean total surface and 15m velocities\n",
      "114 11 dataset_id cmems_obs_mob_glo_phy-cur_nrt_0.25deg_PT1H-i Total surface and 15m velocities\n",
      "115 Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current  and Modeled Ekman Current Processing MULTIOBS_GLO_PHY_NRT_015_003\n",
      "115 0 dataset_id dataset-uv-nrt-daily Daily mean total surface and 15m velocities\n",
      "115 1 dataset_id dataset-uv-nrt-hourly Total surface and 15m velocities\n",
      "115 2 dataset_id dataset-uv-nrt-monthly Monthly mean total surface and 15m velocities\n",
      "116 Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current  and Modeled Ekman Current Reprocessing MULTIOBS_GLO_PHY_REP_015_004\n",
      "116 0 dataset_id dataset-uv-rep-daily Daily mean total surface and 15m velocities\n",
      "116 1 dataset_id dataset-uv-rep-hourly Total surface and 15m velocities\n",
      "116 2 dataset_id dataset-uv-rep-monthly Monthly mean total surface and 15m velocities\n",
      "117 SMOS CATDS Qualified (L2Q) Sea Surface Salinity product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014\n",
      "117 0 dataset_id cmems_obs-mob_glo_phy-sss_mynrt_smos_P1D cmems_obs-mob_glo_phy-sss_mynrt_smos_P1D\n",
      "118 SSS SMOS L4 OI - LOPS-v2021 MULTIOBS_GLO_PHY_SSS_L4_MY_015_015\n",
      "118 0 dataset_id cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W SSS SMOS L4 OI - LOPS-v2021\n",
      "119 Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013\n",
      "119 0 dataset_id cmems_obs-mob_glo_phy-sss_my_multi_P1D Global Analysed Sea Surface Salinity and Density\n",
      "119 1 dataset_id cmems_obs-mob_glo_phy-sss_my_multi_P1M Global analysed sea surface salinity and density monthly average\n",
      "119 2 dataset_id cmems_obs-mob_glo_phy-sss_nrt_multi_P1D Global Analysed Sea Surface Salinity and Density\n",
      "119 3 dataset_id cmems_obs-mob_glo_phy-sss_nrt_multi_P1M Global analysed sea surface salinity and density monthly average\n",
      "119 4 dataset_id dataset-sss-ssd-nrt-monthly Global analysed sea surface salinity and density monthly average\n",
      "119 5 dataset_id dataset-sss-ssd-nrt-weekly Global Analysed Sea Surface Salinity and Density\n",
      "119 6 dataset_id dataset-sss-ssd-rep-monthly Global analysed sea surface salinity and density monthly average\n",
      "119 7 dataset_id dataset-sss-ssd-rep-weekly Global Analysed Sea Surface Salinity and Density\n",
      "120 Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012\n",
      "120 0 dataset_id dataset-armor-3d-nrt-monthly ARMOR3D NRT - TSHUVMld Global Ocean Observation-based Product monthly average\n",
      "120 1 dataset_id dataset-armor-3d-nrt-weekly ARMOR3D NRT - TSHUVMld Global Ocean Observation-based Product\n",
      "120 2 dataset_id dataset-armor-3d-rep-monthly ARMOR3D REP - TSHUVMld Global Ocean Observation-based Product monthly average\n",
      "120 3 dataset_id dataset-armor-3d-rep-weekly ARMOR3D REP - TSHUVMld Global Ocean Observation-based Product\n",
      "121 Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D) MULTIOBS_GLO_PHY_W_3D_REP_015_007\n",
      "121 0 dataset_id dataset-omega-3d-rep-weekly Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)\n",
      "122 North West Shelf Sea Surface Temperature extreme from Reanalysis NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly\n",
      "122 0 dataset_id northwestshelf_omi_tempsal_extreme_var_temp_mean_and_anomaly northwestshelf_omi_tempsal_extreme_var_temp_mean_and_anomaly\n",
      "123 Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast NWSHELF_ANALYSISFORECAST_BGC_004_002\n",
      "123 0 dataset_id cmems_mod_nws_bgc-chl_anfc_7km-3D_P1D-m daily-mean chlorophyll concentration (3D)\n",
      "123 1 dataset_id cmems_mod_nws_bgc-kd_anfc_7km-3D_P1D-m daily-mean attenuation coefficient (3D)\n",
      "123 2 dataset_id cmems_mod_nws_bgc-no3_anfc_7km-3D_P1D-m daily-mean nitrate (3D)\n",
      "123 3 dataset_id cmems_mod_nws_bgc-o2_anfc_7km-3D_P1D-m daily-mean dissolved oxygen (3D)\n",
      "123 4 dataset_id cmems_mod_nws_bgc-ph_anfc_7km-3D_P1D-m daily-mean pH (3D)\n",
      "123 5 dataset_id cmems_mod_nws_bgc-phyc_anfc_7km-3D_P1D-m daily-mean phytoplankton (3D)\n",
      "123 6 dataset_id cmems_mod_nws_bgc-po4_anfc_7km-3D_P1D-m daily-mean phosphate (3D)\n",
      "123 7 dataset_id cmems_mod_nws_bgc-pp_anfc_7km-3D_P1D-m daily-mean primary productivity (3D)\n",
      "123 8 dataset_id cmems_mod_nws_bgc-spco2_anfc_7km-2D_P1D-m daily-mean spCO2 (2D)\n",
      "123 9 dataset_id cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m Biogeochemical 3D daily mean fields for the North West Shelf (NWS) region\n",
      "123 10 dataset_id cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m Biogeochemical 3D monthly mean fields for the North West Shelf (NWS) region\n",
      "123 11 dataset_id cmems_mod_nws_bgc_anfc_0.027deg-3D_static cmems_mod_nws_bgc_anfc_0.027deg-3D_static\n",
      "124 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NWSHELF_ANALYSISFORECAST_PHY_004_013\n",
      "124 0 dataset_id cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i Ocean surface 15-minutes instantaneous fields for the North West Shelf (NWS) region\n",
      "124 1 dataset_id cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m Ocean surface hourly mean fields for the North West Shelf (NWS) region\n",
      "124 2 dataset_id cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m Ocean 3D daily mean fields for the North West Shelf (NWS) region\n",
      "124 3 dataset_id cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m Ocean 3D monthly mean fields for the North West Shelf (NWS) region\n",
      "124 4 dataset_id cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m Ocean 3D hourly mean fields for the North West Shelf (NWS) region\n",
      "124 5 dataset_id cmems_mod_nws_phy_anfc_0.027deg-3D_static cmems_mod_nws_phy_anfc_0.027deg-3D_static\n",
      "125 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NWSHELF_ANALYSISFORECAST_PHY_LR_004_001\n",
      "125 0 dataset_id cmems_mod_nws_phy-bottomt_anfc_7km-2D_P1D-m daily-mean seabed temperature (2D)\n",
      "125 1 dataset_id cmems_mod_nws_phy-mld_anfc_7km-2D_P1D-m daily-mean mixed layer depth (2D)\n",
      "125 2 dataset_id cmems_mod_nws_phy-s_anfc_7km-3D_P1D-m daily-mean salinity (3D)\n",
      "125 3 dataset_id cmems_mod_nws_phy-ssh_anfc_7km-2D_P1D-m daily-mean SSH (2D)\n",
      "125 4 dataset_id cmems_mod_nws_phy-t_anfc_7km-3D_P1D-m daily-mean potential temperature (3D)\n",
      "125 5 dataset_id cmems_mod_nws_phy-uv_anfc_7km-3D_P1D-m daily-mean horizontal velocity (3D)\n",
      "125 6 dataset_id cmems_mod_nws_phy_anfc_7km-3D_static cmems_mod_nws_phy_anfc_7km-3D_static\n",
      "126 Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast NWSHELF_ANALYSISFORECAST_WAV_004_014\n",
      "126 0 dataset_id cmems_mod_nws_wav_anfc_0.05deg_PT1H-i Wave hourly instantaneous fields for the North West Shelf (NWS) region\n",
      "126 1 dataset_id cmems_mod_nws_wav_anfc_0.05deg_static cmems_mod_nws_wav_anfc_0.05deg_static\n",
      "127 Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis NWSHELF_MULTIYEAR_BGC_004_011\n",
      "127 0 dataset_id cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m daily-mean chlorophyll concentration (3D)\n",
      "127 1 dataset_id cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m monthly-mean chlorophyll concentration (3D)\n",
      "127 2 dataset_id cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m monthly-mean chlorophyll concentration (3D)\n",
      "127 3 dataset_id cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m daily-mean attenuation coefficient (3D)\n",
      "127 4 dataset_id cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m monthly-mean attenuation coefficient (3D)\n",
      "127 5 dataset_id cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m monthly-mean attenuation coefficient (3D)\n",
      "127 6 dataset_id cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m daily-mean nitrate (3D)\n",
      "127 7 dataset_id cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m monthly-mean nitrate (3D)\n",
      "127 8 dataset_id cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m monthly-mean nitrate (3D)\n",
      "127 9 dataset_id cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m daily-mean dissolved oxygen (3D)\n",
      "127 10 dataset_id cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m monthly-mean dissolved oxygen (3D)\n",
      "127 11 dataset_id cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m monthly-mean dissolved oxygen (3D)\n",
      "127 12 dataset_id cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m daily-mean chlorophyll concentration in diatoms (3D)\n",
      "127 13 dataset_id cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m monthly-mean chlorophyll concentration in diatoms (3D)\n",
      "127 14 dataset_id cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m daily-mean chlorophyll concentration in dinoflagellates (3D)\n",
      "127 15 dataset_id cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m monthly-mean chlorophyll concentration in dinoflagellates (3D)\n",
      "127 16 dataset_id cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m daily-mean chlorophyll concentration in nanophytoplankton (3D)\n",
      "127 17 dataset_id cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m monthly-mean chlorophyll concentration in nanophytoplankton (3D)\n",
      "127 18 dataset_id cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m daily-mean chlorophyll concentration in picophytoplankton (3D)\n",
      "127 19 dataset_id cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m monthly-mean chlorophyll concentration in picophytoplankton (3D)\n",
      "127 20 dataset_id cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m monthly-mean chlorophyll concentration in diatoms (3D)\n",
      "127 21 dataset_id cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m monthly-mean chlorophyll concentration in dinoflagellates (3D)\n",
      "127 22 dataset_id cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m monthly-mean chlorophyll concentration in nanophytoplankton (3D)\n",
      "127 23 dataset_id cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m monthly-mean chlorophyll concentration in picophytoplankton (3D)\n",
      "127 24 dataset_id cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m daily-mean pH (3D)\n",
      "127 25 dataset_id cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m monthly-mean pH (3D)\n",
      "127 26 dataset_id cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m monthly-mean pH (3D)\n",
      "127 27 dataset_id cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m daily-mean phytoplankton (3D)\n",
      "127 28 dataset_id cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m monthly-mean phytoplankton (3D)\n",
      "127 29 dataset_id cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m monthly-mean phytoplankton (3D)\n",
      "127 30 dataset_id cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m daily-mean phosphate (3D)\n",
      "127 31 dataset_id cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m monthly-mean phosphate (3D)\n",
      "127 32 dataset_id cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m monthly-mean phosphate (3D)\n",
      "127 33 dataset_id cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m daily-mean net primary production (3D)\n",
      "127 34 dataset_id cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m monthly-mean net primary production (3D)\n",
      "127 35 dataset_id cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m monthly-mean net primary production (3D)\n",
      "127 36 dataset_id cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m daily-mean spCO2 (2D)\n",
      "127 37 dataset_id cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m monthly-mean spCO2 (2D)\n",
      "127 38 dataset_id cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m monthly-mean spCO2 (2D)\n",
      "128 Atlantic- European North West Shelf- Ocean Physics Reanalysis NWSHELF_MULTIYEAR_PHY_004_009\n",
      "128 0 dataset_id cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m daily-mean seabed temperature (2D)\n",
      "128 1 dataset_id cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m monthly-mean seabed temperature (2D)\n",
      "128 2 dataset_id cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i hourly-instantaneous seabed temperature (2D)\n",
      "128 3 dataset_id cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m monthly-mean seabed temperature (2D)\n",
      "128 4 dataset_id cmems_mod_nws_phy-mld_my_7km-2D_P1D-m daily-mean mixed layer depth (2D)\n",
      "128 5 dataset_id cmems_mod_nws_phy-mld_my_7km-2D_P1M-m monthly-mean mixed layer depth (2D)\n",
      "128 6 dataset_id cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i hourly-instantaneous mixed layer depth (2D)\n",
      "128 7 dataset_id cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m monthly-mean mixed layer depth (2D)\n",
      "128 8 dataset_id cmems_mod_nws_phy-s_my_7km-3D_P1D-m daily-mean salinity (3D)\n",
      "128 9 dataset_id cmems_mod_nws_phy-s_my_7km-3D_P1M-m monthly-mean salinity (3D)\n",
      "128 10 dataset_id cmems_mod_nws_phy-s_myint_7km-3D_P1M-m monthly-mean salinity (3D)\n",
      "128 11 dataset_id cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m daily-mean SSH (2D)\n",
      "128 12 dataset_id cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m monthly-mean SSH (2D)\n",
      "128 13 dataset_id cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i hourly-instantaneous SSH (2D)\n",
      "128 14 dataset_id cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m monthly-mean SSH (2D)\n",
      "128 15 dataset_id cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i hourly-instantaneous surface salinity (2D)\n",
      "128 16 dataset_id cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i hourly-instantaneous surface temperature (2D)\n",
      "128 17 dataset_id cmems_mod_nws_phy-t_my_7km-3D_P1D-m daily-mean potential temperature (3D)\n",
      "128 18 dataset_id cmems_mod_nws_phy-t_my_7km-3D_P1M-m monthly-mean potential temperature (3D)\n",
      "128 19 dataset_id cmems_mod_nws_phy-t_myint_7km-3D_P1M-m monthly-mean potential temperature (3D)\n",
      "128 20 dataset_id cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i hourly-instantaneous horizontal velocity (2D)\n",
      "128 21 dataset_id cmems_mod_nws_phy-uv_my_7km-3D_P1D-m daily-mean horizontal velocity (3D)\n",
      "128 22 dataset_id cmems_mod_nws_phy-uv_my_7km-3D_P1M-m monthly-mean horizontal velocity (3D)\n",
      "128 23 dataset_id cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m monthly-mean horizontal velocity (3D)\n",
      "129 Atlantic- European North West Shelf- Wave Physics Reanalysis NWSHELF_REANALYSIS_WAV_004_015\n",
      "129 0 dataset_id MetO-NWS-WAV-RAN 3-hourly-instantaneous wave\n",
      "130 Arctic Region, Bio-Geo-Chemical, L3, daily observation OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201\n",
      "130 0 dataset_id cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-m cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-m\n",
      "130 1 dataset_id cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-m cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-m\n",
      "130 2 dataset_id cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-m cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-m\n",
      "131 Arctic Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207\n",
      "131 0 dataset_id cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1D-m cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1D-m\n",
      "131 1 dataset_id cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1M-m cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1M-m\n",
      "131 2 dataset_id cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1D-m cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1D-m\n",
      "131 3 dataset_id cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1M-m cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1M-m\n",
      "131 4 dataset_id cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1D-m cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1D-m\n",
      "131 5 dataset_id cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1M-m cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1M-m\n",
      "132 Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations OCEANCOLOUR_ARC_BGC_L3_MY_009_123\n",
      "132 0 dataset_id cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D\n",
      "132 1 dataset_id cmems_obs-oc_arc_bgc-plankton_my_l3-olci-300m_P1D cmems_obs-oc_arc_bgc-plankton_my_l3-olci-300m_P1D\n",
      "132 2 dataset_id cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D\n",
      "132 3 dataset_id cmems_obs-oc_arc_bgc-reflectance_my_l3-olci-300m_P1D cmems_obs-oc_arc_bgc-reflectance_my_l3-olci-300m_P1D\n",
      "132 4 dataset_id cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D\n",
      "132 5 dataset_id cmems_obs-oc_arc_bgc-transp_my_l3-olci-300m_P1D cmems_obs-oc_arc_bgc-transp_my_l3-olci-300m_P1D\n",
      "133 Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations OCEANCOLOUR_ARC_BGC_L3_NRT_009_121\n",
      "133 0 dataset_id cmems_obs-oc_arc_bgc-plankton_nrt_l3-olci-300m_P1D cmems_obs-oc_arc_bgc-plankton_nrt_l3-olci-300m_P1D\n",
      "133 1 dataset_id cmems_obs-oc_arc_bgc-reflectance_nrt_l3-olci-300m_P1D cmems_obs-oc_arc_bgc-reflectance_nrt_l3-olci-300m_P1D\n",
      "133 2 dataset_id cmems_obs-oc_arc_bgc-transp_nrt_l3-olci-300m_P1D cmems_obs-oc_arc_bgc-transp_nrt_l3-olci-300m_P1D\n",
      "134 Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations OCEANCOLOUR_ARC_BGC_L4_MY_009_124\n",
      "134 0 dataset_id cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M\n",
      "134 1 dataset_id cmems_obs-oc_arc_bgc-plankton_my_l4-multi-climatology-4km_P1D cmems_obs-oc_arc_bgc-plankton_my_l4-multi-climatology-4km_P1D\n",
      "134 2 dataset_id cmems_obs-oc_arc_bgc-plankton_my_l4-olci-300m_P1M cmems_obs-oc_arc_bgc-plankton_my_l4-olci-300m_P1M\n",
      "135 Arctic Ocean Colour Plankton and Transparency L4 NRT monthly observations OCEANCOLOUR_ARC_BGC_L4_NRT_009_122\n",
      "135 0 dataset_id cmems_obs-oc_arc_bgc-plankton_nrt_l4-olci-300m_P1M cmems_obs-oc_arc_bgc-plankton_nrt_l4-olci-300m_P1M\n",
      "135 1 dataset_id cmems_obs-oc_arc_bgc-transp_nrt_l4-olci-300m_P1M cmems_obs-oc_arc_bgc-transp_nrt_l4-olci-300m_P1M\n",
      "136 North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations OCEANCOLOUR_ATL_BGC_L3_MY_009_113\n",
      "136 0 dataset_id cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D\n",
      "136 1 dataset_id cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D\n",
      "136 2 dataset_id cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D\n",
      "136 3 dataset_id cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D\n",
      "136 4 dataset_id cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D\n",
      "136 5 dataset_id cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D\n",
      "137 North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations OCEANCOLOUR_ATL_BGC_L3_NRT_009_111\n",
      "137 0 dataset_id cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D\n",
      "137 1 dataset_id cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D\n",
      "137 2 dataset_id cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D\n",
      "137 3 dataset_id cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D\n",
      "137 4 dataset_id cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D\n",
      "137 5 dataset_id cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D\n",
      "138 Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing) OCEANCOLOUR_ATL_BGC_L4_MY_009_118\n",
      "138 0 dataset_id cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D\n",
      "138 1 dataset_id cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M\n",
      "138 2 dataset_id cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M\n",
      "139 Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time) OCEANCOLOUR_ATL_BGC_L4_NRT_009_116\n",
      "139 0 dataset_id cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D\n",
      "139 1 dataset_id cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M\n",
      "139 2 dataset_id cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M\n",
      "140 Baltic Sea, Bio-Geo-Chemical, L3, daily observation OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202\n",
      "140 0 dataset_id cmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-m cmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-m\n",
      "140 1 dataset_id cmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-m cmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-m\n",
      "140 2 dataset_id cmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-m cmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-m\n",
      "140 3 dataset_id cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m CMEMS HR-OC Baltic Sea transparency (spm, tur) and geophysical (chl) daily observations mosaic\n",
      "141 Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208\n",
      "141 0 dataset_id cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-m cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-m\n",
      "141 1 dataset_id cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-m cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-m\n",
      "141 2 dataset_id cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-m cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-m\n",
      "141 3 dataset_id cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-m cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-m\n",
      "141 4 dataset_id cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-m cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-m\n",
      "141 5 dataset_id cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-m cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-m\n",
      "141 6 dataset_id cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m CMEMS HR-OC Baltic Sea transparency (spm, tur) and geophysical (chl) daily interpolated observations mosaic\n",
      "142 Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations OCEANCOLOUR_BAL_BGC_L3_MY_009_133\n",
      "142 0 dataset_id cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D\n",
      "142 1 dataset_id cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D\n",
      "142 2 dataset_id cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D\n",
      "142 3 dataset_id cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D\n",
      "142 4 dataset_id cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D\n",
      "142 5 dataset_id cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D\n",
      "142 6 dataset_id cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D\n",
      "143 Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations OCEANCOLOUR_BAL_BGC_L3_NRT_009_131\n",
      "143 0 dataset_id cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D\n",
      "143 1 dataset_id cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D\n",
      "143 2 dataset_id cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D\n",
      "143 3 dataset_id cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D\n",
      "144 Baltic Sea Multiyear Ocean Colour Plankton monthly observations OCEANCOLOUR_BAL_BGC_L4_MY_009_134\n",
      "144 0 dataset_id cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M\n",
      "144 1 dataset_id cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M\n",
      "145 Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations OCEANCOLOUR_BAL_BGC_L4_NRT_009_132\n",
      "145 0 dataset_id cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M\n",
      "146 Black Sea, Bio-Geo-Chemical, L3, daily observation OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206\n",
      "146 0 dataset_id cmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-m cmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-m\n",
      "146 1 dataset_id cmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-m cmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-m\n",
      "146 2 dataset_id cmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-m cmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-m\n",
      "146 3 dataset_id cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m CMEMS HR-OC Black Sea transparency (spm, tur) and geophysical (chl) daily observations mosaic\n",
      "147 Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212\n",
      "147 0 dataset_id cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-m cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-m\n",
      "147 1 dataset_id cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-m cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-m\n",
      "147 2 dataset_id cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-m cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-m\n",
      "147 3 dataset_id cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-m cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-m\n",
      "147 4 dataset_id cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-m cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-m\n",
      "147 5 dataset_id cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-m cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-m\n",
      "147 6 dataset_id cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m CMEMS HR-OC Black Sea transparency (spm, tur) and geophysical (chl) daily interpolated observations mosaic\n",
      "148 Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing) OCEANCOLOUR_BLK_BGC_L3_MY_009_153\n",
      "148 0 dataset_id cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D\n",
      "148 1 dataset_id cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D\n",
      "148 2 dataset_id cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D\n",
      "148 3 dataset_id cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D\n",
      "148 4 dataset_id cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D\n",
      "148 5 dataset_id cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D\n",
      "148 6 dataset_id cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D\n",
      "149 Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time) OCEANCOLOUR_BLK_BGC_L3_NRT_009_151\n",
      "149 0 dataset_id cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D\n",
      "149 1 dataset_id cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D\n",
      "149 2 dataset_id cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D\n",
      "149 3 dataset_id cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D\n",
      "149 4 dataset_id cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D\n",
      "149 5 dataset_id cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D\n",
      "149 6 dataset_id cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D\n",
      "150 Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing) OCEANCOLOUR_BLK_BGC_L4_MY_009_154\n",
      "150 0 dataset_id cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D\n",
      "150 1 dataset_id cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M\n",
      "150 2 dataset_id cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D\n",
      "150 3 dataset_id cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M\n",
      "151 Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time) OCEANCOLOUR_BLK_BGC_L4_NRT_009_152\n",
      "151 0 dataset_id cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D\n",
      "151 1 dataset_id cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M\n",
      "151 2 dataset_id cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M\n",
      "151 3 dataset_id cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M\n",
      "151 4 dataset_id cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M\n",
      "152 Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing) OCEANCOLOUR_GLO_BGC_L3_MY_009_103\n",
      "152 0 dataset_id cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D\n",
      "152 1 dataset_id cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D\n",
      "152 2 dataset_id cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D\n",
      "152 3 dataset_id cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D\n",
      "152 4 dataset_id cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D\n",
      "152 5 dataset_id cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-300m_P1D cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-300m_P1D\n",
      "152 6 dataset_id cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D\n",
      "152 7 dataset_id cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D\n",
      "152 8 dataset_id cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D\n",
      "153 Global Ocean Colour Plankton and Reflectances MY L3 daily observations OCEANCOLOUR_GLO_BGC_L3_MY_009_107\n",
      "153 0 dataset_id c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1M\n",
      "153 1 dataset_id c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1M\n",
      "154 Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time) OCEANCOLOUR_GLO_BGC_L3_NRT_009_101\n",
      "154 0 dataset_id cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D\n",
      "154 1 dataset_id cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D\n",
      "154 2 dataset_id cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D\n",
      "154 3 dataset_id cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D\n",
      "154 4 dataset_id cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D\n",
      "154 5 dataset_id cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D\n",
      "154 6 dataset_id cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D\n",
      "154 7 dataset_id cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D\n",
      "154 8 dataset_id cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D\n",
      "155 Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing) OCEANCOLOUR_GLO_BGC_L4_MY_009_104\n",
      "155 0 dataset_id cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M\n",
      "155 1 dataset_id cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D\n",
      "155 2 dataset_id cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M\n",
      "155 3 dataset_id cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D\n",
      "155 4 dataset_id cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M\n",
      "155 5 dataset_id cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M\n",
      "155 6 dataset_id cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M\n",
      "155 7 dataset_id cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M\n",
      "155 8 dataset_id cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M\n",
      "155 9 dataset_id cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M\n",
      "155 10 dataset_id cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D\n",
      "155 11 dataset_id cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M\n",
      "155 12 dataset_id cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M\n",
      "156 Global Ocean Colour Plankton MY L4 monthly observations OCEANCOLOUR_GLO_BGC_L4_MY_009_108\n",
      "156 0 dataset_id c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M\n",
      "157 Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time) OCEANCOLOUR_GLO_BGC_L4_NRT_009_102\n",
      "157 0 dataset_id cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M\n",
      "157 1 dataset_id cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D\n",
      "157 2 dataset_id cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M\n",
      "157 3 dataset_id cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M\n",
      "157 4 dataset_id cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M\n",
      "157 5 dataset_id cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M\n",
      "157 6 dataset_id cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M\n",
      "157 7 dataset_id cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M\n",
      "157 8 dataset_id cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M\n",
      "157 9 dataset_id cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D\n",
      "157 10 dataset_id cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M\n",
      "157 11 dataset_id cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M\n",
      "158 Iberic Sea, Bio-Geo-Chemical, L3, daily observation OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204\n",
      "158 0 dataset_id cmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-m cmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-m\n",
      "158 1 dataset_id cmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-m cmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-m\n",
      "158 2 dataset_id cmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-m cmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-m\n",
      "158 3 dataset_id cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m CMEMS HR-OC Iberian Sea transparency (spm, tur) and geophysical (chl) daily observations mosaic\n",
      "159 Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210\n",
      "159 0 dataset_id cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-m cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-m\n",
      "159 1 dataset_id cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-m cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-m\n",
      "159 2 dataset_id cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-m cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-m\n",
      "159 3 dataset_id cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-m cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-m\n",
      "159 4 dataset_id cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-m cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-m\n",
      "159 5 dataset_id cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-m cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-m\n",
      "159 6 dataset_id cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m CMEMS HR-OC Iberian Sea transparency (spm, tur) and geophysical (chl) daily interpolated observations mosaic\n",
      "160 Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205\n",
      "160 0 dataset_id cmems_obs_oc_med_bgc_geophy_nrt_l3-hr_P1D-m cmems_obs_oc_med_bgc_geophy_nrt_l3-hr_P1D-m\n",
      "160 1 dataset_id cmems_obs_oc_med_bgc_optics_nrt_l3-hr_P1D-m cmems_obs_oc_med_bgc_optics_nrt_l3-hr_P1D-m\n",
      "160 2 dataset_id cmems_obs_oc_med_bgc_transp_nrt_l3-hr_P1D-m cmems_obs_oc_med_bgc_transp_nrt_l3-hr_P1D-m\n",
      "160 3 dataset_id cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m CMEMS HR-OC Mediterranean Sea transparency (spm, tur) and geophysical (chl) daily observations mosaic\n",
      "161 Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211\n",
      "161 0 dataset_id cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-m cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-m\n",
      "161 1 dataset_id cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-m cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-m\n",
      "161 2 dataset_id cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-m cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-m\n",
      "161 3 dataset_id cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-m cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-m\n",
      "161 4 dataset_id cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-m cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-m\n",
      "161 5 dataset_id cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-m cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-m\n",
      "161 6 dataset_id cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m CMEMS HR-OC Mediterranean Sea transparency (spm, tur) and geophysical (chl) daily interpolated observations mosaic\n",
      "162 Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing) OCEANCOLOUR_MED_BGC_L3_MY_009_143\n",
      "162 0 dataset_id cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D\n",
      "162 1 dataset_id cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D\n",
      "162 2 dataset_id cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D\n",
      "162 3 dataset_id cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D\n",
      "162 4 dataset_id cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D\n",
      "162 5 dataset_id cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D\n",
      "162 6 dataset_id cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D\n",
      "163 Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time) OCEANCOLOUR_MED_BGC_L3_NRT_009_141\n",
      "163 0 dataset_id cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D\n",
      "163 1 dataset_id cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D\n",
      "163 2 dataset_id cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D\n",
      "163 3 dataset_id cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D\n",
      "163 4 dataset_id cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D\n",
      "163 5 dataset_id cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D\n",
      "163 6 dataset_id cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D\n",
      "164 Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing) OCEANCOLOUR_MED_BGC_L4_MY_009_144\n",
      "164 0 dataset_id cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D\n",
      "164 1 dataset_id cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M\n",
      "164 2 dataset_id cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D\n",
      "164 3 dataset_id cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M\n",
      "164 4 dataset_id cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D\n",
      "164 5 dataset_id cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M\n",
      "165 Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time) OCEANCOLOUR_MED_BGC_L4_NRT_009_142\n",
      "165 0 dataset_id cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D\n",
      "165 1 dataset_id cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M\n",
      "165 2 dataset_id cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M\n",
      "165 3 dataset_id cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M\n",
      "165 4 dataset_id cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M\n",
      "166 North West Shelf Region, Bio-Geo-Chemical, L3, daily observation OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203\n",
      "166 0 dataset_id cmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-m cmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-m\n",
      "166 1 dataset_id cmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-m cmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-m\n",
      "166 2 dataset_id cmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-m cmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-m\n",
      "166 3 dataset_id cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m CMEMS HR-OC North West Shelf Region transparency (spm, tur) and geophysical (chl) daily observations mosaic\n",
      "167 North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209\n",
      "167 0 dataset_id cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-m cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-m\n",
      "167 1 dataset_id cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-m cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-m\n",
      "167 2 dataset_id cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-m cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-m\n",
      "167 3 dataset_id cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-m cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-m\n",
      "167 4 dataset_id cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-m cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-m\n",
      "167 5 dataset_id cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-m cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-m\n",
      "167 6 dataset_id cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m CMEMS HR-OC North West Shelf Region transparency (spm, tur) and geophysical (chl) daily interpolated observations mosaic\n",
      "168 Black Sea Rim Current Intensity Index OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index\n",
      "168 0 dataset_id blksea_omi_circulation_rim_current_index blksea_omi_circulation_rim_current_index\n",
      "169 Kuroshio Phase from Observations Reprocessing OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged\n",
      "169 0 dataset_id omi_circulation_boundary_pacific_kuroshio_phase_area_averaged omi_circulation_boundary_pacific_kuroshio_phase_area_averaged\n",
      "170 Mediterranean Meridional Overturning Circulation Index from Reanalysis OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean\n",
      "170 0 dataset_id omi_circulation_moc_medsea_area_averaged_mean omi_circulation_moc_medsea_area_averaged_mean\n",
      "171 Nordic Seas Volume Transports from Reanalysis OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged\n",
      "171 0 dataset_id omi_circulation_voltrans_arctic_averaged omi_circulation_voltrans_arctic_averaged\n",
      "172 Black Sea Ocean Heat Content Anomaly (0-300m) time series and trend from Reanalysis & Multi-Observations Reprocessing OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies\n",
      "172 0 dataset_id omi_climate_ohc_blksea_area_averaged_anomalies omi_climate_ohc_blksea_area_averaged_anomalies\n",
      "173 Iberia Biscay Ireland Ocean Heat Content Anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing OMI_CLIMATE_OHC_IBI_area_averaged_anomalies\n",
      "173 0 dataset_id omi_climate_ohc_ibi_area_averaged_anomalies omi_climate_ohc_ibi_area_averaged_anomalies\n",
      "174 Mediterranean Ocean Salt Content (0-300m) OMI_CLIMATE_OSC_MEDSEA_volume_mean\n",
      "174 0 dataset_id omi_climate_osc_medsea_volume_mean omi_climate_osc_medsea_volume_mean\n",
      "175 Baltic Sea Mean Sea Level time series and trend from Observations Reprocessing OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies\n",
      "175 0 dataset_id omi_climate_sl_baltic_area_averaged_anomalies omi_climate_sl_baltic_area_averaged_anomalies\n",
      "176 Black Sea Mean Sea Level time series and trend from Observations Reprocessing OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies\n",
      "176 0 dataset_id omi_climate_sl_blksea_area_averaged_anomalies omi_climate_sl_blksea_area_averaged_anomalies\n",
      "177 Global Ocean Mean Sea Level time series and trend from Observations Reprocessing OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies\n",
      "177 0 dataset_id omi_climate_sl_global_area_averaged_anomalies omi_climate_sl_global_area_averaged_anomalies\n",
      "178 Global Ocean Mean Sea Level trend map from Observations Reprocessing OMI_CLIMATE_SL_GLOBAL_regional_trends\n",
      "178 0 dataset_id omi_climate_sl_global_regional_trends_202311 omi_climate_sl_global_regional_trends_202311\n",
      "179 Atlantic Iberian Biscay Mean Sea Level time series and trend from Observations Reprocessing OMI_CLIMATE_SL_IBI_regional_trends\n",
      "179 0 dataset_id omi_climate_sl_ibi_area_averaged_anomalies omi_climate_sl_ibi_area_averaged_anomalies\n",
      "180 Mediterranean Sea Mean Sea Level time series and trend from Observations Reprocessing OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies\n",
      "180 0 dataset_id omi_climate_sl_medsea_area_averaged_anomalies omi_climate_sl_medsea_area_averaged_anomalies\n",
      "181 North West Atlantic Shelf Mean Sea Level time series and trend from Observations Reprocessing OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies\n",
      "181 0 dataset_id omi_climate_sl_northwestshelf_area_averaged_anomalies_202311 omi_climate_sl_northwestshelf_area_averaged_anomalies_202311\n",
      "182 Baltic Sea Surface Temperature anomaly time series and trend from Observations Reprocessing OMI_CLIMATE_SST_BAL_area_averaged_anomalies\n",
      "182 0 dataset_id omi_climate_sst_bal_area_averaged_anomalies omi_climate_sst_bal_area_averaged_anomalies\n",
      "183 Baltic Sea Surface Temperature cumulative trend map from Observations Reprocessing OMI_CLIMATE_SST_BAL_trend\n",
      "183 0 dataset_id omi_climate_sst_bal_trend omi_climate_sst_bal_trend\n",
      "184 Iberia Biscay Ireland Sea Surface Temperature time series and trend from Observations Reprocessing OMI_CLIMATE_SST_IBI_area_averaged_anomalies\n",
      "184 0 dataset_id omi_climate_sst_ibi_area_averaged_anomalies omi_climate_sst_ibi_area_averaged_anomalies\n",
      "185 Iberia Biscay Ireland Sea Surface Temperature trend map from Observations Reprocessing OMI_CLIMATE_SST_IBI_trend\n",
      "185 0 dataset_id omi_climate_sst_ibi_trend omi_climate_sst_ibi_trend\n",
      "186 Arctic Sea and Sea Ice Surface Temperature anomaly time series based on reprocessed observations OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies\n",
      "186 0 dataset_id omi_climate_sst_ist_arctic_area_averaged_anomalies omi_climate_sst_ist_arctic_area_averaged_anomalies\n",
      "187 Arctic Sea and Sea Ice Surface Temperature 2D trend from climatology based on reprocessed observations OMI_CLIMATE_SST_IST_ARCTIC_trend\n",
      "187 0 dataset_id omi_climate_sst_ist_arctic_trend omi_climate_sst_ist_arctic_trend\n",
      "188 European North West Shelf Sea Surface Temperature time series and trend from Observations Reprocessing OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies\n",
      "188 0 dataset_id omi_climate_sst_northwestshelf_area_averaged_anomalies omi_climate_sst_northwestshelf_area_averaged_anomalies\n",
      "189 European North West Shelf Sea Surface Temperature trend map from Observations Reprocessing OMI_CLIMATE_SST_NORTHWESTSHELF_trend\n",
      "189 0 dataset_id omi_climate_sst_northwestshelf_trend omi_climate_sst_northwestshelf_trend\n",
      "190 North Pacific Gyre Oscillation from Observations Reprocessing OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection\n",
      "190 0 dataset_id omi_extreme_climvar_pacific_npgo_sla_eof_mode_projection omi_extreme_climvar_pacific_npgo_sla_eof_mode_projection\n",
      "191 Baltic Sea Level extreme from Observations Reprocessing OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs\n",
      "191 0 dataset_id omi_extreme_sl_baltic_slev_mean_and_anomaly_obs omi_extreme_sl_baltic_slev_mean_and_anomaly_obs\n",
      "192 Iberia Biscay Ireland Sea Level extreme from Observations Reprocessing OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs\n",
      "192 0 dataset_id omi_extreme_sl_ibi_slev_mean_and_anomaly_obs omi_extreme_sl_ibi_slev_mean_and_anomaly_obs\n",
      "193 Mediterranean Sea Mean Sea Level extreme from Observations Reprocessing OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs\n",
      "193 0 dataset_id omi_extreme_sl_medsea_slev_mean_and_anomaly_obs omi_extreme_sl_medsea_slev_mean_and_anomaly_obs\n",
      "194 North West Shelf Mean Sea Level extreme from Observations Reprocessing OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs\n",
      "194 0 dataset_id omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs\n",
      "195 Baltic Sea Surface Temperature extreme from Observations Reprocessing OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs\n",
      "195 0 dataset_id omi_extreme_sst_baltic_sst_mean_and_anomaly_obs omi_extreme_sst_baltic_sst_mean_and_anomaly_obs\n",
      "196 Iberia Biscay Ireland Sea Surface Temperature extreme from Observations Reprocessing OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs\n",
      "196 0 dataset_id omi_extreme_sst_ibi_sst_mean_and_anomaly_obs omi_extreme_sst_ibi_sst_mean_and_anomaly_obs\n",
      "197 Mediterranean Sea Surface Temperature extreme from Observations Reprocessing OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs\n",
      "197 0 dataset_id omi_extreme_sst_medsea_sst_mean_and_anomaly_obs omi_extreme_sst_medsea_sst_mean_and_anomaly_obs\n",
      "198 North West Shelf Sea Surface Temperature extreme from Observations Reprocessing OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs\n",
      "198 0 dataset_id omi_extreme_sst_northwestshelf_sst_mean_and_anomaly_obs omi_extreme_sst_northwestshelf_sst_mean_and_anomaly_obs\n",
      "199 Baltic Sea Significant Wave Height extreme from Observations Reprocessing OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs\n",
      "199 0 dataset_id omi_extreme_wave_baltic_swh_mean_and_anomaly_obs omi_extreme_wave_baltic_swh_mean_and_anomaly_obs\n",
      "200 Iberia Biscay Ireland Significant Wave Height extreme from Observations Reprocessing OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs\n",
      "200 0 dataset_id omi_extreme_wave_ibi_swh_mean_and_anomaly_obs omi_extreme_wave_ibi_swh_mean_and_anomaly_obs\n",
      "201 Mediterranean Sea Significant Wave Height extreme from Observations Reprocessing OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs\n",
      "201 0 dataset_id omi_extreme_wave_medsea_swh_mean_and_anomaly_obs omi_extreme_wave_medsea_swh_mean_and_anomaly_obs\n",
      "202 North West Shelf Significant Wave Height extreme from Observations Reprocessing OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs\n",
      "202 0 dataset_id omi_extreme_wave_northwestshelf_swh_mean_and_anomaly_obs omi_extreme_wave_northwestshelf_swh_mean_and_anomaly_obs\n",
      "203 Arctic Ocean Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean\n",
      "203 0 dataset_id omi_health_chl_arctic_oceancolour_area_averaged_mean omi_health_chl_arctic_oceancolour_area_averaged_mean\n",
      "204 North Atlantic Ocean Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean\n",
      "204 0 dataset_id omi_health_chl_atlantic_oceancolour_area_averaged_mean omi_health_chl_atlantic_oceancolour_area_averaged_mean\n",
      "205 Baltic Sea Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean\n",
      "205 0 dataset_id omi_health_chl_baltic_oceancolour_area_averaged_mean omi_health_chl_baltic_oceancolour_area_averaged_mean\n",
      "206 Baltic Sea Chlorophyll-a trend map from Observations Reprocessing OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend\n",
      "206 0 dataset_id omi_health_chl_baltic_oceancolour_trend omi_health_chl_baltic_oceancolour_trend\n",
      "207 Black Sea Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean\n",
      "207 0 dataset_id omi_health_chl_blksea_oceancolour_area_averaged_mean omi_health_chl_blksea_oceancolour_area_averaged_mean\n",
      "208 Black Sea Chlorophyll-a trend map from Observations Reprocessing OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend\n",
      "208 0 dataset_id omi_health_chl_blksea_oceancolour_trend omi_health_chl_blksea_oceancolour_trend\n",
      "209 North Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean\n",
      "209 0 dataset_id omi_health_chl_global_oceancolour_oligo_nag_area_mean omi_health_chl_global_oceancolour_oligo_nag_area_mean\n",
      "210 North Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean\n",
      "210 0 dataset_id omi_health_chl_global_oceancolour_oligo_npg_area_mean omi_health_chl_global_oceancolour_oligo_npg_area_mean\n",
      "211 South Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean\n",
      "211 0 dataset_id omi_health_chl_global_oceancolour_oligo_sag_area_mean omi_health_chl_global_oceancolour_oligo_sag_area_mean\n",
      "212 South Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean\n",
      "212 0 dataset_id omi_health_chl_global_oceancolour_oligo_spg_area_mean omi_health_chl_global_oceancolour_oligo_spg_area_mean\n",
      "213 Global Ocean Chlorophyll-a trend map from Observations Reprocessing OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend\n",
      "213 0 dataset_id omi_health_chl_global_oceancolour_trend omi_health_chl_global_oceancolour_trend\n",
      "214 Mediterranean Sea Chlorophyll-a time series and trend from Observations Reprocessing OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean\n",
      "214 0 dataset_id omi_health_chl_medsea_oceancolour_area_averaged_mean omi_health_chl_medsea_oceancolour_area_averaged_mean\n",
      "215 Mediterranean Sea Chlorophyll-a trend map from Observations Reprocessing OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend\n",
      "215 0 dataset_id omi_health_chl_medsea_oceancolour_trend omi_health_chl_medsea_oceancolour_trend\n",
      "216 Mediterranean Water Mass Formation Rates from Reanalysis OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean\n",
      "216 0 dataset_id omi_var_extreme_wmf_medsea_area_averaged_mean omi_var_extreme_wmf_medsea_area_averaged_mean\n",
      "217 Antarctic Ocean - High Resolution Sea Ice Information SEAICE_ANT_PHY_AUTO_L3_NRT_011_012\n",
      "217 0 dataset_id cmems_obs-si_ant_phy_nrt_l3-1km_P1D cmems_obs-si_ant_phy_nrt_l3-1km_P1D\n",
      "218 Antarctic Ocean Sea Ice Drift REPROCESSED SEAICE_ANT_PHY_L3_MY_011_018\n",
      "218 0 dataset_id cmems_obs-si_ant_physic_my_drift-amsr_P2D cmems_obs-si_ant_physic_my_drift-amsr_P2D\n",
      "218 1 dataset_id cmems_obs-si_ant_physic_my_drift-amsr_P3D cmems_obs-si_ant_physic_my_drift-amsr_P3D\n",
      "219 Arctic Ocean - High resolution Sea Ice Concentration and Sea Ice Type SEAICE_ARC_PHY_AUTO_L4_NRT_011_015\n",
      "219 0 dataset_id cmems_obs-si_arc_phy-icetype_nrt_L4-auto_P1D cmems_obs-si_arc_phy-icetype_nrt_L4-auto_P1D\n",
      "219 1 dataset_id cmems_obs-si_arc_phy-siconc_nrt_L4-auto_P1D cmems_obs-si_arc_phy-siconc_nrt_L4-auto_P1D\n",
      "219 2 dataset_id cmems_obs-si_arc_phy_nrt_l3_P1D cmems_obs-si_arc_phy_nrt_l3_P1D\n",
      "220 Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021\n",
      "220 0 dataset_id cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m DMI Arctic SST/IST, L3S , (SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021)\n",
      "221 Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016\n",
      "221 0 dataset_id cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m Arctic Sea and Ice Surface Temperature, L4, 5km daily reprocessed SST and IST\n",
      "222 ARCTIC Ocean and Sea-Ice Sigma-Nought SEAICE_ARC_PHY_L3M_NRT_011_017\n",
      "222 0 dataset_id cmems_obs-si_arc_physic_nrt_L2-EW_PT1H-irr cmems_obs-si_arc_physic_nrt_L2-EW_PT1H-irr\n",
      "223 Sea Ice Thickness derived from merging CryoSat-2 and SMOS ice thickness SEAICE_ARC_PHY_L4_NRT_011_014\n",
      "223 0 dataset_id esa_obs-si_arc_phy-sit_nrt_l4_multi_P1D-m esa_obs-si_arc_phy-sit_nrt_l4_multi_P1D-m\n",
      "224 Arctic Ocean Sea Ice Drift REPROCESSED SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010\n",
      "224 0 dataset_id CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE\n",
      "224 1 dataset_id CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE\n",
      "224 2 dataset_id CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE\n",
      "224 3 dataset_id CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE\n",
      "224 4 dataset_id CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE\n",
      "224 5 dataset_id CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE\n",
      "224 6 dataset_id CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE\n",
      "224 7 dataset_id CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE\n",
      "224 8 dataset_id cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D Arctic Ocean - Sea Ice - Monthly Drift, SSMI\n",
      "224 9 dataset_id cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D Arctic Ocean - Sea Ice - 3 days Drift, SSMI\n",
      "224 10 dataset_id cmems_obs-si_arc_phy_my_drift-amsr_P2D cmems_obs-si_arc_phy_my_drift-amsr_P2D\n",
      "224 11 dataset_id cmems_obs-si_arc_phy_my_drift-amsr_P3D cmems_obs-si_arc_phy_my_drift-amsr_P3D\n",
      "224 12 dataset_id cmems_obs-si_arc_phy_my_drift-amsr_P6D cmems_obs-si_arc_phy_my_drift-amsr_P6D\n",
      "225 Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002\n",
      "225 0 dataset_id METNO-ARC-SEAICE_CONC-L4-NRT-OBS METNO-ARC-SEAICE_CONC-L4-NRT-OBS\n",
      "225 1 dataset_id cmems_obs-si_arc_physic_nrt_1km-grl_P1D-irr cmems_obs-si_arc_physic_nrt_1km-grl_P1D-irr\n",
      "225 2 dataset_id cmems_obs-si_arc_physic_nrt_1km-grl_P1WT3D-m cmems_obs-si_arc_physic_nrt_1km-grl_P1WT3D-m\n",
      "226 SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1 SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007\n",
      "226 0 dataset_id DMI-ARC-SEAICE_BERG-L4-NRT-OBS DMI-ARC-SEAICE_BERG-L4-NRT-OBS\n",
      "226 1 dataset_id DMI-ARC-SEAICE_BERG_IW-L4-NRT-OBS DMI-ARC-SEAICE_BERG_IW-L4-NRT-OBS\n",
      "226 2 dataset_id DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS DMI-ARC-SEAICE_BERG_MOSAIC-L4-NRT-OBS\n",
      "226 3 dataset_id DMI-ARC-SEAICE_BERG_MOSAIC_IW-L4-NRT-OBS DMI-ARC-SEAICE_BERG_MOSAIC_IW-L4-NRT-OBS\n",
      "226 4 dataset_id cmems_sat-si_arc_berg-point_nrt_ew_d cmems_sat-si_arc_berg-point_nrt_ew_d\n",
      "226 5 dataset_id cmems_sat-si_arc_berg-point_nrt_iw_d cmems_sat-si_arc_berg-point_nrt_iw_d\n",
      "227 Arctic Ocean - Sea and Ice Surface Temperature SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008\n",
      "227 0 dataset_id DMI-ARC-SEAICE_TEMP-L4-NRT-OBS Arctic Sea and Ice Surface Temperature, L4, 5km daily  (DMI-ARC-SEAICE_TEMP-L4-NRT-OBS)\n",
      "228 Baltic Sea ice concentration, extent, and classification time series SEAICE_BAL_PHY_L4_MY_011_019\n",
      "228 0 dataset_id cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m Baltic Ice Thickness, L4, 1km daily\n",
      "228 1 dataset_id cmems_obs-si_bal_seaice-conc_my_1km Baltic Ice Concentration and classification, L4, 1km daily\n",
      "229 Baltic Sea - Sea Ice Concentration and Thickness Charts SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004\n",
      "229 0 dataset_id FMI-BAL-SEAICE_CONC-L4-NRT-OBS Baltic Ice Concentration, L4, 1km daily (FMI-BAL-SEAICE_CONC-L4-NRT-OBS)\n",
      "229 1 dataset_id FMI-BAL-SEAICE_THICK-L4-NRT-OBS Baltic Ice Thickness, L4, 1km daily (FMI-BAL-SEAICE_THICK-L4-NRT-OBS)\n",
      "229 2 dataset_id cmems_obs-si_bal_phy-sie_nrt_l4_P1D-m cmems_obs-si_bal_phy-sie_nrt_l4_P1D-m\n",
      "230 Baltic Sea - SAR Sea Ice Thickness and Drift, Multisensor Sea Ice Concentration SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011\n",
      "230 0 dataset_id FMI-BAL-SEAICE_DRIFT-SAR-NRT-OBS FMI-BAL-SEAICE_DRIFT-SAR-NRT-OBS\n",
      "230 1 dataset_id FMI-BAL-SEAICE_THICK-MOSAIC-SAR-NRT-OBS Baltic ice thickness mosaic, l4, 0.5km daily (fmi-bal-seaice thick mosaic-sar-nrt-obs)\n",
      "230 2 dataset_id FMI-BAL-SEAICE_THICK-SAR-NRT-OBS FMI-BAL-SEAICE_THICK-SAR-NRT-OBS\n",
      "230 3 dataset_id cmems_obs-si_bal_phy-sit_nrt_x_500m_l4_P1D cmems_obs-si_bal_phy-sit_nrt_x_500m_l4_P1D\n",
      "230 4 dataset_id cmems_sat-si_bal_conc_nrt_500m_d cmems_sat-si_bal_conc_nrt_500m_d\n",
      "230 5 dataset_id cmems_sat-si_bal_thick_nrt_500m_hi Baltic ice thickness, l4 sar, 0.5km daily (fmi-bal-seaice thick-sar-iw-nrt-obs)\n",
      "231 Arctic Ocean - Sea Ice Thickness REPROCESSED SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013\n",
      "231 0 dataset_id c3s_obs-si_glo_phy_my_nh-l3_P1M Sea Ice Thickness\n",
      "231 1 dataset_id c3s_obs_si_glo_phy_my_nh_l3_P1M c3s_obs_si_glo_phy_my_nh_l3_P1M\n",
      "232 Global Ocean - High Resolution SAR Sea Ice Drift Time Series SEAICE_GLO_PHY_L4_MY_011_020\n",
      "232 0 dataset_id cmems_obs-si_glo_phy-drift-north_my_l4_P1D-m cmems_obs-si_glo_phy-drift-north_my_l4_P1D-m\n",
      "232 1 dataset_id cmems_obs-si_glo_phy-drift-south_my_l4_P1D-m cmems_obs-si_glo_phy-drift-south_my_l4_P1D-m\n",
      "233 Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF) SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001\n",
      "233 0 dataset_id METNO-GLO-SEAICE_DRIFT-NORTH-L4-NRT-OBS METNO-GLO-SEAICE_DRIFT-NORTH-L4-NRT-OBS\n",
      "233 1 dataset_id METNO-GLO-SEAICE_DRIFT-SOUTH-L4-NRT-OBS METNO-GLO-SEAICE_DRIFT-SOUTH-L4-NRT-OBS\n",
      "233 2 dataset_id osisaf_obs-si_glo_phy-sic-north_nrt_amsr2_l4_P1D-m osisaf_obs-si_glo_phy-sic-north_nrt_amsr2_l4_P1D-m\n",
      "233 3 dataset_id osisaf_obs-si_glo_phy-sic-north_nrt_ssmis_l4_P1D-m osisaf_obs-si_glo_phy-sic-north_nrt_ssmis_l4_P1D-m\n",
      "233 4 dataset_id osisaf_obs-si_glo_phy-sic-south_nrt_amsr2_l4_P1D-m osisaf_obs-si_glo_phy-sic-south_nrt_amsr2_l4_P1D-m\n",
      "233 5 dataset_id osisaf_obs-si_glo_phy-sic-south_nrt_ssmis_l4_P1D-m osisaf_obs-si_glo_phy-sic-south_nrt_ssmis_l4_P1D-m\n",
      "233 6 dataset_id osisaf_obs-si_glo_phy-siedge_nrt_nh-P1D osisaf_obs-si_glo_phy-siedge_nrt_nh-P1D\n",
      "233 7 dataset_id osisaf_obs-si_glo_phy-siedge_nrt_sh-P1D osisaf_obs-si_glo_phy-siedge_nrt_sh-P1D\n",
      "233 8 dataset_id osisaf_obs-si_glo_phy-sitype_nrt_nh-P1D osisaf_obs-si_glo_phy-sitype_nrt_nh-P1D\n",
      "233 9 dataset_id osisaf_obs-si_glo_phy-sitype_nrt_sh-P1D osisaf_obs-si_glo_phy-sitype_nrt_sh-P1D\n",
      "234 Global Ocean - High Resolution SAR Sea Ice Drift SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006\n",
      "234 0 dataset_id DTU-GLO-SEAICE_DRIFT-NORTH-L4-NRT-OBS DTU-GLO-SEAICE_DRIFT-NORTH-L4-NRT-OBS\n",
      "234 1 dataset_id DTU-GLO-SEAICE_DRIFT-SOUTH-L4-NRT-OBS DTU-GLO-SEAICE_DRIFT-SOUTH-L4-NRT-OBS\n",
      "234 2 dataset_id cmems_sat-si_glo_drift_nrt_north_d cmems_sat-si_glo_drift_nrt_north_d\n",
      "234 3 dataset_id cmems_sat-si_glo_drift_nrt_south_d cmems_sat-si_glo_drift_nrt_south_d\n",
      "235 Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF) SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009\n",
      "235 0 dataset_id OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS Interim Sea Ice Concentration Climate Data Record Version 3 from the EUMETSAT OSI SAF\n",
      "235 1 dataset_id OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS Interim Sea Ice Concentration Climate Data Record Version 3 from the EUMETSAT OSI SAF\n",
      "235 2 dataset_id OSISAF-GLO-SEAICE_CONC_TIMESERIES-NH-LA-OBS OSISAF-GLO-SEAICE_CONC_TIMESERIES-NH-LA-OBS\n",
      "235 3 dataset_id OSISAF-GLO-SEAICE_CONC_TIMESERIES-SH-LA-OBS OSISAF-GLO-SEAICE_CONC_TIMESERIES-SH-LA-OBS\n",
      "236 BLACK SEA MEAN DYNAMIC TOPOGRAPHY SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067\n",
      "236 0 dataset_id cmems_obs-sl_blk_phy-mdt_my_l4-0.0625deg_P20Y Mean Dynamic Topography\n",
      "237 EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION SEALEVEL_EUR_PHY_L3_MY_008_061\n",
      "237 0 dataset_id cmems_obs-sl_eur_phy-ssh_my_al-l3-duacs_PT1S Saral/AltiKa\n",
      "237 1 dataset_id cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S Saral/AltiKa geodetic orbit\n",
      "237 2 dataset_id cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S Cryosat-2\n",
      "237 3 dataset_id cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S Cryosat-2 new orbit\n",
      "237 4 dataset_id cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S ERS-1\n",
      "237 5 dataset_id cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S ERS-1 geodetic phase\n",
      "237 6 dataset_id cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S ERS-2\n",
      "237 7 dataset_id cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S Envisat\n",
      "237 8 dataset_id cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S Envisat new orbit\n",
      "237 9 dataset_id cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S GFO\n",
      "237 10 dataset_id cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S HaiYang-2A\n",
      "237 11 dataset_id cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S HaiYang-2A geodetic orbit\n",
      "237 12 dataset_id cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S HaiYang-2B\n",
      "237 13 dataset_id cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S Jason-1\n",
      "237 14 dataset_id cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S Jason-1 geodetic orbit\n",
      "237 15 dataset_id cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S Jason-1 new orbit\n",
      "237 16 dataset_id cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S OSTM/Jason-2\n",
      "237 17 dataset_id cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S OSTM/Jason-2 long-repeat orbit\n",
      "237 18 dataset_id cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S OSTM/Jason-2 interleaved orbit\n",
      "237 19 dataset_id cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S Jason-3\n",
      "237 20 dataset_id cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S Jason-3 interleaved orbit\n",
      "237 21 dataset_id cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S Sentinel-3A\n",
      "237 22 dataset_id cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S Sentinel-3B\n",
      "237 23 dataset_id cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S Sentinel-6A LRM\n",
      "237 24 dataset_id cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S TOPEX/Poseidon\n",
      "237 25 dataset_id cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S TOPEX/Poseidon new orbit\n",
      "237 26 dataset_id cmems_obs-sl_eur_phy-ssh_myint_alg-l3-duacs_PT1S cmems_obs-sl_eur_phy-ssh_myint_alg-l3-duacs_PT1S\n",
      "237 27 dataset_id cmems_obs-sl_eur_phy-ssh_myint_c2n-l3-duacs_PT1S cmems_obs-sl_eur_phy-ssh_myint_c2n-l3-duacs_PT1S\n",
      "237 28 dataset_id cmems_obs-sl_eur_phy-ssh_myint_h2b-l3-duacs_PT1S cmems_obs-sl_eur_phy-ssh_myint_h2b-l3-duacs_PT1S\n",
      "237 29 dataset_id cmems_obs-sl_eur_phy-ssh_myint_j3n-l3-duacs_PT1S cmems_obs-sl_eur_phy-ssh_myint_j3n-l3-duacs_PT1S\n",
      "237 30 dataset_id cmems_obs-sl_eur_phy-ssh_myint_s3a-l3-duacs_PT1S cmems_obs-sl_eur_phy-ssh_myint_s3a-l3-duacs_PT1S\n",
      "237 31 dataset_id cmems_obs-sl_eur_phy-ssh_myint_s3b-l3-duacs_PT1S cmems_obs-sl_eur_phy-ssh_myint_s3b-l3-duacs_PT1S\n",
      "237 32 dataset_id cmems_obs-sl_eur_phy-ssh_myint_s6a-lr-l3-duacs_PT1S cmems_obs-sl_eur_phy-ssh_myint_s6a-lr-l3-duacs_PT1S\n",
      "238 EUROPEAN SEAS ALONG-TRACK L3 SEA LEVEL ANOMALIES NRT SEALEVEL_EUR_PHY_L3_NRT_008_059\n",
      "238 0 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_al-l3-duacs_PT1S Saral/AltiKa\n",
      "238 1 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_c2n-l3-duacs_PT1S Cryosat-2 new orbit\n",
      "238 2 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_h2b-l3-duacs_PT0.2S cmems_obs-sl_eur_phy-ssh_nrt_h2b-l3-duacs_PT0.2S\n",
      "238 3 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_h2b-l3-duacs_PT1S HaiYang-2B\n",
      "238 4 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_j3n-l3-duacs_PT0.2S Jason-3 interleaved orbit\n",
      "238 5 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_j3n-l3-duacs_PT1S Jason-3 interleaved orbit\n",
      "238 6 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_s3a-l3-duacs_PT0.2S Sentinel-3A\n",
      "238 7 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_s3a-l3-duacs_PT1S Sentinel-3A\n",
      "238 8 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_s3b-l3-duacs_PT0.2S Sentinel-3B\n",
      "238 9 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_s3b-l3-duacs_PT1S Sentinel-3B\n",
      "238 10 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_s6a-hr-l3-duacs_PT0.2S Sentinel-6A SAR\n",
      "238 11 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_s6a-hr-l3-duacs_PT1S Sentinel-6A SAR\n",
      "238 12 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_swon-l3-duacs_PT0.2S SWOT nadir\n",
      "238 13 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_swon-l3-duacs_PT1S SWOT nadir\n",
      "239 EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING) SEALEVEL_EUR_PHY_L4_MY_008_068\n",
      "239 0 dataset_id cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D DT merged all satellites European Seas Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables\n",
      "239 1 dataset_id cmems_obs-sl_eur_phy-ssh_myint_allsat-l4-duacs-0.125deg_P1D cmems_obs-sl_eur_phy-ssh_myint_allsat-l4-duacs-0.125deg_P1D\n",
      "240 EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT SEALEVEL_EUR_PHY_L4_NRT_008_060\n",
      "240 0 dataset_id cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D NRT merged all satellites European Seas Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables\n",
      "241 EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT SEALEVEL_EUR_PHY_L4_NRT_OBSERVATIONS_008_060\n",
      "241 0 dataset_id dataset-duacs-nrt-europe-merged-allsat-phy-l4 NRT merged all satellites European Seas Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables\n",
      "242 GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE) SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057\n",
      "242 0 dataset_id c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D DT merged two satellites Global Ocean Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables\n",
      "242 1 dataset_id c3s_obs-sl_glo_phy-ssh_myint_twosat-l4-duacs-0.25deg_P1D c3s_obs-sl_glo_phy-ssh_myint_twosat-l4-duacs-0.25deg_P1D\n",
      "243 GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION SEALEVEL_GLO_PHY_L3_MY_008_062\n",
      "243 0 dataset_id cmems_obs-sl_glo_phy-ssh_my_al-l3-duacs_PT1S Saral/AltiKa\n",
      "243 1 dataset_id cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S Saral/AltiKa geodetic orbit\n",
      "243 2 dataset_id cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S Cryosat-2\n",
      "243 3 dataset_id cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S Cryosat-2 new orbit\n",
      "243 4 dataset_id cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S ERS-1\n",
      "243 5 dataset_id cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S ERS-1 geodetic phase\n",
      "243 6 dataset_id cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S ERS-2\n",
      "243 7 dataset_id cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S Envisat\n",
      "243 8 dataset_id cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S Envisat new orbit\n",
      "243 9 dataset_id cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S GFO\n",
      "243 10 dataset_id cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S HaiYang-2A\n",
      "243 11 dataset_id cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S HaiYang-2A geodetic orbit\n",
      "243 12 dataset_id cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S HaiYang-2B\n",
      "243 13 dataset_id cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S Jason-1\n",
      "243 14 dataset_id cmems_obs-sl_glo_phy-ssh_my_j1g-l3-duacs_PT1S Jason-1 geodetic orbit\n",
      "243 15 dataset_id cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S Jason-1 new orbit\n",
      "243 16 dataset_id cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S OSTM/Jason-2\n",
      "243 17 dataset_id cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S OSTM/Jason-2 long-repeat orbit\n",
      "243 18 dataset_id cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S OSTM/Jason-2 interleaved orbit\n",
      "243 19 dataset_id cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S Jason-3\n",
      "243 20 dataset_id cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S Jason-3 interleaved orbit\n",
      "243 21 dataset_id cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S Sentinel-3A\n",
      "243 22 dataset_id cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S Sentinel-3B\n",
      "243 23 dataset_id cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S Sentinel-6A LRM\n",
      "243 24 dataset_id cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S TOPEX/Poseidon\n",
      "243 25 dataset_id cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S TOPEX/Poseidon new orbit\n",
      "243 26 dataset_id cmems_obs-sl_glo_phy-ssh_myint_alg-l3-duacs_PT1S cmems_obs-sl_glo_phy-ssh_myint_alg-l3-duacs_PT1S\n",
      "243 27 dataset_id cmems_obs-sl_glo_phy-ssh_myint_c2n-l3-duacs_PT1S cmems_obs-sl_glo_phy-ssh_myint_c2n-l3-duacs_PT1S\n",
      "243 28 dataset_id cmems_obs-sl_glo_phy-ssh_myint_h2b-l3-duacs_PT1S cmems_obs-sl_glo_phy-ssh_myint_h2b-l3-duacs_PT1S\n",
      "243 29 dataset_id cmems_obs-sl_glo_phy-ssh_myint_j3n-l3-duacs_PT1S cmems_obs-sl_glo_phy-ssh_myint_j3n-l3-duacs_PT1S\n",
      "243 30 dataset_id cmems_obs-sl_glo_phy-ssh_myint_s3a-l3-duacs_PT1S cmems_obs-sl_glo_phy-ssh_myint_s3a-l3-duacs_PT1S\n",
      "243 31 dataset_id cmems_obs-sl_glo_phy-ssh_myint_s3b-l3-duacs_PT1S cmems_obs-sl_glo_phy-ssh_myint_s3b-l3-duacs_PT1S\n",
      "243 32 dataset_id cmems_obs-sl_glo_phy-ssh_myint_s6a-lr-l3-duacs_PT1S cmems_obs-sl_glo_phy-ssh_myint_s6a-lr-l3-duacs_PT1S\n",
      "244 GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS NRT SEALEVEL_GLO_PHY_L3_NRT_008_044\n",
      "244 0 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_al-l3-duacs_PT1S Saral/AltiKa\n",
      "244 1 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_c2n-l3-duacs_PT1S Cryosat-2 new orbit\n",
      "244 2 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_h2b-l3-duacs_PT0.2S cmems_obs-sl_glo_phy-ssh_nrt_h2b-l3-duacs_PT0.2S\n",
      "244 3 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_h2b-l3-duacs_PT1S HaiYang-2B\n",
      "244 4 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_j3n-l3-duacs_PT0.2S Jason-3 interleaved orbit\n",
      "244 5 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_j3n-l3-duacs_PT1S Jason-3 interleaved orbit\n",
      "244 6 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_s3a-l3-duacs_PT0.2S Sentinel-3A\n",
      "244 7 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_s3a-l3-duacs_PT1S Sentinel-3A\n",
      "244 8 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_s3b-l3-duacs_PT0.2S Sentinel-3B\n",
      "244 9 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_s3b-l3-duacs_PT1S Sentinel-3B\n",
      "244 10 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_s6a-hr-l3-duacs_PT0.2S Sentinel-6A SAR\n",
      "244 11 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_s6a-hr-l3-duacs_PT1S Sentinel-6A SAR\n",
      "244 12 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_swon-l3-duacs_PT0.2S SWOT nadir\n",
      "244 13 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_swon-l3-duacs_PT1S SWOT nadir\n",
      "245 GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING) SEALEVEL_GLO_PHY_L4_MY_008_047\n",
      "245 0 dataset_id cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.25deg_P1D DT merged all satellites Global Ocean Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables\n",
      "245 1 dataset_id cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.25deg_P1M-m DT merged all satellites Global Ocean Ocean Gridded Monthly Mean of Sea Level Anomalies L4 product\n",
      "245 2 dataset_id cmems_obs-sl_glo_phy-ssh_myint_allsat-l4-duacs-0.25deg_P1D cmems_obs-sl_glo_phy-ssh_myint_allsat-l4-duacs-0.25deg_P1D\n",
      "245 3 dataset_id cmems_obs-sl_glo_phy-ssh_myint_allsat-l4-duacs-0.25deg_P1M-m cmems_obs-sl_glo_phy-ssh_myint_allsat-l4-duacs-0.25deg_P1M-m\n",
      "246 GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT SEALEVEL_GLO_PHY_L4_NRT_008_046\n",
      "246 0 dataset_id cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D NRT merged all satellites Global Ocean Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables\n",
      "247 GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS_008_046\n",
      "247 0 dataset_id dataset-duacs-nrt-global-merged-allsat-phy-l4 NRT merged all satellites Global Ocean Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables\n",
      "248 GLOBAL OCEAN MEAN DYNAMIC TOPOGRAPHY SEALEVEL_GLO_PHY_MDT_008_063\n",
      "248 0 dataset_id cnes_obs-sl_glo_phy-mdt_my_0.125deg_P20Y Mean Dynamic Topography\n",
      "249 GLOBAL OCEAN GRIDDED NORMALIZED MEASUREMENT NOISE OF SEA LEVEL ANOMALIES SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033\n",
      "249 0 dataset_id cmems_obs-sl_glo_phy-noise_mynrt_multi-L4-duacs-0.25deg_P1Y cmems_obs-sl_glo_phy-noise_mynrt_multi-L4-duacs-0.25deg_P1Y\n",
      "250 MEDITERRANEAN SEA MEAN DYNAMIC TOPOGRAPHY SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066\n",
      "250 0 dataset_id cmems_obs-sl_med_phy-mdt_my_l4-0.0417deg_P20Y Mean Dynamic Topography\n",
      "251 European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed SST_ATL_PHY_L3S_MY_010_038\n",
      "251 0 dataset_id cmems_obs-sst_atl_phy_my_l3s_P1D-m Merged collation of sea surface temperature from multiple satellite sources\n",
      "252 European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations SST_ATL_PHY_L3S_NRT_010_037\n",
      "252 0 dataset_id cmems_obs-sst_atl_phy_l3s_gir_P1D-m ODYSSEA North-East Atlantic Sea Surface Temperature Gridded Level 3S Daily Geostationary Infra Red (GIR) Multi-Sensor Observations\n",
      "252 1 dataset_id cmems_obs-sst_atl_phy_l3s_pir_P1D-m ODYSSEA North-East Atlantic Sea Surface Temperature Gridded Level 3S Daily Polar orbiting Infra Red (PIR) Multi-Sensor Observations\n",
      "252 2 dataset_id cmems_obs-sst_atl_phy_l3s_pmw_P1D-m ODYSSEA North-East Atlantic Sea Surface Temperature Gridded Level 3S Daily Polar orbiting MicroWave (PMW) Multi-Sensor Observations\n",
      "252 3 dataset_id cmems_obs-sst_atl_phy_nrt_l3s_P1D-m ODYSSEA North-East Atlantic Sea Surface Temperature Gridded Level 3S Daily Multi-Sensor Observations\n",
      "253 European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA L4 Sea Surface Temperature Analysis SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025\n",
      "253 0 dataset_id IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE ODYSSEA North-East Atlantic Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations\n",
      "254 European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed SST_ATL_SST_L4_REP_OBSERVATIONS_010_026\n",
      "254 0 dataset_id cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed\n",
      "255 Baltic Sea - L3S Sea Surface Temperature Reprocessed SST_BAL_PHY_L3S_MY_010_040\n",
      "255 0 dataset_id cmems_obs-sst_bal_phy_my_l3s_P1D-m North Sea/Baltic Sea SST, L3S, (SST_BAL_PHY_L3S_MY_010_040)\n",
      "256 Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis SST_BAL_PHY_SUBSKIN_L4_NRT_010_034\n",
      "256 0 dataset_id cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m North Sea/Baltic Sea SST L4 Diurnal Sub-skin Sea Surface Temperature Analysis (SST_BAL_PHY_SUBSKIN_L4_NRT_010_034, cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m)\n",
      "257 North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032\n",
      "257 0 dataset_id DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE DMI North Sea/Baltic Sea SST, L3S, (SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032)\n",
      "258 Baltic Sea- Sea Surface Temperature Analysis L4 SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b\n",
      "258 0 dataset_id DMI-BALTIC-SST-L4-NRT-OBS_FULL_TIME_SERIE DMI-BALTIC-SST-L4-NRT-OBS_FULL_TIME_SERIE\n",
      "259 Baltic Sea- Sea Surface Temperature Reprocessed SST_BAL_SST_L4_REP_OBSERVATIONS_010_016\n",
      "259 0 dataset_id DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016 Baltic Sea SST analysis, daily reprocessed level 4 analysis\n",
      "260 Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed SST_BS_PHY_L3S_MY_010_041\n",
      "260 0 dataset_id cmems_obs-sst_bs_phy_my_l3s_P1D-m Black Sea SST, L3S, Reprocessed using ESA CCI SST v.2.0, C3S v.2.0, C3S v.2.1 L3C product and PFV53 data, 0.05 deg. daily (SST_BS_PHY_L3S_MY_010_041)\n",
      "261 Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis SST_BS_PHY_SUBSKIN_L4_NRT_010_035\n",
      "261 0 dataset_id cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m Black Sea Diurnal Subskin SST Analysis, L4, 1/16deg hourly\n",
      "262 Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013\n",
      "262 0 dataset_id SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a\n",
      "262 1 dataset_id SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b\n",
      "263 Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis SST_BS_SST_L4_NRT_OBSERVATIONS_010_006\n",
      "263 0 dataset_id SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b Black Sea SST Anomaly, L4, 1/16deg daily (SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b)\n",
      "263 1 dataset_id SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d Black Sea SST Anomaly, L4, 1km daily (SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d)\n",
      "263 2 dataset_id SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2 SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2\n",
      "263 3 dataset_id SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2 SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2\n",
      "264 Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed SST_BS_SST_L4_REP_OBSERVATIONS_010_022\n",
      "264 0 dataset_id cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022 Black Sea SST Analysis L4, Reprocessed using ESA CCI SST v.2.0, C3S v.2.0 and PFV53 data, 0.05 deg. daily (cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022)\n",
      "265 Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations SST_GLO_PHY_L3S_MY_010_039\n",
      "265 0 dataset_id cmems_obs-sst_glo_phy_my_l3s_P1D-m Merged collation of sea surface temperature from multiple satellite sources\n",
      "266 ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations SST_GLO_PHY_L4_NRT_010_043\n",
      "266 0 dataset_id cmems_obs-sst_glo_phy_nrt_l4_P1D-m ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations\n",
      "267 ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010\n",
      "267 0 dataset_id IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE ODYSSEA Global Sea Surface Temperature Gridded Level 3S Daily Multi-Sensor Observations\n",
      "267 1 dataset_id cmems_obs-sst_glo_phy_l3s_gir_P1D-m ODYSSEA Global Sea Surface Temperature Gridded Level 3S Daily Geostationary Infra Red (GIR) Multi-Sensor Observations\n",
      "267 2 dataset_id cmems_obs-sst_glo_phy_l3s_pir_P1D-m ODYSSEA Global Sea Surface Temperature Gridded Level 3S Daily Polar orbiting Infra Red (PIR) Multi-Sensor Observations\n",
      "267 3 dataset_id cmems_obs-sst_glo_phy_l3s_pmw_P1D-m ODYSSEA Global Sea Surface Temperature Gridded Level 3S Daily Polar orbiting MicroWave (PMW) Multi-Sensor Observations\n",
      "268 Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001\n",
      "268 0 dataset_id METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2 Global SST & Sea Ice Analysis, L4 OSTIA, 0.05 deg daily (METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2)\n",
      "269 Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed SST_GLO_SST_L4_REP_OBSERVATIONS_010_011\n",
      "269 0 dataset_id METOFFICE-GLO-SST-L4-REP-OBS-SST Global SST & Sea Ice Analysis, L4 OSTIA, 0.05 deg daily (METOFFICE-GLO-SST-L4-REP-OBS-SST-V2)\n",
      "270 ESA SST CCI and C3S reprocessed sea surface temperature analyses SST_GLO_SST_L4_REP_OBSERVATIONS_010_024\n",
      "270 0 dataset_id C3S-GLO-SST-L4-REP-OBS-SST C3S SST L4 product\n",
      "270 1 dataset_id ESACCI-GLO-SST-L4-REP-OBS-SST ESA SST CCI OSTIA L4 product\n",
      "271 Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed SST_MED_PHY_L3S_MY_010_042\n",
      "271 0 dataset_id cmems_obs-sst_med_phy_my_l3s_P1D-m Mediterranean Sea SST, L3S, Reprocessed using ESA CCI SST v.2.0, C3S v.2.0, C3S v.2.1 L3C product and PFV53 data, 0.05 deg. daily (SST_MED_PHY_L3S_MY_010_042)\n",
      "272 Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis SST_MED_PHY_SUBSKIN_L4_NRT_010_036\n",
      "272 0 dataset_id cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m Mediterranean Sea Diurnal Subskin SST Analysis, L4, 1/16deg hourly\n",
      "273 Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012\n",
      "273 0 dataset_id SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a\n",
      "273 1 dataset_id SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b\n",
      "274 Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis SST_MED_SST_L4_NRT_OBSERVATIONS_010_004\n",
      "274 0 dataset_id SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b Mediterranean SST Anomaly, L4, 1/16deg daily (SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b)\n",
      "274 1 dataset_id SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d Mediterranean SST Anomaly, L4, 1km daily (SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d)\n",
      "274 2 dataset_id SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2 SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2\n",
      "274 3 dataset_id SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2 SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2\n",
      "275 Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed SST_MED_SST_L4_REP_OBSERVATIONS_010_021\n",
      "275 0 dataset_id cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021 Mediterranean Sea SST Analysis L4, Reprocessed using ESA CCI SST v.2.0, C3S v.2.0 and PFV53 data, 0.05 deg. daily (cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021)\n",
      "276 GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM REPROCESSED SATELLITE MEASUREMENTS WAVE_GLO_PHY_SPC_L3_MY_014_006\n",
      "276 0 dataset_id dataset-wav-sar-l3-spc-rep-global-s1a dataset-wav-sar-l3-spc-rep-global-s1a\n",
      "276 1 dataset_id dataset-wav-sar-l3-spc-rep-global-s1b dataset-wav-sar-l3-spc-rep-global-s1b\n",
      "277 GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS WAVE_GLO_PHY_SPC_L4_NRT_014_004\n",
      "277 0 dataset_id cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H\n",
      "278 GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS WAVE_GLO_PHY_SWH_L3_MY_014_005\n",
      "278 0 dataset_id cci_obs-wave_glo_phy-swh_my_al-l3_PT1S SARAL-Altika\n",
      "278 1 dataset_id cci_obs-wave_glo_phy-swh_my_c2-l3_PT1S Cryosat-2\n",
      "278 2 dataset_id cci_obs-wave_glo_phy-swh_my_en-l3_PT1S Envisat\n",
      "278 3 dataset_id cci_obs-wave_glo_phy-swh_my_j1-l3_PT1S Jason-1\n",
      "278 4 dataset_id cci_obs-wave_glo_phy-swh_my_j2-l3_PT1S Jason-2\n",
      "278 5 dataset_id cci_obs-wave_glo_phy-swh_my_j3-l3_PT1S Jason-3\n",
      "278 6 dataset_id cmems_obs-wave_glo_phy-swh_my_cfo-l3_PT1S CFOSAT\n",
      "279 GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS WAVE_GLO_PHY_SWH_L3_NRT_014_001\n",
      "279 0 dataset_id cmems_obs-wave_glo_phy-swh_nrt_al-l3_PT1S Saral/AltiKa\n",
      "279 1 dataset_id cmems_obs-wave_glo_phy-swh_nrt_c2-l3_PT1S Cryosat-2\n",
      "279 2 dataset_id cmems_obs-wave_glo_phy-swh_nrt_cfo-l3_PT1S CFOSAT\n",
      "279 3 dataset_id cmems_obs-wave_glo_phy-swh_nrt_h2b-l3_PT1S HaiYang-2B\n",
      "279 4 dataset_id cmems_obs-wave_glo_phy-swh_nrt_h2c-l3_PT1S HaiYang-2C\n",
      "279 5 dataset_id cmems_obs-wave_glo_phy-swh_nrt_j3-l3_PT1S Jason-3\n",
      "279 6 dataset_id cmems_obs-wave_glo_phy-swh_nrt_s3a-l3_PT1S Sentinel-3A\n",
      "279 7 dataset_id cmems_obs-wave_glo_phy-swh_nrt_s3b-l3_PT1S Sentinel-3B\n",
      "279 8 dataset_id cmems_obs-wave_glo_phy-swh_nrt_s6a-l3_PT1S Sentinel-6A\n",
      "279 9 dataset_id cmems_obs-wave_glo_phy-swh_nrt_swon-l3_PT1S SWOT nadir\n",
      "280 GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS WAVE_GLO_PHY_SWH_L4_MY_014_007\n",
      "280 0 dataset_id cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D Multi-Year merged all satellites Global Ocean Gridded Significant Wave Height L4 product and derived variables\n",
      "281 GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS WAVE_GLO_PHY_SWH_L4_NRT_014_003\n",
      "281 0 dataset_id cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D NRT merged all satellites Global Ocean Gridded Significant Wave Height L4 product and derived variables\n",
      "282 GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS WAVE_GLO_WAV_L3_SPC_NRT_OBSERVATIONS_014_002\n",
      "282 0 dataset_id cmems_obs-wave_glo_phy-spc_nrt_cfo-l3_PT10S cmems_obs-wave_glo_phy-spc_nrt_cfo-l3_PT10S\n",
      "282 1 dataset_id dataset-wav-sar-l3-spc-nrt-global-s1a dataset-wav-sar-l3-spc-nrt-global-s1a\n",
      "282 2 dataset_id dataset-wav-sar-l3-spc-nrt-global-s1b dataset-wav-sar-l3-spc-nrt-global-s1b\n",
      "283 Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model WIND_GLO_PHY_CLIMATE_L4_MY_012_003\n",
      "283 0 dataset_id cmems_obs-wind_glo_phy_my_l4_P1M  Global Ocean - Wind and Stress - Monthly - From Scatterometer and Model\n",
      "284 Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer WIND_GLO_PHY_L3_MY_012_005\n",
      "284 0 dataset_id cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i Global Ocean - Wind - ERS-1 SCAT - 25km daily Ascending V2\n",
      "284 1 dataset_id cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i Global Ocean - Wind - ERS-1 SCAT - 25km daily Descending V2\n",
      "284 2 dataset_id cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i Global Ocean - Wind - ERS-2 SCAT - 25km daily Ascending V2\n",
      "284 3 dataset_id cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i Global Ocean - Wind - ERS-2 SCAT - 25km daily Descending V2\n",
      "284 4 dataset_id cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i Global Ocean - Wind - METOP-A ASCAT - 12km daily Ascending V2\n",
      "284 5 dataset_id cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i Global Ocean - Wind - METOP-A ASCAT - 25km daily Ascending V2\n",
      "284 6 dataset_id cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i Global Ocean - Wind - METOP-A ASCAT - 12km daily Descending V2\n",
      "284 7 dataset_id cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i Global Ocean - Wind - METOP-A ASCAT - 25km daily Descending V2\n",
      "284 8 dataset_id cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i Global Ocean - Wind - METOP-B ASCAT - 12km daily Ascending V2\n",
      "284 9 dataset_id cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i Global Ocean - Wind - METOP-B ASCAT - 25km daily Ascending V2\n",
      "284 10 dataset_id cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i Global Ocean - Wind - METOP-B ASCAT - 12km daily Descending V2\n",
      "284 11 dataset_id cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i Global Ocean - Wind - METOP-B ASCAT - 25km daily Descending V2\n",
      "284 12 dataset_id cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i Global Ocean - Wind - Oceanscat2 OSCAT - 25km daily Ascending V2\n",
      "284 13 dataset_id cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i Global Ocean - Wind - Oceanscat2 OSCAT - 50km daily Ascending V2\n",
      "284 14 dataset_id cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i Global Ocean - Wind - Oceanscat2 OSCAT - 25km daily Descending V2\n",
      "284 15 dataset_id cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i Global Ocean - Wind - Oceanscat2 OSCAT - 50km daily Descending V2\n",
      "284 16 dataset_id cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i Global Ocean - Wind - QuikSCAT SeaWinds - 25km daily Ascending V2\n",
      "284 17 dataset_id cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i Global Ocean - Wind - QuikSCAT SeaWinds - 50km daily Ascending V2\n",
      "284 18 dataset_id cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i Global Ocean - Wind - QuikSCAT SeaWinds - 25km daily Descending V2\n",
      "284 19 dataset_id cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i Global Ocean - Wind - QuikSCAT SeaWinds - 50km daily Descending V2\n",
      "285 Global Ocean Daily Gridded Sea Surface Winds from Scatterometer WIND_GLO_PHY_L3_NRT_012_002\n",
      "285 0 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i Global Ocean - Wind - HY-2B HSCAT - 25km daily Ascending V2\n",
      "285 1 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i Global Ocean - Wind - HY-2B HSCAT - 50km daily Ascending V2\n",
      "285 2 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i Global Ocean - Wind - HY-2B HSCAT - 25km daily Descending V2\n",
      "285 3 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i Global Ocean - Wind - HY-2B HSCAT - 50km daily Descending V2\n",
      "285 4 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i Global Ocean - Wind - HY-2C HSCAT - 25km daily Ascending V2\n",
      "285 5 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i Global Ocean - Wind - HY-2C HSCAT - 50km daily Ascending V2\n",
      "285 6 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i Global Ocean - Wind - HY-2C HSCAT - 25km daily Descending V2\n",
      "285 7 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i Global Ocean - Wind - HY-2C HSCAT - 50km daily Descending V2\n",
      "285 8 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i Global Ocean - Wind - HY-2D HSCAT - 25km daily Ascending V2\n",
      "285 9 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i Global Ocean - Wind - HY-2D HSCAT - 50km daily Ascending V2\n",
      "285 10 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i Global Ocean - Wind - HY-2D HSCAT - 25km daily Descending V2\n",
      "285 11 dataset_id cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i Global Ocean - Wind - HY-2D HSCAT - 50km daily Descending V2\n",
      "285 12 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i Global Ocean - Wind - METOP-A ASCAT - 12km daily Ascending V2\n",
      "285 13 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i Global Ocean - Wind - METOP-A ASCAT - 25km daily Ascending V2\n",
      "285 14 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i Global Ocean - Wind - METOP-A ASCAT - 12km daily Descending V2\n",
      "285 15 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i Global Ocean - Wind - METOP-A ASCAT - 25km daily Descending V2\n",
      "285 16 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i Global Ocean - Wind - METOP-B ASCAT - 12km daily Ascending V2\n",
      "285 17 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i Global Ocean - Wind - METOP-B ASCAT - 25km daily Ascending V2\n",
      "285 18 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i Global Ocean - Wind - METOP-B ASCAT - 12km daily Descending V2\n",
      "285 19 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i Global Ocean - Wind - METOP-B ASCAT - 25km daily Descending V2\n",
      "285 20 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i Global Ocean - Wind - METOP-C ASCAT - 12km daily Ascending V2\n",
      "285 21 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i Global Ocean - Wind - METOP-C ASCAT - 25km daily Ascending V2\n",
      "285 22 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i Global Ocean - Wind - METOP-C ASCAT - 12km daily Descending V2\n",
      "285 23 dataset_id cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i Global Ocean - Wind - METOP-C ASCAT - 25km daily Descending V2\n",
      "285 24 dataset_id cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i Global Ocean - Wind - ScatSat-1 OSCAT - 25km daily Ascending V2\n",
      "285 25 dataset_id cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i Global Ocean - Wind - ScatSat-1 OSCAT - 50km daily Ascending V2\n",
      "285 26 dataset_id cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i Global Ocean - Wind - ScatSat-1 OSCAT - 25km daily Descending V2\n",
      "285 27 dataset_id cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i Global Ocean - Wind - ScatSat-1 OSCAT - 50km daily Descending V2\n",
      "286 Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model WIND_GLO_PHY_L4_MY_012_006\n",
      "286 0 dataset_id cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H\n",
      "286 1 dataset_id cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H  Global Ocean - Wind and Stress - Hourly - Reprocessed - From Scatterometer and Model\n",
      "287 Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model WIND_GLO_PHY_L4_NRT_012_004\n",
      "287 0 dataset_id cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H  Global Ocean - Wind and Stress - Hourly - From Scatterometer and Model\n",
      "288 Global Ocean Daily Gridded Sea Surface Winds from Scatterometer WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002\n",
      "288 0 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2B_HSCAT_25_ASC_V2 Global Ocean - Wind - HY-2B HSCAT - 25km daily Ascending V2\n",
      "288 1 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2B_HSCAT_25_DES_V2 Global Ocean - Wind - HY-2B HSCAT - 25km daily Descending V2\n",
      "288 2 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2B_HSCAT_50_ASC_V2 Global Ocean - Wind - HY-2B HSCAT - 50km daily Ascending V2\n",
      "288 3 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2B_HSCAT_50_DES_V2 Global Ocean - Wind - HY-2B HSCAT - 50km daily Descending V2\n",
      "288 4 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2C_HSCAT_25_ASC_V2 Global Ocean - Wind - HY-2C HSCAT - 25km daily Ascending V2\n",
      "288 5 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2C_HSCAT_25_DES_V2 Global Ocean - Wind - HY-2C HSCAT - 25km daily Descending V2\n",
      "288 6 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2C_HSCAT_50_ASC_V2 Global Ocean - Wind - HY-2C HSCAT - 50km daily Ascending V2\n",
      "288 7 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2C_HSCAT_50_DES_V2 Global Ocean - Wind - HY-2C HSCAT - 50km daily Descending V2\n",
      "288 8 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2D_HSCAT_25_ASC_V2 Global Ocean - Wind - HY-2D HSCAT - 25km daily Ascending V2\n",
      "288 9 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2D_HSCAT_25_DES_V2 Global Ocean - Wind - HY-2D HSCAT - 25km daily Descending V2\n",
      "288 10 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2D_HSCAT_50_ASC_V2 Global Ocean - Wind - HY-2D HSCAT - 50km daily Ascending V2\n",
      "288 11 dataset_id KNMI-GLO-WIND_L3-OBS_HY-2D_HSCAT_50_DES_V2 Global Ocean - Wind - HY-2D HSCAT - 50km daily Descending V2\n",
      "288 12 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-A_ASCAT_12_ASC_V2 Global Ocean - Wind - METOP-A ASCAT - 12km daily Ascending V2\n",
      "288 13 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-A_ASCAT_12_DES_V2 Global Ocean - Wind - METOP-A ASCAT - 12km daily Descending V2\n",
      "288 14 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-A_ASCAT_25_ASC_V2 Global Ocean - Wind - METOP-A ASCAT - 25km daily Ascending V2\n",
      "288 15 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-A_ASCAT_25_DES_V2 Global Ocean - Wind - METOP-A ASCAT - 25km daily Descending V2\n",
      "288 16 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-B_ASCAT_12_ASC_V2 Global Ocean - Wind - METOP-B ASCAT - 12km daily Ascending V2\n",
      "288 17 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-B_ASCAT_12_DES_V2 Global Ocean - Wind - METOP-B ASCAT - 12km daily Descending V2\n",
      "288 18 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-B_ASCAT_25_ASC_V2 Global Ocean - Wind - METOP-B ASCAT - 25km daily Ascending V2\n",
      "288 19 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-B_ASCAT_25_DES_V2 Global Ocean - Wind - METOP-B ASCAT - 25km daily Descending V2\n",
      "288 20 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-C_ASCAT_12_ASC_V2 Global Ocean - Wind - METOP-C ASCAT - 12km daily Ascending V2\n",
      "288 21 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-C_ASCAT_12_DES_V2 Global Ocean - Wind - METOP-C ASCAT - 12km daily Descending V2\n",
      "288 22 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-C_ASCAT_25_ASC_V2 Global Ocean - Wind - METOP-C ASCAT - 25km daily Ascending V2\n",
      "288 23 dataset_id KNMI-GLO-WIND_L3-OBS_METOP-C_ASCAT_25_DES_V2 Global Ocean - Wind - METOP-C ASCAT - 25km daily Descending V2\n",
      "288 24 dataset_id KNMI-GLO-WIND_L3-OBS_SCATSAT-1_OSCAT_25_ASC_V2 Global Ocean - Wind - ScatSat-1 OSCAT - 25km daily Ascending V2\n",
      "288 25 dataset_id KNMI-GLO-WIND_L3-OBS_SCATSAT-1_OSCAT_25_DES_V2 Global Ocean - Wind - ScatSat-1 OSCAT - 25km daily Descending V2\n",
      "288 26 dataset_id KNMI-GLO-WIND_L3-OBS_SCATSAT-1_OSCAT_50_ASC_V2 Global Ocean - Wind - ScatSat-1 OSCAT - 50km daily Ascending V2\n",
      "288 27 dataset_id KNMI-GLO-WIND_L3-OBS_SCATSAT-1_OSCAT_50_DES_V2 Global Ocean - Wind - ScatSat-1 OSCAT - 50km daily Descending V2\n",
      "289 Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005\n",
      "289 0 dataset_id KNMI-GLO-WIND_L3-REP-OBS_ERS-1_SCAT_25_ASC Global Ocean - Wind - ERS-1 SCAT - 25km daily Ascending V2\n",
      "289 1 dataset_id KNMI-GLO-WIND_L3-REP-OBS_ERS-1_SCAT_25_DES Global Ocean - Wind - ERS-1 SCAT - 25km daily Descending V2\n",
      "289 2 dataset_id KNMI-GLO-WIND_L3-REP-OBS_ERS-2_SCAT_25_ASC Global Ocean - Wind - ERS-2 SCAT - 25km daily Ascending V2\n",
      "289 3 dataset_id KNMI-GLO-WIND_L3-REP-OBS_ERS-2_SCAT_25_DES Global Ocean - Wind - ERS-2 SCAT - 25km daily Descending V2\n",
      "289 4 dataset_id KNMI-GLO-WIND_L3-REP-OBS_METOP-A_ASCAT_12_ASC Global Ocean - Wind - METOP-A ASCAT - 12km daily Ascending V2\n",
      "289 5 dataset_id KNMI-GLO-WIND_L3-REP-OBS_METOP-A_ASCAT_12_DES Global Ocean - Wind - METOP-A ASCAT - 12km daily Descending V2\n",
      "289 6 dataset_id KNMI-GLO-WIND_L3-REP-OBS_METOP-A_ASCAT_25_ASC Global Ocean - Wind - METOP-A ASCAT - 25km daily Ascending V2\n",
      "289 7 dataset_id KNMI-GLO-WIND_L3-REP-OBS_METOP-A_ASCAT_25_DES Global Ocean - Wind - METOP-A ASCAT - 25km daily Descending V2\n",
      "289 8 dataset_id KNMI-GLO-WIND_L3-REP-OBS_METOP-B_ASCAT_12_ASC Global Ocean - Wind - METOP-B ASCAT - 12km daily Ascending V2\n",
      "289 9 dataset_id KNMI-GLO-WIND_L3-REP-OBS_METOP-B_ASCAT_12_DES Global Ocean - Wind - METOP-B ASCAT - 12km daily Descending V2\n",
      "289 10 dataset_id KNMI-GLO-WIND_L3-REP-OBS_METOP-B_ASCAT_25_ASC Global Ocean - Wind - METOP-B ASCAT - 25km daily Ascending V2\n",
      "289 11 dataset_id KNMI-GLO-WIND_L3-REP-OBS_METOP-B_ASCAT_25_DES Global Ocean - Wind - METOP-B ASCAT - 25km daily Descending V2\n",
      "289 12 dataset_id KNMI-GLO-WIND_L3-REP-OBS_OCEANSAT2_OSCAT_25_ASC Global Ocean - Wind - Oceanscat2 OSCAT - 25km daily Ascending V2\n",
      "289 13 dataset_id KNMI-GLO-WIND_L3-REP-OBS_OCEANSAT2_OSCAT_25_DES Global Ocean - Wind - Oceanscat2 OSCAT - 25km daily Descending V2\n",
      "289 14 dataset_id KNMI-GLO-WIND_L3-REP-OBS_OCEANSAT2_OSCAT_50_ASC Global Ocean - Wind - Oceanscat2 OSCAT - 50km daily Ascending V2\n",
      "289 15 dataset_id KNMI-GLO-WIND_L3-REP-OBS_OCEANSAT2_OSCAT_50_DES Global Ocean - Wind - Oceanscat2 OSCAT - 50km daily Descending V2\n",
      "289 16 dataset_id KNMI-GLO-WIND_L3-REP-OBS_QUIKSCAT_SEAWINDS_25_ASC Global Ocean - Wind - QuikSCAT SeaWinds - 25km daily Ascending V2\n",
      "289 17 dataset_id KNMI-GLO-WIND_L3-REP-OBS_QUIKSCAT_SEAWINDS_25_DES Global Ocean - Wind - QuikSCAT SeaWinds - 25km daily Descending V2\n",
      "289 18 dataset_id KNMI-GLO-WIND_L3-REP-OBS_QUIKSCAT_SEAWINDS_50_ASC Global Ocean - Wind - QuikSCAT SeaWinds - 50km daily Ascending V2\n",
      "289 19 dataset_id KNMI-GLO-WIND_L3-REP-OBS_QUIKSCAT_SEAWINDS_50_DES Global Ocean - Wind - QuikSCAT SeaWinds - 50km daily Descending V2\n"
     ]
    }
   ],
   "source": [
    "catalogue = copernicusmarine.describe(\n",
    "    include_datasets=True,\n",
    "#    contains = ['European North West Shelf - Ocean Physics '],\n",
    "#     contains = ['Atlantic-Iberian Biscay Irish- Ocean Physics']   \n",
    "#    contains = ['mld']\n",
    ")\n",
    "for index, product in enumerate(catalogue['products']):\n",
    "    print(index, product['title'], product['product_id'])\n",
    "    \n",
    "    for index2, dataset in enumerate(product['datasets']):\n",
    "       # if 'dataset_id' in dataset and 'H' in dataset['dataset_id']:        \n",
    "        print(index,index2, 'dataset_id',dataset['dataset_id'], dataset['dataset_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4a050e2-45cd-4cd0-a959-a8e9049a2718",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching catalog: 100%|██████████| 4/4 [00:11<00:00,  2.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO - 2024-03-06T09:09:17Z - Dataset version was not specified, the latest one was selected: \"202309\"\n",
      "INFO - 2024-03-06T09:09:17Z - Dataset part was not specified, the first one was selected: \"default\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO - 2024-03-06T09:09:20Z - Service was not specified, the default one was selected: \"arco-geo-series\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 46GB\n",
       "Dimensions:    (time: 22104, latitude: 551, longitude: 936)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "  * time       (time) datetime64[ns] 177kB 2021-09-01 ... 2024-03-09T23:00:00\n",
       "Data variables:\n",
       "    zos        (time, latitude, longitude) float32 46GB ...\n",
       "Attributes: (12/13)\n",
       "    Conventions:     CF-1.8\n",
       "    comment:         \n",
       "    contact:         https://marine.copernicus.eu/contact\n",
       "    domain_name:     NWS36\n",
       "    field_date:      20230711\n",
       "    field_type:      mean\n",
       "    ...              ...\n",
       "    forecast_type:   analysis\n",
       "    institution:     Nologin (Spain)\n",
       "    licence:         https://marine.copernicus.eu/user-corner/service-commitm...\n",
       "    references:      http://marine.copernicus.eu/\n",
       "    source:          NEMO3.6\n",
       "    title:           Ocean surface hourly mean fields for the North West Shel...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9cda5951-f44c-4fd2-ace6-2aa6279ba314' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9cda5951-f44c-4fd2-ace6-2aa6279ba314' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 22104</li><li><span class='xr-has-index'>latitude</span>: 551</li><li><span class='xr-has-index'>longitude</span>: 936</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4e77a668-abbc-4a6b-931e-21e73e980819' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e77a668-abbc-4a6b-931e-21e73e980819' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>46.0 46.03 46.06 ... 61.25 61.28</div><input id='attrs-578bf512-3034-4967-aa4d-24b7bb376fb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-578bf512-3034-4967-aa4d-24b7bb376fb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d698c4a9-2a3b-4855-b82c-685f844f15c4' class='xr-var-data-in' type='checkbox'><label for='data-d698c4a9-2a3b-4855-b82c-685f844f15c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>step :</span></dt><dd>0.02777863f</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>46.003643</dd><dt><span>valid_max :</span></dt><dd>61.281887</dd></dl></div><div class='xr-var-data'><pre>array([46.003643, 46.03142 , 46.0592  , ..., 61.22633 , 61.25411 , 61.281887],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-16.0 -15.97 -15.94 ... 9.949 9.977</div><input id='attrs-4b7335cd-402a-4e15-b8e9-2de23154ab74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b7335cd-402a-4e15-b8e9-2de23154ab74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ff13829-0528-4fde-b2de-30fbdc276170' class='xr-var-data-in' type='checkbox'><label for='data-2ff13829-0528-4fde-b2de-30fbdc276170' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>step :</span></dt><dd>0.02777863f</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-15.996015</dd><dt><span>valid_max :</span></dt><dd>9.977004</dd></dl></div><div class='xr-var-data'><pre>array([-15.996015, -15.968236, -15.940457, ...,   9.921447,   9.949225,\n",
       "         9.977004], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-09-01 ... 2024-03-09T23:00:00</div><input id='attrs-2c406ed9-3966-4149-bb84-75181e9223f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c406ed9-3966-4149-bb84-75181e9223f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6ab7b73-63c9-452c-b4d4-93cabd2c62c7' class='xr-var-data-in' type='checkbox'><label for='data-c6ab7b73-63c9-452c-b4d4-93cabd2c62c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>2021-09-01T00:00:00.000000000</dd><dt><span>valid_max :</span></dt><dd>2024-03-09T23:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-09-01T00:00:00.000000000&#x27;, &#x27;2021-09-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-01T02:00:00.000000000&#x27;, ..., &#x27;2024-03-09T21:00:00.000000000&#x27;,\n",
       "       &#x27;2024-03-09T22:00:00.000000000&#x27;, &#x27;2024-03-09T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5196db17-8e68-4456-9ba3-baa92c757135' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5196db17-8e68-4456-9ba3-baa92c757135' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>zos</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-17749538-26e8-4f4c-bd5c-cc04186ef465' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17749538-26e8-4f4c-bd5c-cc04186ef465' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f98359d3-b302-45dc-9214-bf08f65ca5de' class='xr-var-data-in' type='checkbox'><label for='data-f98359d3-b302-45dc-9214-bf08f65ca5de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>latitude_max :</span></dt><dd>61.2819f</dd><dt><span>latitude_min :</span></dt><dd>46.0036f</dd><dt><span>long_name :</span></dt><dd>Sea surface height</dd><dt><span>longitude_max :</span></dt><dd>9.977f</dd><dt><span>longitude_min :</span></dt><dd>-15.996f</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>-10000</dd><dt><span>z_max :</span></dt><dd>0.494025f</dd><dt><span>z_min :</span></dt><dd>0.494025f</dd></dl></div><div class='xr-var-data'><pre>[11399828544 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3ef118a-fb62-4a22-b24f-6bc37eb1fca0' class='xr-section-summary-in' type='checkbox'  ><label for='section-e3ef118a-fb62-4a22-b24f-6bc37eb1fca0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-29e11fd0-2557-41f0-b9b0-933033309bc4' class='xr-index-data-in' type='checkbox'/><label for='index-29e11fd0-2557-41f0-b9b0-933033309bc4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 46.00364303588867,  46.03142166137695, 46.059200286865234,\n",
       "       46.086978912353516,   46.1147575378418,  46.14253616333008,\n",
       "        46.17031478881836,  46.19809341430664,  46.22587203979492,\n",
       "         46.2536506652832,\n",
       "       ...\n",
       "        61.03187942504883,  61.05965805053711,  61.08743667602539,\n",
       "        61.11521530151367,  61.14299392700195, 61.170772552490234,\n",
       "       61.198551177978516,   61.2263298034668,  61.25410842895508,\n",
       "        61.28188705444336],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=551))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-399ddd4d-3a7b-48d8-b9eb-e4f87dbf730b' class='xr-index-data-in' type='checkbox'/><label for='index-399ddd4d-3a7b-48d8-b9eb-e4f87dbf730b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-15.996014595031738, -15.968235969543457, -15.940457344055176,\n",
       "       -15.912678718566895, -15.884900093078613, -15.857121467590332,\n",
       "        -15.82934284210205,  -15.80156421661377, -15.773785591125488,\n",
       "       -15.746006965637207,\n",
       "       ...\n",
       "         9.726996421813965,   9.754775047302246,   9.782553672790527,\n",
       "         9.810332298278809,    9.83811092376709,   9.865889549255371,\n",
       "         9.893668174743652,   9.921446800231934,   9.949225425720215,\n",
       "         9.977004051208496],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=936))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e10fb5ca-e5a2-4949-9d2f-90147852b160' class='xr-index-data-in' type='checkbox'/><label for='index-e10fb5ca-e5a2-4949-9d2f-90147852b160' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2021-09-01 00:00:00&#x27;, &#x27;2021-09-01 01:00:00&#x27;,\n",
       "               &#x27;2021-09-01 02:00:00&#x27;, &#x27;2021-09-01 03:00:00&#x27;,\n",
       "               &#x27;2021-09-01 04:00:00&#x27;, &#x27;2021-09-01 05:00:00&#x27;,\n",
       "               &#x27;2021-09-01 06:00:00&#x27;, &#x27;2021-09-01 07:00:00&#x27;,\n",
       "               &#x27;2021-09-01 08:00:00&#x27;, &#x27;2021-09-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-03-09 14:00:00&#x27;, &#x27;2024-03-09 15:00:00&#x27;,\n",
       "               &#x27;2024-03-09 16:00:00&#x27;, &#x27;2024-03-09 17:00:00&#x27;,\n",
       "               &#x27;2024-03-09 18:00:00&#x27;, &#x27;2024-03-09 19:00:00&#x27;,\n",
       "               &#x27;2024-03-09 20:00:00&#x27;, &#x27;2024-03-09 21:00:00&#x27;,\n",
       "               &#x27;2024-03-09 22:00:00&#x27;, &#x27;2024-03-09 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=22104, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-205a2fbd-4f12-4d26-8ff6-0a8d5bbfdf92' class='xr-section-summary-in' type='checkbox'  ><label for='section-205a2fbd-4f12-4d26-8ff6-0a8d5bbfdf92' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8</dd><dt><span>comment :</span></dt><dd></dd><dt><span>contact :</span></dt><dd>https://marine.copernicus.eu/contact</dd><dt><span>domain_name :</span></dt><dd>NWS36</dd><dt><span>field_date :</span></dt><dd>20230711</dd><dt><span>field_type :</span></dt><dd>mean</dd><dt><span>forecast_range :</span></dt><dd>07-day_analysis</dd><dt><span>forecast_type :</span></dt><dd>analysis</dd><dt><span>institution :</span></dt><dd>Nologin (Spain)</dd><dt><span>licence :</span></dt><dd>https://marine.copernicus.eu/user-corner/service-commitments-and-licence</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu/</dd><dt><span>source :</span></dt><dd>NEMO3.6</dd><dt><span>title :</span></dt><dd>Ocean surface hourly mean fields for the North West Shelf (NWS) region</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 46GB\n",
       "Dimensions:    (time: 22104, latitude: 551, longitude: 936)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "  * time       (time) datetime64[ns] 177kB 2021-09-01 ... 2024-03-09T23:00:00\n",
       "Data variables:\n",
       "    zos        (time, latitude, longitude) float32 46GB ...\n",
       "Attributes: (12/13)\n",
       "    Conventions:     CF-1.8\n",
       "    comment:         \n",
       "    contact:         https://marine.copernicus.eu/contact\n",
       "    domain_name:     NWS36\n",
       "    field_date:      20230711\n",
       "    field_type:      mean\n",
       "    ...              ...\n",
       "    forecast_type:   analysis\n",
       "    institution:     Nologin (Spain)\n",
       "    licence:         https://marine.copernicus.eu/user-corner/service-commitm...\n",
       "    references:      http://marine.copernicus.eu/\n",
       "    source:          NEMO3.6\n",
       "    title:           Ocean surface hourly mean fields for the North West Shel..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import copernicusmarine as copernicusmarine\n",
    "#mdt=copernicusmarine.open_dataset(dataset_id='cmems_mod_nws_phy_anfc_0.027deg-3D_static',no_metadata_cache=True )#,\n",
    "#thetao=copernicusmarine.open_dataset(dataset_id='cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m',variables=['thetao'],no_metadata_cache=True )#,\n",
    "zos=copernicusmarine.open_dataset(dataset_id='cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m',variables=['zos'],no_metadata_cache=True )# \n",
    "zos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa1ca200-a490-4c83-8d96-08627929685e",
   "metadata": {},
   "outputs": [],
   "source": [
    "0 0 dataset_id cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i Ocean surface 15-minutes instantaneous fields for the North West Shelf (NWS) region\n",
    "0 1 dataset_id cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m Ocean surface hourly mean fields for the North West Shelf (NWS) region\n",
    "0 4 dataset_id cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m Ocean 3D hourly mean fields for the North West Shelf (NWS) region\n",
    "0 5 dataset_id cmems_mod_nws_phy_anfc_0.027deg-3D_static cmems_mod_nws_phy_anfc_0.027deg-3D_static"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b693e15-5be6-417d-a101-3894f6fcd2de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching catalog: 100%|██████████| 4/4 [00:10<00:00,  2.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO - 2024-03-05T06:05:30Z - Dataset version was not specified, the latest one was selected: \"202309\"\n",
      "INFO - 2024-03-05T06:05:30Z - Dataset part was not specified, the first one was selected: \"default\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO - 2024-03-05T06:05:31Z - Service was not specified, the default one was selected: \"arco-geo-series\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (depth: 50, latitude: 551, longitude: 936, time: 6024)\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * latitude   (latitude) float32 46.0 46.03 46.06 46.09 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "  * time       (time) datetime64[ns] 2023-07-02 ... 2024-03-08T23:00:00\n",
       "Data variables:\n",
       "    so         (time, depth, latitude, longitude) float32 ...\n",
       "    thetao     (time, depth, latitude, longitude) float32 ...\n",
       "    uo         (time, depth, latitude, longitude) float32 ...\n",
       "    vo         (time, depth, latitude, longitude) float32 ...\n",
       "Attributes: (12/13)\n",
       "    Conventions:     CF-1.8\n",
       "    comment:         \n",
       "    contact:         https://marine.copernicus.eu/contact\n",
       "    domain_name:     NWS36\n",
       "    field_date:      20231027\n",
       "    field_type:      mean\n",
       "    ...              ...\n",
       "    forecast_type:   forecast\n",
       "    institution:     Nologin (Spain)\n",
       "    licence:         https://marine.copernicus.eu/user-corner/service-commitm...\n",
       "    references:      http://marine.copernicus.eu/\n",
       "    source:          NEMO3.6\n",
       "    title:           Ocean 3D hourly mean fields for the North West Shelf (NW...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-21b89c9e-e022-46bf-8af6-42d7fd0dd47a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-21b89c9e-e022-46bf-8af6-42d7fd0dd47a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>depth</span>: 50</li><li><span class='xr-has-index'>latitude</span>: 551</li><li><span class='xr-has-index'>longitude</span>: 936</li><li><span class='xr-has-index'>time</span>: 6024</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-609317d5-b7fa-4b11-a663-26b6bddae6a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-609317d5-b7fa-4b11-a663-26b6bddae6a8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.494 1.541 ... 5.275e+03 5.728e+03</div><input id='attrs-9beb7b13-b2e8-4dc2-be97-66c3adc096d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9beb7b13-b2e8-4dc2-be97-66c3adc096d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-047b5a1a-1259-4ee0-abc6-e7d625b3db94' class='xr-var-data-in' type='checkbox'><label for='data-047b5a1a-1259-4ee0-abc6-e7d625b3db94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0.49402538</dd><dt><span>valid_max :</span></dt><dd>5727.9165</dd></dl></div><div class='xr-var-data'><pre>array([4.940254e-01, 1.541375e+00, 2.645669e+00, 3.819495e+00, 5.078224e+00,\n",
       "       6.440614e+00, 7.929560e+00, 9.572997e+00, 1.140500e+01, 1.346714e+01,\n",
       "       1.581007e+01, 1.849556e+01, 2.159882e+01, 2.521141e+01, 2.944473e+01,\n",
       "       3.443415e+01, 4.034405e+01, 4.737369e+01, 5.576429e+01, 6.580727e+01,\n",
       "       7.785385e+01, 9.232607e+01, 1.097293e+02, 1.306660e+02, 1.558507e+02,\n",
       "       1.861256e+02, 2.224752e+02, 2.660403e+02, 3.181274e+02, 3.802130e+02,\n",
       "       4.539377e+02, 5.410889e+02, 6.435668e+02, 7.633331e+02, 9.023393e+02,\n",
       "       1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,\n",
       "       2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,\n",
       "       3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03, 5.727917e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>46.0 46.03 46.06 ... 61.25 61.28</div><input id='attrs-0a92e443-5ad8-41ba-85f7-049562ca6db6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a92e443-5ad8-41ba-85f7-049562ca6db6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26946d42-8e50-4fda-bd79-623dcb9893c5' class='xr-var-data-in' type='checkbox'><label for='data-26946d42-8e50-4fda-bd79-623dcb9893c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>step :</span></dt><dd>0.02777863f</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>46.003643</dd><dt><span>valid_max :</span></dt><dd>61.281887</dd></dl></div><div class='xr-var-data'><pre>array([46.003643, 46.03142 , 46.0592  , ..., 61.22633 , 61.25411 , 61.281887],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-16.0 -15.97 -15.94 ... 9.949 9.977</div><input id='attrs-42ebd756-e12a-4d8d-a9d9-c613452f0b88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42ebd756-e12a-4d8d-a9d9-c613452f0b88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4138694-c8de-4404-94b3-84b9bd21dc9c' class='xr-var-data-in' type='checkbox'><label for='data-f4138694-c8de-4404-94b3-84b9bd21dc9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>step :</span></dt><dd>0.02777863f</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-15.996015</dd><dt><span>valid_max :</span></dt><dd>9.977004</dd></dl></div><div class='xr-var-data'><pre>array([-15.996015, -15.968236, -15.940457, ...,   9.921447,   9.949225,\n",
       "         9.977004], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-07-02 ... 2024-03-08T23:00:00</div><input id='attrs-564cc6fb-fa1d-433e-b15d-4fc15ba42ab1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-564cc6fb-fa1d-433e-b15d-4fc15ba42ab1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2ad3153-73d6-4721-904a-1c74e8dc04c7' class='xr-var-data-in' type='checkbox'><label for='data-f2ad3153-73d6-4721-904a-1c74e8dc04c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>2023-07-02T00:00:00.000000000</dd><dt><span>valid_max :</span></dt><dd>2024-03-08T23:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-07-02T00:00:00.000000000&#x27;, &#x27;2023-07-02T01:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-02T02:00:00.000000000&#x27;, ..., &#x27;2024-03-08T21:00:00.000000000&#x27;,\n",
       "       &#x27;2024-03-08T22:00:00.000000000&#x27;, &#x27;2024-03-08T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35a42273-eba3-494d-873b-eb94b182f2e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35a42273-eba3-494d-873b-eb94b182f2e4' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>so</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3c73433-d64b-4b15-8753-4180dcc5e01f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3c73433-d64b-4b15-8753-4180dcc5e01f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c91b5b7-c29a-4caf-b4f5-b9659f54e280' class='xr-var-data-in' type='checkbox'><label for='data-9c91b5b7-c29a-4caf-b4f5-b9659f54e280' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>latitude_max :</span></dt><dd>61.2819f</dd><dt><span>latitude_min :</span></dt><dd>46.0036f</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>longitude_max :</span></dt><dd>9.977f</dd><dt><span>longitude_min :</span></dt><dd>-15.996f</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_max :</span></dt><dd>20000</dd><dt><span>valid_min :</span></dt><dd>-20000</dd><dt><span>z_max :</span></dt><dd>5727.917f</dd><dt><span>z_min :</span></dt><dd>0.49402f</dd></dl></div><div class='xr-var-data'><pre>[155339683200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd6a33fb-325c-49cd-807c-e190b25a79ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd6a33fb-325c-49cd-807c-e190b25a79ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8b7c27d-52f5-4a92-a1df-24667888ec3d' class='xr-var-data-in' type='checkbox'><label for='data-f8b7c27d-52f5-4a92-a1df-24667888ec3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>latitude_max :</span></dt><dd>61.2819f</dd><dt><span>latitude_min :</span></dt><dd>46.0036f</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>longitude_max :</span></dt><dd>9.977f</dd><dt><span>longitude_min :</span></dt><dd>-15.996f</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>degrees_C</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_max :</span></dt><dd>22000</dd><dt><span>valid_min :</span></dt><dd>-12000</dd><dt><span>z_max :</span></dt><dd>5727.917f</dd><dt><span>z_min :</span></dt><dd>0.49402f</dd></dl></div><div class='xr-var-data'><pre>[155339683200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-200057be-a457-47dd-a629-3e79e5b6b160' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-200057be-a457-47dd-a629-3e79e5b6b160' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c34550fb-a1b2-433d-8b71-fcaaa2741beb' class='xr-var-data-in' type='checkbox'><label for='data-c34550fb-a1b2-433d-8b71-fcaaa2741beb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>latitude_max :</span></dt><dd>61.2819f</dd><dt><span>latitude_min :</span></dt><dd>46.0036f</dd><dt><span>long_name :</span></dt><dd>Eastward velocity</dd><dt><span>longitude_max :</span></dt><dd>9.977f</dd><dt><span>longitude_min :</span></dt><dd>-15.996f</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>9000</dd><dt><span>valid_min :</span></dt><dd>-9000</dd><dt><span>z_max :</span></dt><dd>5727.917f</dd><dt><span>z_min :</span></dt><dd>0.49402f</dd></dl></div><div class='xr-var-data'><pre>[155339683200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5208fd1-f4af-4a96-ae8e-7e2cff03b15b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5208fd1-f4af-4a96-ae8e-7e2cff03b15b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5a7cb9-4054-483b-a521-fb1cb4235574' class='xr-var-data-in' type='checkbox'><label for='data-aa5a7cb9-4054-483b-a521-fb1cb4235574' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>latitude_max :</span></dt><dd>61.2819f</dd><dt><span>latitude_min :</span></dt><dd>46.0036f</dd><dt><span>long_name :</span></dt><dd>Northward velocity</dd><dt><span>longitude_max :</span></dt><dd>9.977f</dd><dt><span>longitude_min :</span></dt><dd>-15.996f</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>9000</dd><dt><span>valid_min :</span></dt><dd>-9000</dd><dt><span>z_max :</span></dt><dd>5727.917f</dd><dt><span>z_min :</span></dt><dd>0.49402f</dd></dl></div><div class='xr-var-data'><pre>[155339683200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb1a7388-9fc9-4f11-8b12-6cfef560337d' class='xr-section-summary-in' type='checkbox'  ><label for='section-cb1a7388-9fc9-4f11-8b12-6cfef560337d' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-470808b4-efd8-4214-9d69-d164ef16ece6' class='xr-index-data-in' type='checkbox'/><label for='index-470808b4-efd8-4214-9d69-d164ef16ece6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.4940253794193268, 1.5413753986358643, 2.6456685066223145,\n",
       "       3.8194947242736816,  5.078223705291748,  6.440614223480225,\n",
       "         7.92956018447876,  9.572997093200684,  11.40500259399414,\n",
       "       13.467138290405273, 15.810072898864746, 18.495559692382812,\n",
       "        21.59881591796875, 25.211408615112305,  29.44472885131836,\n",
       "        34.43415451049805, 40.344051361083984, 47.373687744140625,\n",
       "        55.76428985595703,   65.8072738647461,  77.85385131835938,\n",
       "         92.3260726928711, 109.72927856445312, 130.66598510742188,\n",
       "       155.85072326660156,  186.1255645751953,  222.4751739501953,\n",
       "        266.0402526855469,    318.12744140625,  380.2130126953125,\n",
       "         453.937744140625,  541.0889282226562,  643.5668334960938,\n",
       "        763.3330688476562,  902.3392944335938,  1062.439697265625,\n",
       "       1245.2911376953125,    1452.2509765625,  1684.284423828125,\n",
       "       1941.8934326171875,  2225.077880859375,  2533.336181640625,\n",
       "         2865.70263671875,       3220.8203125,  3597.031982421875,\n",
       "         3992.48388671875,   4405.22412109375,   4833.29052734375,\n",
       "          5274.7841796875,   5727.91650390625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-54626410-4420-4ade-ac62-8534159cc2b8' class='xr-index-data-in' type='checkbox'/><label for='index-54626410-4420-4ade-ac62-8534159cc2b8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 46.00364303588867,  46.03142166137695, 46.059200286865234,\n",
       "       46.086978912353516,   46.1147575378418,  46.14253616333008,\n",
       "        46.17031478881836,  46.19809341430664,  46.22587203979492,\n",
       "         46.2536506652832,\n",
       "       ...\n",
       "        61.03187942504883,  61.05965805053711,  61.08743667602539,\n",
       "        61.11521530151367,  61.14299392700195, 61.170772552490234,\n",
       "       61.198551177978516,   61.2263298034668,  61.25410842895508,\n",
       "        61.28188705444336],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=551))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b932b2d5-86c5-4b2b-8ae7-46dcb925d6cc' class='xr-index-data-in' type='checkbox'/><label for='index-b932b2d5-86c5-4b2b-8ae7-46dcb925d6cc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-15.996014595031738, -15.968235969543457, -15.940457344055176,\n",
       "       -15.912678718566895, -15.884900093078613, -15.857121467590332,\n",
       "        -15.82934284210205,  -15.80156421661377, -15.773785591125488,\n",
       "       -15.746006965637207,\n",
       "       ...\n",
       "         9.726996421813965,   9.754775047302246,   9.782553672790527,\n",
       "         9.810332298278809,    9.83811092376709,   9.865889549255371,\n",
       "         9.893668174743652,   9.921446800231934,   9.949225425720215,\n",
       "         9.977004051208496],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=936))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e4fbcd0b-3684-479f-93b6-3f4671153dcf' class='xr-index-data-in' type='checkbox'/><label for='index-e4fbcd0b-3684-479f-93b6-3f4671153dcf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-07-02 00:00:00&#x27;, &#x27;2023-07-02 01:00:00&#x27;,\n",
       "               &#x27;2023-07-02 02:00:00&#x27;, &#x27;2023-07-02 03:00:00&#x27;,\n",
       "               &#x27;2023-07-02 04:00:00&#x27;, &#x27;2023-07-02 05:00:00&#x27;,\n",
       "               &#x27;2023-07-02 06:00:00&#x27;, &#x27;2023-07-02 07:00:00&#x27;,\n",
       "               &#x27;2023-07-02 08:00:00&#x27;, &#x27;2023-07-02 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-03-08 14:00:00&#x27;, &#x27;2024-03-08 15:00:00&#x27;,\n",
       "               &#x27;2024-03-08 16:00:00&#x27;, &#x27;2024-03-08 17:00:00&#x27;,\n",
       "               &#x27;2024-03-08 18:00:00&#x27;, &#x27;2024-03-08 19:00:00&#x27;,\n",
       "               &#x27;2024-03-08 20:00:00&#x27;, &#x27;2024-03-08 21:00:00&#x27;,\n",
       "               &#x27;2024-03-08 22:00:00&#x27;, &#x27;2024-03-08 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6024, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3e19f01-564b-47d6-aae5-deae2c4b0d69' class='xr-section-summary-in' type='checkbox'  ><label for='section-b3e19f01-564b-47d6-aae5-deae2c4b0d69' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8</dd><dt><span>comment :</span></dt><dd></dd><dt><span>contact :</span></dt><dd>https://marine.copernicus.eu/contact</dd><dt><span>domain_name :</span></dt><dd>NWS36</dd><dt><span>field_date :</span></dt><dd>20231027</dd><dt><span>field_type :</span></dt><dd>mean</dd><dt><span>forecast_range :</span></dt><dd>05-day_forecast</dd><dt><span>forecast_type :</span></dt><dd>forecast</dd><dt><span>institution :</span></dt><dd>Nologin (Spain)</dd><dt><span>licence :</span></dt><dd>https://marine.copernicus.eu/user-corner/service-commitments-and-licence</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu/</dd><dt><span>source :</span></dt><dd>NEMO3.6</dd><dt><span>title :</span></dt><dd>Ocean 3D hourly mean fields for the North West Shelf (NWS) region</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (depth: 50, latitude: 551, longitude: 936, time: 6024)\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * latitude   (latitude) float32 46.0 46.03 46.06 46.09 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "  * time       (time) datetime64[ns] 2023-07-02 ... 2024-03-08T23:00:00\n",
       "Data variables:\n",
       "    so         (time, depth, latitude, longitude) float32 ...\n",
       "    thetao     (time, depth, latitude, longitude) float32 ...\n",
       "    uo         (time, depth, latitude, longitude) float32 ...\n",
       "    vo         (time, depth, latitude, longitude) float32 ...\n",
       "Attributes: (12/13)\n",
       "    Conventions:     CF-1.8\n",
       "    comment:         \n",
       "    contact:         https://marine.copernicus.eu/contact\n",
       "    domain_name:     NWS36\n",
       "    field_date:      20231027\n",
       "    field_type:      mean\n",
       "    ...              ...\n",
       "    forecast_type:   forecast\n",
       "    institution:     Nologin (Spain)\n",
       "    licence:         https://marine.copernicus.eu/user-corner/service-commitm...\n",
       "    references:      http://marine.copernicus.eu/\n",
       "    source:          NEMO3.6\n",
       "    title:           Ocean 3D hourly mean fields for the North West Shelf (NW..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copernicusmarine.open_dataset(dataset_id='cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m',no_metadata_cache=True )# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5801963b-ab63-4561-bbae-7fce9355f9ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_timestamp = pd.Timestamp('2022-06-16 17:00:00', tz='UTC')\n",
    "end_timestamp = pd.Timestamp('2023-05-22 08:00:00', tz='UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afe16fbf-1d11-4be1-8934-f99218d53945",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, product in enumerate(catalogue['products']):\n",
    "    print(index, product['title'], product['product_id'])\n",
    "    \n",
    "    for index2, dataset in enumerate(product['datasets']):\n",
    "       # if 'dataset_id' in dataset and 'H' in dataset['dataset_id']:        \n",
    "        print(index,index2, 'dataset_id',dataset['dataset_id'], dataset['dataset_name'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bab3d3a8-ffe9-444b-90d1-d4ef28e73bbf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dataset_id': 'cmems_mod_nws_phy_anfc_7km-3D_static',\n",
       " 'dataset_name': 'cmems_mod_nws_phy_anfc_7km-3D_static',\n",
       " 'versions': [{'label': '202112',\n",
       "   'parts': [{'name': 'bathy',\n",
       "     'services': [{'service_type': {'service_name': 'original-files',\n",
       "        'short_name': 'files'},\n",
       "       'uri': 'https://s3.waw3-1.cloudferro.com/mdl-native-13/native/NWSHELF_ANALYSISFORECAST_PHY_LR_004_001/cmems_mod_nws_phy_anfc_7km-3D_static_202112/NWS-MFC_004_001_mask_bathy.nc',\n",
       "       'variables': [{'short_name': 'deptho',\n",
       "         'standard_name': 'sea_floor_depth_below_geoid',\n",
       "         'units': 'm',\n",
       "         'bbox': [-19.88888931274414,\n",
       "          40.06666946411133,\n",
       "          12.999670028686523,\n",
       "          65.00125122070312],\n",
       "         'coordinates': [{'coordinates_id': 'latitude',\n",
       "           'units': '',\n",
       "           'minimum_value': 40.06666946411133,\n",
       "           'maximum_value': 65.00125122070312,\n",
       "           'step': 0.06667000469676951,\n",
       "           'values': None},\n",
       "          {'coordinates_id': 'longitude',\n",
       "           'units': '',\n",
       "           'minimum_value': -19.88888931274414,\n",
       "           'maximum_value': 12.999670028686523,\n",
       "           'step': 0.11110999133135821,\n",
       "           'values': None}]},\n",
       "        {'short_name': 'deptho_lev_interp',\n",
       "         'standard_name': 'model_level_number_at_sea_floor',\n",
       "         'units': '1',\n",
       "         'bbox': [-19.88888931274414,\n",
       "          40.06666946411133,\n",
       "          12.999670028686523,\n",
       "          65.00125122070312],\n",
       "         'coordinates': [{'coordinates_id': 'latitude',\n",
       "           'units': '',\n",
       "           'minimum_value': 40.06666946411133,\n",
       "           'maximum_value': 65.00125122070312,\n",
       "           'step': 0.06667000469676951,\n",
       "           'values': None},\n",
       "          {'coordinates_id': 'longitude',\n",
       "           'units': '',\n",
       "           'minimum_value': -19.88888931274414,\n",
       "           'maximum_value': 12.999670028686523,\n",
       "           'step': 0.11110999133135821,\n",
       "           'values': None}]},\n",
       "        {'short_name': 'mask',\n",
       "         'standard_name': 'sea_binary_mask',\n",
       "         'units': '1',\n",
       "         'bbox': [-19.88888931274414,\n",
       "          40.06666946411133,\n",
       "          12.999670028686523,\n",
       "          65.00125122070312],\n",
       "         'coordinates': [{'coordinates_id': 'depth',\n",
       "           'units': 'm',\n",
       "           'minimum_value': -5000,\n",
       "           'maximum_value': 0,\n",
       "           'step': None,\n",
       "           'values': [-5000,\n",
       "            -4000,\n",
       "            -3000,\n",
       "            -2000,\n",
       "            -1500,\n",
       "            -1000,\n",
       "            -750,\n",
       "            -600,\n",
       "            -500,\n",
       "            -400,\n",
       "            -300,\n",
       "            -250,\n",
       "            -200,\n",
       "            -150,\n",
       "            -125,\n",
       "            -100,\n",
       "            -75,\n",
       "            -50,\n",
       "            -30,\n",
       "            -20,\n",
       "            -15,\n",
       "            -10,\n",
       "            -3,\n",
       "            0]},\n",
       "          {'coordinates_id': 'latitude',\n",
       "           'units': '',\n",
       "           'minimum_value': 40.06666946411133,\n",
       "           'maximum_value': 65.00125122070312,\n",
       "           'step': 0.06667000469676951,\n",
       "           'values': None},\n",
       "          {'coordinates_id': 'longitude',\n",
       "           'units': '',\n",
       "           'minimum_value': -19.88888931274414,\n",
       "           'maximum_value': 12.999670028686523,\n",
       "           'step': 0.11110999133135821,\n",
       "           'values': None}]}]},\n",
       "      {'service_type': {'service_name': 'static-arco',\n",
       "        'short_name': 'static-arco'},\n",
       "       'uri': 'https://s3.waw3-1.cloudferro.com/mdl-arco-time/arco/NWSHELF_ANALYSISFORECAST_PHY_LR_004_001/cmems_mod_nws_phy_anfc_7km-3D_static_202112--ext--bathy/static.zarr',\n",
       "       'variables': [{'short_name': 'deptho',\n",
       "         'standard_name': 'sea_floor_depth_below_geoid',\n",
       "         'units': 'm',\n",
       "         'bbox': [-19.88888931274414,\n",
       "          40.06666946411133,\n",
       "          12.999670028686523,\n",
       "          65.00125122070312],\n",
       "         'coordinates': [{'coordinates_id': 'latitude',\n",
       "           'units': '',\n",
       "           'minimum_value': 40.06666946411133,\n",
       "           'maximum_value': 65.00125122070312,\n",
       "           'step': 0.06667000469676951,\n",
       "           'values': None},\n",
       "          {'coordinates_id': 'longitude',\n",
       "           'units': '',\n",
       "           'minimum_value': -19.88888931274414,\n",
       "           'maximum_value': 12.999670028686523,\n",
       "           'step': 0.11110999133135821,\n",
       "           'values': None}]},\n",
       "        {'short_name': 'deptho_lev_interp',\n",
       "         'standard_name': 'model_level_number_at_sea_floor',\n",
       "         'units': '1',\n",
       "         'bbox': [-19.88888931274414,\n",
       "          40.06666946411133,\n",
       "          12.999670028686523,\n",
       "          65.00125122070312],\n",
       "         'coordinates': [{'coordinates_id': 'latitude',\n",
       "           'units': '',\n",
       "           'minimum_value': 40.06666946411133,\n",
       "           'maximum_value': 65.00125122070312,\n",
       "           'step': 0.06667000469676951,\n",
       "           'values': None},\n",
       "          {'coordinates_id': 'longitude',\n",
       "           'units': '',\n",
       "           'minimum_value': -19.88888931274414,\n",
       "           'maximum_value': 12.999670028686523,\n",
       "           'step': 0.11110999133135821,\n",
       "           'values': None}]},\n",
       "        {'short_name': 'mask',\n",
       "         'standard_name': 'sea_binary_mask',\n",
       "         'units': '1',\n",
       "         'bbox': [-19.88888931274414,\n",
       "          40.06666946411133,\n",
       "          12.999670028686523,\n",
       "          65.00125122070312],\n",
       "         'coordinates': [{'coordinates_id': 'depth',\n",
       "           'units': 'm',\n",
       "           'minimum_value': -5000,\n",
       "           'maximum_value': 0,\n",
       "           'step': None,\n",
       "           'values': [-5000,\n",
       "            -4000,\n",
       "            -3000,\n",
       "            -2000,\n",
       "            -1500,\n",
       "            -1000,\n",
       "            -750,\n",
       "            -600,\n",
       "            -500,\n",
       "            -400,\n",
       "            -300,\n",
       "            -250,\n",
       "            -200,\n",
       "            -150,\n",
       "            -125,\n",
       "            -100,\n",
       "            -75,\n",
       "            -50,\n",
       "            -30,\n",
       "            -20,\n",
       "            -15,\n",
       "            -10,\n",
       "            -3,\n",
       "            0]},\n",
       "          {'coordinates_id': 'latitude',\n",
       "           'units': '',\n",
       "           'minimum_value': 40.06666946411133,\n",
       "           'maximum_value': 65.00125122070312,\n",
       "           'step': 0.06667000469676951,\n",
       "           'values': None},\n",
       "          {'coordinates_id': 'longitude',\n",
       "           'units': '',\n",
       "           'minimum_value': -19.88888931274414,\n",
       "           'maximum_value': 12.999670028686523,\n",
       "           'step': 0.11110999133135821,\n",
       "           'values': None}]}]}]},\n",
       "    {'name': 'mdt',\n",
       "     'services': [{'service_type': {'service_name': 'original-files',\n",
       "        'short_name': 'files'},\n",
       "       'uri': 'https://s3.waw3-1.cloudferro.com/mdl-native-13/native/NWSHELF_ANALYSISFORECAST_PHY_LR_004_001/cmems_mod_nws_phy_anfc_7km-3D_static_202112/NWS-MFC_004_001_mdt.nc',\n",
       "       'variables': [{'short_name': 'mdt',\n",
       "         'standard_name': 'sea_surface_height_above_geoid',\n",
       "         'units': 'm',\n",
       "         'bbox': [-19.88888931274414,\n",
       "          40.06666946411133,\n",
       "          12.999670028686523,\n",
       "          65.00125122070312],\n",
       "         'coordinates': [{'coordinates_id': 'latitude',\n",
       "           'units': '',\n",
       "           'minimum_value': 40.06666946411133,\n",
       "           'maximum_value': 65.00125122070312,\n",
       "           'step': 0.06667000469676951,\n",
       "           'values': None},\n",
       "          {'coordinates_id': 'longitude',\n",
       "           'units': '',\n",
       "           'minimum_value': -19.88888931274414,\n",
       "           'maximum_value': 12.999670028686523,\n",
       "           'step': 0.11110999133135821,\n",
       "           'values': None}]}]},\n",
       "      {'service_type': {'service_name': 'static-arco',\n",
       "        'short_name': 'static-arco'},\n",
       "       'uri': 'https://s3.waw3-1.cloudferro.com/mdl-arco-time/arco/NWSHELF_ANALYSISFORECAST_PHY_LR_004_001/cmems_mod_nws_phy_anfc_7km-3D_static_202112--ext--mdt/static.zarr',\n",
       "       'variables': [{'short_name': 'mdt',\n",
       "         'standard_name': 'sea_surface_height_above_geoid',\n",
       "         'units': 'm',\n",
       "         'bbox': [-19.88888931274414,\n",
       "          40.06666946411133,\n",
       "          12.999670028686523,\n",
       "          65.00125122070312],\n",
       "         'coordinates': [{'coordinates_id': 'latitude',\n",
       "           'units': '',\n",
       "           'minimum_value': 40.06666946411133,\n",
       "           'maximum_value': 65.00125122070312,\n",
       "           'step': 0.06667000469676951,\n",
       "           'values': None},\n",
       "          {'coordinates_id': 'longitude',\n",
       "           'units': '',\n",
       "           'minimum_value': -19.88888931274414,\n",
       "           'maximum_value': 12.999670028686523,\n",
       "           'step': 0.11110999133135821,\n",
       "           'values': None}]}]}]}]}]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e923dd04-29f3-49b0-9be9-5501d2cfdab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO - 2024-02-13T08:58:43Z - Dataset version was not specified, the latest one was selected: \"202309\"\n",
      "INFO - 2024-02-13T08:58:43Z - Dataset part was not specified, the first one was selected: \"bathy\"\n",
      "INFO - 2024-02-13T08:58:43Z - Service was not specified, the default one was selected: \"static-arco\"\n",
      "INFO - 2024-02-13T08:58:45Z - Dataset version was not specified, the latest one was selected: \"202309\"\n",
      "INFO - 2024-02-13T08:58:45Z - Dataset part was not specified, the first one was selected: \"default\"\n",
      "INFO - 2024-02-13T08:58:45Z - Service was not specified, the default one was selected: \"arco-geo-series\"\n"
     ]
    }
   ],
   "source": [
    "NWSHELF_ANALYSISFORECAST_PHY_LR_004_001=xr.merge([\n",
    "    copernicusmarine.open_dataset(dataset_id='cmems_mod_nws_phy_anfc_0.027deg-3D_static'),\n",
    "    copernicusmarine.open_dataset(dataset_id='cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m',variables=['thetao']),\n",
    "#    copernicusmarine.open_dataset(dataset_id='cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m',variables=['zos'])\n",
    "]\n",
    "    )\n",
    "\n",
    "NWSHELF_ANALYSISFORECAST_PHY_004_013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef5fd0bd-829b-4f1c-bbfc-93cee406f2a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO - 2024-02-06T09:56:19Z - Dataset version was not specified, the latest one was selected: \"202112\"\n",
      "INFO - 2024-02-06T09:56:19Z - Dataset part was not specified, the first one was selected: \"mdt\"\n",
      "INFO - 2024-02-06T09:56:19Z - Service was not specified, the default one was selected: \"static-arco\"\n",
      "INFO - 2024-02-06T09:56:20Z - Dataset version was not specified, the latest one was selected: \"202012\"\n",
      "INFO - 2024-02-06T09:56:20Z - Dataset part was not specified, the first one was selected: \"default\"\n",
      "INFO - 2024-02-06T09:56:20Z - Service was not specified, the default one was selected: \"arco-geo-series\"\n",
      "INFO - 2024-02-06T09:56:22Z - Dataset version was not specified, the latest one was selected: \"202012\"\n",
      "INFO - 2024-02-06T09:56:22Z - Dataset part was not specified, the first one was selected: \"default\"\n",
      "INFO - 2024-02-06T09:56:22Z - Service was not specified, the default one was selected: \"arco-geo-series\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (latitude: 1240, longitude: 958, depth: 33, time: 44136)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 46.0 46.01 46.03 46.04 ... 62.72 62.73 62.74\n",
       "  * longitude  (longitude) float32 -16.0 -15.97 -15.94 ... 12.94 12.97 13.0\n",
       "  * depth      (depth) float32 0.0 3.0 5.0 10.0 15.0 ... 2e+03 3e+03 4e+03 5e+03\n",
       "  * time       (time) datetime64[ns] 2019-01-01T01:00:00 ... 2024-01-14\n",
       "Data variables:\n",
       "    mdt        (latitude, longitude) float32 ...\n",
       "    thetao     (time, depth, latitude, longitude) float32 ...\n",
       "    zos        (time, latitude, longitude) float32 ...\n",
       "Attributes: (12/13)\n",
       "    Conventions:          CF-1.7\n",
       "    NCO:                  netCDF Operators version 4.7.5 (Homepage = http://n...\n",
       "    contact:              servicedesk.cmems@mercator-ocean.eu\n",
       "    creation_date:        2021-11-30T06:00:00Z\n",
       "    credit:               E.U. Copernicus Marine Service Information (CMEMS)\n",
       "    forcing_data_source:  ECMWF Global Atmospheric Model (HRES); CMEMS-MERCAT...\n",
       "    ...                   ...\n",
       "    institution:          UK Met Office\n",
       "    licence:              http://marine.copernicus.eu/services-portfolio/serv...\n",
       "    netcdf-version-id:    netCDF-4\n",
       "    product:              NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013\n",
       "    references:           http://marine.copernicus.eu/\n",
       "    source:               PS-OS 45, AMM-FOAM 1.5 km (tidal) NEMO v3.6_WAVEWAT...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8373e64b-1f40-4dc5-afb3-d091d47eef3e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8373e64b-1f40-4dc5-afb3-d091d47eef3e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 1240</li><li><span class='xr-has-index'>longitude</span>: 958</li><li><span class='xr-has-index'>depth</span>: 33</li><li><span class='xr-has-index'>time</span>: 44136</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c936b15c-0bf2-45b5-81d0-8b4ebfa95d56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c936b15c-0bf2-45b5-81d0-8b4ebfa95d56' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>46.0 46.01 46.03 ... 62.73 62.74</div><input id='attrs-e035d111-7f3c-4c3a-b54e-991fd25f3d21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e035d111-7f3c-4c3a-b54e-991fd25f3d21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acebb9c0-50b9-4576-9c73-df2e7fb9e556' class='xr-var-data-in' type='checkbox'><label for='data-acebb9c0-50b9-4576-9c73-df2e7fb9e556' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>nav_model :</span></dt><dd>grid_T</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>46.0</dd><dt><span>valid_max :</span></dt><dd>62.74324</dd></dl></div><div class='xr-var-data'><pre>array([46.     , 46.01351, 46.02703, ..., 62.71622, 62.72973, 62.74324],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-16.0 -15.97 -15.94 ... 12.97 13.0</div><input id='attrs-a45ff7fd-c7e7-487e-a0c3-42d161ff85c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a45ff7fd-c7e7-487e-a0c3-42d161ff85c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-318d6f17-c87c-4604-9fb1-047c90f6a977' class='xr-var-data-in' type='checkbox'><label for='data-318d6f17-c87c-4604-9fb1-047c90f6a977' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>nav_model :</span></dt><dd>grid_T</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-16.0</dd><dt><span>valid_max :</span></dt><dd>13.0</dd></dl></div><div class='xr-var-data'><pre>array([-16.     , -15.9697 , -15.93939, ...,  12.93939,  12.9697 ,  13.     ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 3.0 5.0 ... 3e+03 4e+03 5e+03</div><input id='attrs-c4ac5198-ba9d-422a-a0ed-2e6420bec8f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4ac5198-ba9d-422a-a0ed-2e6420bec8f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ad6ce10-1a07-413c-9ee0-e21cbc36ec7b' class='xr-var-data-in' type='checkbox'><label for='data-2ad6ce10-1a07-413c-9ee0-e21cbc36ec7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>5000.0</dd></dl></div><div class='xr-var-data'><pre>array([0.00e+00, 3.00e+00, 5.00e+00, 1.00e+01, 1.50e+01, 2.00e+01, 2.50e+01,\n",
       "       3.00e+01, 4.00e+01, 5.00e+01, 6.00e+01, 7.50e+01, 1.00e+02, 1.25e+02,\n",
       "       1.50e+02, 1.75e+02, 2.00e+02, 2.25e+02, 2.50e+02, 3.00e+02, 3.50e+02,\n",
       "       4.00e+02, 4.50e+02, 5.00e+02, 5.50e+02, 6.00e+02, 7.50e+02, 1.00e+03,\n",
       "       1.50e+03, 2.00e+03, 3.00e+03, 4.00e+03, 5.00e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-01-01T01:00:00 ... 2024-01-14</div><input id='attrs-666353a6-1cae-41b0-9fbc-c265d89e18ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-666353a6-1cae-41b0-9fbc-c265d89e18ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59c1840f-35db-499f-a80d-3b75cc6450bd' class='xr-var-data-in' type='checkbox'><label for='data-59c1840f-35db-499f-a80d-3b75cc6450bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>2019-01-01T01:00:00.000000000</dd><dt><span>valid_max :</span></dt><dd>2024-01-14T00:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-01-01T01:00:00.000000000&#x27;, &#x27;2019-01-01T02:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T03:00:00.000000000&#x27;, ..., &#x27;2024-01-13T22:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-13T23:00:00.000000000&#x27;, &#x27;2024-01-14T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-529a2b79-1ff1-42b3-82db-a60a35395eaa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-529a2b79-1ff1-42b3-82db-a60a35395eaa' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mdt</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1469c16-087c-409c-ae53-8a1f19332c5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1469c16-087c-409c-ae53-8a1f19332c5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cb42631-516a-4079-a34d-1a91f19bff62' class='xr-var-data-in' type='checkbox'><label for='data-9cb42631-516a-4079-a34d-1a91f19bff62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean dynamic topography</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1187920 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0a587987-ca0b-4a21-bb97-23476a282ea9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a587987-ca0b-4a21-bb97-23476a282ea9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47632b88-0305-4003-95d2-73b85c01d080' class='xr-var-data-in' type='checkbox'><label for='data-47632b88-0305-4003-95d2-73b85c01d080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_max :</span></dt><dd>30000</dd><dt><span>valid_min :</span></dt><dd>-30000</dd></dl></div><div class='xr-var-data'><pre>[1730191224960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zos</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c7593a5-e020-4d42-ac5d-30e0f6f122ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c7593a5-e020-4d42-ac5d-30e0f6f122ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fad3fcd-88c7-42ce-a73a-143c649743f5' class='xr-var-data-in' type='checkbox'><label for='data-5fad3fcd-88c7-42ce-a73a-143c649743f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface height above geoid</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>10000</dd><dt><span>valid_min :</span></dt><dd>-10000</dd></dl></div><div class='xr-var-data'><pre>[52430037120 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0ed5c0c-141f-44de-bb90-dffb9dcdf58c' class='xr-section-summary-in' type='checkbox'  ><label for='section-d0ed5c0c-141f-44de-bb90-dffb9dcdf58c' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e7fee79-2a12-46b1-82ad-db56eeebe422' class='xr-index-data-in' type='checkbox'/><label for='index-0e7fee79-2a12-46b1-82ad-db56eeebe422' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              46.0, 46.013511657714844,  46.02703094482422,\n",
       "         46.0405387878418,  46.05405044555664, 46.067569732666016,\n",
       "        46.08108139038086,  46.09458923339844,  46.10810852050781,\n",
       "       46.121620178222656,\n",
       "       ...\n",
       "       62.621620178222656,  62.63513946533203, 62.648651123046875,\n",
       "        62.66215896606445,   62.6756706237793,  62.68918991088867,\n",
       "       62.702701568603516,  62.71622085571289,  62.72972869873047,\n",
       "        62.74324035644531],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=1240))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-569b9a44-8348-46ea-ae7e-5671dac3041a' class='xr-index-data-in' type='checkbox'/><label for='index-569b9a44-8348-46ea-ae7e-5671dac3041a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -16.0,  -15.96969985961914, -15.939390182495117,\n",
       "       -15.909090042114258, -15.878789901733398, -15.848480224609375,\n",
       "       -15.818180084228516, -15.787879943847656, -15.757579803466797,\n",
       "       -15.727270126342773,\n",
       "       ...\n",
       "        12.727270126342773,  12.757579803466797,  12.787879943847656,\n",
       "        12.818180084228516,  12.848489761352539,  12.878789901733398,\n",
       "        12.909090042114258,  12.939390182495117,   12.96969985961914,\n",
       "                      13.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=958))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bdc69ba0-5dfd-4b09-bc9a-d308adddce67' class='xr-index-data-in' type='checkbox'/><label for='index-bdc69ba0-5dfd-4b09-bc9a-d308adddce67' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,    3.0,    5.0,   10.0,   15.0,   20.0,   25.0,   30.0,   40.0,\n",
       "         50.0,   60.0,   75.0,  100.0,  125.0,  150.0,  175.0,  200.0,  225.0,\n",
       "        250.0,  300.0,  350.0,  400.0,  450.0,  500.0,  550.0,  600.0,  750.0,\n",
       "       1000.0, 1500.0, 2000.0, 3000.0, 4000.0, 5000.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cfdd17b1-39ce-4eda-9c7b-1347a64c2e7a' class='xr-index-data-in' type='checkbox'/><label for='index-cfdd17b1-39ce-4eda-9c7b-1347a64c2e7a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-01-01 01:00:00&#x27;, &#x27;2019-01-01 02:00:00&#x27;,\n",
       "               &#x27;2019-01-01 03:00:00&#x27;, &#x27;2019-01-01 04:00:00&#x27;,\n",
       "               &#x27;2019-01-01 05:00:00&#x27;, &#x27;2019-01-01 06:00:00&#x27;,\n",
       "               &#x27;2019-01-01 07:00:00&#x27;, &#x27;2019-01-01 08:00:00&#x27;,\n",
       "               &#x27;2019-01-01 09:00:00&#x27;, &#x27;2019-01-01 10:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-01-13 15:00:00&#x27;, &#x27;2024-01-13 16:00:00&#x27;,\n",
       "               &#x27;2024-01-13 17:00:00&#x27;, &#x27;2024-01-13 18:00:00&#x27;,\n",
       "               &#x27;2024-01-13 19:00:00&#x27;, &#x27;2024-01-13 20:00:00&#x27;,\n",
       "               &#x27;2024-01-13 21:00:00&#x27;, &#x27;2024-01-13 22:00:00&#x27;,\n",
       "               &#x27;2024-01-13 23:00:00&#x27;, &#x27;2024-01-14 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=44136, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1731300-91bb-4d34-b7c5-36e61690e2b0' class='xr-section-summary-in' type='checkbox'  ><label for='section-f1731300-91bb-4d34-b7c5-36e61690e2b0' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>contact :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu</dd><dt><span>creation_date :</span></dt><dd>2021-11-30T06:00:00Z</dd><dt><span>credit :</span></dt><dd>E.U. Copernicus Marine Service Information (CMEMS)</dd><dt><span>forcing_data_source :</span></dt><dd>ECMWF Global Atmospheric Model (HRES); CMEMS-MERCATOR12; UKMO GLOWave; CMEMS-BALTIC; climatological rivers</dd><dt><span>history :</span></dt><dd>See source and creation_date attributes</dd><dt><span>institution :</span></dt><dd>UK Met Office</dd><dt><span>licence :</span></dt><dd>http://marine.copernicus.eu/services-portfolio/service-commitments-and-licence/</dd><dt><span>netcdf-version-id :</span></dt><dd>netCDF-4</dd><dt><span>product :</span></dt><dd>NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu/</dd><dt><span>source :</span></dt><dd>PS-OS 45, AMM-FOAM 1.5 km (tidal) NEMO v3.6_WAVEWATCH-III v4.18_NEMOVAR v6</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 1240, longitude: 958, depth: 33, time: 44136)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 46.0 46.01 46.03 46.04 ... 62.72 62.73 62.74\n",
       "  * longitude  (longitude) float32 -16.0 -15.97 -15.94 ... 12.94 12.97 13.0\n",
       "  * depth      (depth) float32 0.0 3.0 5.0 10.0 15.0 ... 2e+03 3e+03 4e+03 5e+03\n",
       "  * time       (time) datetime64[ns] 2019-01-01T01:00:00 ... 2024-01-14\n",
       "Data variables:\n",
       "    mdt        (latitude, longitude) float32 ...\n",
       "    thetao     (time, depth, latitude, longitude) float32 ...\n",
       "    zos        (time, latitude, longitude) float32 ...\n",
       "Attributes: (12/13)\n",
       "    Conventions:          CF-1.7\n",
       "    NCO:                  netCDF Operators version 4.7.5 (Homepage = http://n...\n",
       "    contact:              servicedesk.cmems@mercator-ocean.eu\n",
       "    creation_date:        2021-11-30T06:00:00Z\n",
       "    credit:               E.U. Copernicus Marine Service Information (CMEMS)\n",
       "    forcing_data_source:  ECMWF Global Atmospheric Model (HRES); CMEMS-MERCAT...\n",
       "    ...                   ...\n",
       "    institution:          UK Met Office\n",
       "    licence:              http://marine.copernicus.eu/services-portfolio/serv...\n",
       "    netcdf-version-id:    netCDF-4\n",
       "    product:              NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013\n",
       "    references:           http://marine.copernicus.eu/\n",
       "    source:               PS-OS 45, AMM-FOAM 1.5 km (tidal) NEMO v3.6_WAVEWAT..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://data.marine.copernicus.eu/product/IBI_ANALYSISFORECAST_PHY_005_001/description\n",
    "# the web site indicates that we have data from 2020, but i see only from 2023 March.\n",
    "\n",
    "#ds=copernicusmarine.open_dataset(dataset_id='cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m')\n",
    "#ds=copernicusmarine.open_dataset(dataset_id='cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m')\n",
    "#ds=copernicusmarine.open_dataset(dataset_id='cmems_mod_nws_phy-t_anfc_7km-3D_P1D-m')\n",
    "#0 14 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013\n",
    "#14 dataset_id MetO-NWS-PHY-hi-TEM hourly-instantaneous potential temperature (3D)\n",
    "#ds=copernicusmarine.open_dataset(dataset_id='MetO-NWS-PHY-hi-TEM')\n",
    "#0 11 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013\n",
    "#11 dataset_id MetO-NWS-PHY-hi-SSH hourly-instantaneous SSH (2D)\n",
    "#ds=\n",
    "\n",
    "MetO_NWS_PHY=xr.merge([\n",
    "    copernicusmarine.open_dataset(dataset_id='MetO-NWS-PHY-001-013-STATIC'),\n",
    "    copernicusmarine.open_dataset(dataset_id='MetO-NWS-PHY-hi-TEM'),\n",
    "    copernicusmarine.open_dataset(dataset_id='MetO-NWS-PHY-hi-SSH')])\n",
    "\n",
    "MetO_NWS_PHY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69186a17-181b-4c95-9e4b-7aef439954cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f867d41f290>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#MetO_NWS_PHY.thetao.isel(depth=0,time=0).plot()\n",
    "MetO_NWS_PHY.mdt.plot()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "64fc80c2-3415-4301-ac10-6885534643d7",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "catalogue['products'][index]['datasets'][index2]['dataset_name']"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1adc00d9-2cf4-4561-8c6b-4b36d63f35a3",
   "metadata": {},
   "source": [
    "sst_l3s = copernicusmarine.open_dataset(\n",
    "    dataset_id = data_request[\"dataset_id_sst_gap_l3s\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ae46fa67-8473-4d98-8a0b-ff4f3a266067",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Arctic Ocean Physics Analysis and Forecast ARCTIC_ANALYSISFORECAST_PHY_002_001\n",
      "0 cmems_mod_arc_phy_anfc_6km_detided_P1D-m Arctic Ocean Physics Analysis and Forecast, 6.25 km daily mean\n",
      "0 0\n",
      "0 Arctic Ocean Physics Analysis and Forecast ARCTIC_ANALYSISFORECAST_PHY_002_001\n",
      "1 cmems_mod_arc_phy_anfc_6km_detided_P1M-m Arctic Ocean Physics Analysis, 6.25km monthly mean\n",
      "0 1\n",
      "1 Arctic Ocean Physics Reanalysis ARCTIC_MULTIYEAR_PHY_002_003\n",
      "0 cmems_mod_arc_phy_my_topaz4_P1D-m Arctic Ocean Physics Reanalysis\n",
      "1 0\n",
      "1 Arctic Ocean Physics Reanalysis ARCTIC_MULTIYEAR_PHY_002_003\n",
      "1 cmems_mod_arc_phy_my_topaz4_P1M Arctic Ocean Physics Reanalysis\n",
      "1 1\n",
      "1 Arctic Ocean Physics Reanalysis ARCTIC_MULTIYEAR_PHY_002_003\n",
      "2 cmems_mod_arc_phy_my_topaz4_P1Y Arctic Ocean Physics Reanalysis\n",
      "1 2\n",
      "2 Baltic Sea Physics Analysis and Forecast BALTICSEA_ANALYSISFORECAST_PHY_003_006\n",
      "0 cmems_mod_bal_phy_anfc_P1D-m cmems_mod_bal_phy_anfc_P1D-m\n",
      "2 0\n",
      "2 Baltic Sea Physics Analysis and Forecast BALTICSEA_ANALYSISFORECAST_PHY_003_006\n",
      "1 cmems_mod_bal_phy_anfc_P1M-m cmems_mod_bal_phy_anfc_P1M-m\n",
      "2 1\n",
      "2 Baltic Sea Physics Analysis and Forecast BALTICSEA_ANALYSISFORECAST_PHY_003_006\n",
      "2 cmems_mod_bal_phy_anfc_PT1H-i CMEMS NEMO hourly model fields\n",
      "2 2\n",
      "2 Baltic Sea Physics Analysis and Forecast BALTICSEA_ANALYSISFORECAST_PHY_003_006\n",
      "3 cmems_mod_bal_phy_anfc_PT1h-i CMEMS NEMO hourly model fields\n",
      "2 3\n",
      "3 Baltic Sea Physics Reanalysis BALTICSEA_MULTIYEAR_PHY_003_011\n",
      "0 cmems_mod_bal_phy_my_P1D-m CMEMS NEMO daily integrated model fields\n",
      "3 0\n",
      "3 Baltic Sea Physics Reanalysis BALTICSEA_MULTIYEAR_PHY_003_011\n",
      "1 cmems_mod_bal_phy_my_P1M-m CMEMS NEMO monthly integrated model fields\n",
      "3 1\n",
      "3 Baltic Sea Physics Reanalysis BALTICSEA_MULTIYEAR_PHY_003_011\n",
      "2 cmems_mod_bal_phy_my_P1Y-m CMEMS NEMO annual integrated model fields\n",
      "3 2\n",
      "4 Black Sea Physics Analysis and Forecast BLKSEA_ANALYSISFORECAST_PHY_007_001\n",
      "0 cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m\n",
      "4 0\n",
      "4 Black Sea Physics Analysis and Forecast BLKSEA_ANALYSISFORECAST_PHY_007_001\n",
      "1 cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m\n",
      "4 1\n",
      "4 Black Sea Physics Analysis and Forecast BLKSEA_ANALYSISFORECAST_PHY_007_001\n",
      "2 cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m\n",
      "4 2\n",
      "4 Black Sea Physics Analysis and Forecast BLKSEA_ANALYSISFORECAST_PHY_007_001\n",
      "3 cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m Potential Temperature (3D) - Daily Mean\n",
      "4 3\n",
      "4 Black Sea Physics Analysis and Forecast BLKSEA_ANALYSISFORECAST_PHY_007_001\n",
      "4 cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i Potential Temperature (3D) - Hourly Mean\n",
      "4 4\n",
      "5 Black Sea Physics Reanalysis BLKSEA_MULTIYEAR_PHY_007_004\n",
      "0 cmems_mod_blk_phy-tem_my_2.5km_P1D-m Potential Temperature (3D), Bottom Temperature (2D) - Daily Mean\n",
      "5 0\n",
      "5 Black Sea Physics Reanalysis BLKSEA_MULTIYEAR_PHY_007_004\n",
      "1 cmems_mod_blk_phy-tem_my_2.5km_P1M-m Potential Temperature (3D), Bottom Temperature (2D) - Monthly Mean\n",
      "5 1\n",
      "5 Black Sea Physics Reanalysis BLKSEA_MULTIYEAR_PHY_007_004\n",
      "2 cmems_mod_blk_phy-tem_my_2.5km_P1Y-m Potential Temperature (3D), Bottom Temperature (2D) - Yearly Mean\n",
      "5 2\n",
      "5 Black Sea Physics Reanalysis BLKSEA_MULTIYEAR_PHY_007_004\n",
      "3 cmems_mod_blk_phy-tem_myint_2.5km_P1M-m Potential Temperature (3D), Bottom Temperature (2D) - Monthly Mean\n",
      "5 3\n",
      "6 Global Ocean Physics Analysis and Forecast GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "0 cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "6 0\n",
      "6 Global Ocean Physics Analysis and Forecast GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "1 cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m Monthly mean fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "6 1\n",
      "6 Global Ocean Physics Analysis and Forecast GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "2 cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i Instantaneous fields for product GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "6 2\n",
      "6 Global Ocean Physics Analysis and Forecast GLOBAL_ANALYSISFORECAST_PHY_001_024\n",
      "3 cmems_mod_glo_phy_anfc_0.083deg_PT1H-m hourly mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "6 3\n",
      "7 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "0 cmems_mod_glo_phy_my_0.083-climatology_P1M-m Monthly climatology fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "7 0\n",
      "7 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "1 cmems_mod_glo_phy_my_0.083_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "7 1\n",
      "7 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "2 cmems_mod_glo_phy_my_0.083_P1M-m Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "7 2\n",
      "7 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "3 cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m Monthly climatology fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "7 3\n",
      "7 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "4 cmems_mod_glo_phy_my_0.083deg_P1D-m daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily\n",
      "7 4\n",
      "7 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "5 cmems_mod_glo_phy_my_0.083deg_P1M-m Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "7 5\n",
      "7 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "6 cmems_mod_glo_phy_myint_0.083deg_P1D-m cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311\n",
      "7 6\n",
      "7 Global Ocean Physics Reanalysis GLOBAL_MULTIYEAR_PHY_001_030\n",
      "7 cmems_mod_glo_phy_myint_0.083deg_P1M-m Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_030\n",
      "7 7\n",
      "8 Global Ocean Ensemble Physics Reanalysis GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "0 cmems_mod_glo_phy-all_my_0.25deg_P1M-m Monthly mean fields for product GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "8 0\n",
      "8 Global Ocean Ensemble Physics Reanalysis GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "1 cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m Daily mean fields for product GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "8 1\n",
      "8 Global Ocean Ensemble Physics Reanalysis GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "2 cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m Monthly mean fields for product GLOBAL_MULTIYEAR_PHY_ENS_001_031\n",
      "8 2\n",
      "9 Global Ocean Ensemble Physics Reanalysis - Low resolution GLOBAL_REANALYSIS_PHY_001_026\n",
      "0 global-reanalysis-phy-001-026-grepv1-monthly monthly mean from reanalyses\n",
      "9 0\n",
      "10 Global Ocean Ensemble Physics Reanalysis GLOBAL_REANALYSIS_PHY_001_031\n",
      "0 global-reanalysis-phy-001-031-grepv2-daily Daily mean fields for product GLOBAL_REANALYSIS_PHY_001_031\n",
      "10 0\n",
      "10 Global Ocean Ensemble Physics Reanalysis GLOBAL_REANALYSIS_PHY_001_031\n",
      "1 global-reanalysis-phy-001-031-grepv2-mnstd-daily Daily mean fields for product GLOBAL_REANALYSIS_PHY_001_031\n",
      "10 1\n",
      "10 Global Ocean Ensemble Physics Reanalysis GLOBAL_REANALYSIS_PHY_001_031\n",
      "2 global-reanalysis-phy-001-031-grepv2-mnstd-monthly Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_031\n",
      "10 2\n",
      "10 Global Ocean Ensemble Physics Reanalysis GLOBAL_REANALYSIS_PHY_001_031\n",
      "3 global-reanalysis-phy-001-031-grepv2-monthly Monthly mean fields for product GLOBAL_REANALYSIS_PHY_001_031\n",
      "10 3\n",
      "11 Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast IBI_ANALYSISFORECAST_PHY_005_001\n",
      "0 cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m Ocean surface hourly mean fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "11 0\n",
      "11 Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast IBI_ANALYSISFORECAST_PHY_005_001\n",
      "1 cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m Ocean 3D daily mean fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "11 1\n",
      "11 Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast IBI_ANALYSISFORECAST_PHY_005_001\n",
      "2 cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m Ocean 3D monthly mean fields for the Iberia-Biscay-Ireland (IBI) region\n",
      "11 2\n",
      "11 Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast IBI_ANALYSISFORECAST_PHY_005_001\n",
      "3 cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m Ocean 3D IBI hourly mean fields\n",
      "11 3\n",
      "11 Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast IBI_ANALYSISFORECAST_PHY_005_001\n",
      "4 cmems_mod_ibi_phy_anfc_0.027deg-3D_static cmems_mod_ibi_phy_anfc_0.027deg-3D_static\n",
      "11 4\n",
      "12 Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis IBI_MULTIYEAR_PHY_005_002\n",
      "0 cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m CMEMS IBI REANALYSIS: HOURLY PHYSICAL PRODUCTS \n",
      "12 0\n",
      "12 Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis IBI_MULTIYEAR_PHY_005_002\n",
      "1 cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m Monthly climatology fields for product IBI_MULTIYEAR_PHY_005_002\n",
      "12 1\n",
      "12 Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis IBI_MULTIYEAR_PHY_005_002\n",
      "2 cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m CMEMS IBI REANALYSIS: DAILY PHYSICAL PRODUCTS \n",
      "12 2\n",
      "12 Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis IBI_MULTIYEAR_PHY_005_002\n",
      "3 cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m CMEMS IBI REANALYSIS: MONTHLY PHYSICAL PRODUCTS \n",
      "12 3\n",
      "12 Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis IBI_MULTIYEAR_PHY_005_002\n",
      "4 cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m CMEMS IBI REANALYSIS: YEARLY PHYSICAL PRODUCTS\n",
      "12 4\n",
      "12 Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis IBI_MULTIYEAR_PHY_005_002\n",
      "5 cmems_mod_ibi_phy_my_0.083deg-3D_static cmems_mod_ibi_phy_my_0.083deg-3D_static\n",
      "12 5\n",
      "13 Mediterranean Sea Physics Analysis and Forecast MEDSEA_ANALYSISFORECAST_PHY_006_013\n",
      "0 cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m Sea Surface Temperature (2D) - Hourly Mean\n",
      "13 0\n",
      "13 Mediterranean Sea Physics Analysis and Forecast MEDSEA_ANALYSISFORECAST_PHY_006_013\n",
      "1 cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m Sea Temperature (3D) - Hourly Mean\n",
      "13 1\n",
      "13 Mediterranean Sea Physics Analysis and Forecast MEDSEA_ANALYSISFORECAST_PHY_006_013\n",
      "2 cmems_mod_med_phy-tem_anfc_4.2km_P1D-m cmems_mod_med_phy-tem_anfc_4.2km_P1D-m\n",
      "13 2\n",
      "13 Mediterranean Sea Physics Analysis and Forecast MEDSEA_ANALYSISFORECAST_PHY_006_013\n",
      "3 cmems_mod_med_phy-tem_anfc_4.2km_P1M-m cmems_mod_med_phy-tem_anfc_4.2km_P1M-m\n",
      "13 3\n",
      "13 Mediterranean Sea Physics Analysis and Forecast MEDSEA_ANALYSISFORECAST_PHY_006_013\n",
      "4 cmems_mod_med_phy-tem_anfc_4.2km_PT1H-m Sea Temperature (3D) - Hourly Mean\n",
      "13 4\n",
      "13 Mediterranean Sea Physics Analysis and Forecast MEDSEA_ANALYSISFORECAST_PHY_006_013\n",
      "5 cmems_mod_med_phy-tem_anfc_4.2km_PT1HTS-m Sea Temperature (3D) - Hourly Mean - time series\n",
      "13 5\n",
      "14 Mediterranean Sea Physics Reanalysis MEDSEA_MULTIYEAR_PHY_006_004\n",
      "0 cmems_mod_med_phy-tem_my_4.2km_P1Y-m Potential Temperature (3D) - Yearly Mean\n",
      "14 0\n",
      "14 Mediterranean Sea Physics Reanalysis MEDSEA_MULTIYEAR_PHY_006_004\n",
      "1 med-cmcc-tem-int-m Potential Temperature (3D) - Monthly Mean\n",
      "14 1\n",
      "14 Mediterranean Sea Physics Reanalysis MEDSEA_MULTIYEAR_PHY_006_004\n",
      "2 med-cmcc-tem-rean-d Potential Temperature (3D) - Daily Mean\n",
      "14 2\n",
      "14 Mediterranean Sea Physics Reanalysis MEDSEA_MULTIYEAR_PHY_006_004\n",
      "3 med-cmcc-tem-rean-m Potential Temperature (3D) - Monthly Mean\n",
      "14 3\n",
      "15 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013\n",
      "0 MetO-NWS-PHY-dm-TEM daily-mean potential temperature (3D)\n",
      "15 0\n",
      "15 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013\n",
      "1 MetO-NWS-PHY-hi-SST hourly-instantaneous surface temperature (2D)\n",
      "15 1\n",
      "15 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013\n",
      "2 MetO-NWS-PHY-hi-TEM hourly-instantaneous potential temperature (3D)\n",
      "15 2\n",
      "16 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NWSHELF_ANALYSISFORECAST_PHY_004_013\n",
      "0 cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m Ocean surface hourly mean fields for the North West Shelf (NWS) region\n",
      "16 0\n",
      "16 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NWSHELF_ANALYSISFORECAST_PHY_004_013\n",
      "1 cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m Ocean 3D daily mean fields for the North West Shelf (NWS) region\n",
      "16 1\n",
      "16 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NWSHELF_ANALYSISFORECAST_PHY_004_013\n",
      "2 cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m Ocean 3D monthly mean fields for the North West Shelf (NWS) region\n",
      "16 2\n",
      "16 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NWSHELF_ANALYSISFORECAST_PHY_004_013\n",
      "3 cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m Ocean 3D hourly mean fields for the North West Shelf (NWS) region\n",
      "16 3\n",
      "17 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast NWSHELF_ANALYSISFORECAST_PHY_LR_004_001\n",
      "0 cmems_mod_nws_phy-t_anfc_7km-3D_P1D-m daily-mean potential temperature (3D)\n",
      "17 0\n",
      "18 Atlantic- European North West Shelf- Ocean Physics Reanalysis NWSHELF_MULTIYEAR_PHY_004_009\n",
      "0 cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i hourly-instantaneous surface temperature (2D)\n",
      "18 0\n",
      "18 Atlantic- European North West Shelf- Ocean Physics Reanalysis NWSHELF_MULTIYEAR_PHY_004_009\n",
      "1 cmems_mod_nws_phy-t_my_7km-3D_P1D-m daily-mean potential temperature (3D)\n",
      "18 1\n",
      "18 Atlantic- European North West Shelf- Ocean Physics Reanalysis NWSHELF_MULTIYEAR_PHY_004_009\n",
      "2 cmems_mod_nws_phy-t_my_7km-3D_P1M-m monthly-mean potential temperature (3D)\n",
      "18 2\n",
      "18 Atlantic- European North West Shelf- Ocean Physics Reanalysis NWSHELF_MULTIYEAR_PHY_004_009\n",
      "3 cmems_mod_nws_phy-t_myint_7km-3D_P1M-m monthly-mean potential temperature (3D)\n",
      "18 3\n"
     ]
    }
   ],
   "source": [
    "#pprint(catalogue)\n",
    "for index, product1 in enumerate(catalogue['products']):\n",
    "#    \n",
    "    for index2, product in enumerate(catalogue['products'][index]['datasets']):\n",
    "        print( index,product1['title'], product1['product_id'] )\n",
    "        print( index2,product['dataset_id'], product['dataset_name'])\n",
    "        print(index,index2)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ca697de1-b24c-4d1f-8acd-f521aded8109",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO - 2024-01-31T21:40:27Z - Dataset version was not specified, the latest one was selected: \"default\"\n",
      "INFO - 2024-01-31T21:40:27Z - Dataset part was not specified, the first one was selected: \"default\"\n",
      "INFO - 2024-01-31T21:40:30Z - Service was not specified, the default one was selected: \"arco-geo-series\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (depth: 12, latitude: 321, longitude: 2880, time: 2230)\n",
       "Coordinates:\n",
       "  * depth        (depth) float32 5.0 30.0 50.0 100.0 ... 2e+03 2.5e+03 3e+03\n",
       "  * latitude     (latitude) float64 50.0 50.12 50.25 50.38 ... 89.75 89.88 90.0\n",
       "  * longitude    (longitude) float64 -180.0 -179.9 -179.8 ... 179.6 179.8 179.9\n",
       "  * time         (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2024-02-08\n",
       "Data variables: (12/17)\n",
       "    albedo       (time, latitude, longitude) float32 ...\n",
       "    bsfd         (time, latitude, longitude) float32 ...\n",
       "    btemp        (time, latitude, longitude) float32 ...\n",
       "    fice         (time, latitude, longitude) float32 ...\n",
       "    fy_age       (time, latitude, longitude) float32 ...\n",
       "    fy_frac      (time, latitude, longitude) float32 ...\n",
       "    ...           ...\n",
       "    ssh          (time, latitude, longitude) float32 ...\n",
       "    temperature  (time, depth, latitude, longitude) float32 ...\n",
       "    u            (time, depth, latitude, longitude) float32 ...\n",
       "    uice         (time, latitude, longitude) float32 ...\n",
       "    v            (time, depth, latitude, longitude) float32 ...\n",
       "    vice         (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:    CF-1.4\n",
       "    bulletin_date:  2018-01-08\n",
       "    bulletin_type:  Hindcast\n",
       "    field_date:     2018-01-01\n",
       "    field_type:     Files based on file type nersc_daily\n",
       "    history:        20180108:Created by program hyc2proj, version V0.3\n",
       "    institution:    MET Norway, Henrik Mohns plass 1, N-0313 Oslo, Norway\n",
       "    references:     http://marine.copernicus.eu\n",
       "    source:         NERSC-HYCOM model fields\n",
       "    title:          Arctic Ocean Physics Analysis and Forecast, 12.5km daily ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-462bf8a5-9664-494a-8055-2673456b5f41' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-462bf8a5-9664-494a-8055-2673456b5f41' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>depth</span>: 12</li><li><span class='xr-has-index'>latitude</span>: 321</li><li><span class='xr-has-index'>longitude</span>: 2880</li><li><span class='xr-has-index'>time</span>: 2230</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c7fd9047-5249-48b2-880c-b7345d924ef4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7fd9047-5249-48b2-880c-b7345d924ef4' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 30.0 50.0 ... 2.5e+03 3e+03</div><input id='attrs-624345f3-fddb-4c7f-a20d-66611d6529df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-624345f3-fddb-4c7f-a20d-66611d6529df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e6b9885-a336-4aab-b6e5-c4456d261376' class='xr-var-data-in' type='checkbox'><label for='data-0e6b9885-a336-4aab-b6e5-c4456d261376' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>5.0</dd><dt><span>valid_max :</span></dt><dd>3000.0</dd></dl></div><div class='xr-var-data'><pre>array([   5.,   30.,   50.,  100.,  200.,  400.,  700., 1000., 1500., 2000.,\n",
       "       2500., 3000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>50.0 50.12 50.25 ... 89.88 90.0</div><input id='attrs-e6854963-5a00-4132-aaef-565cc16e75f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6854963-5a00-4132-aaef-565cc16e75f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce16c9be-515b-436b-b5e0-1dbbedd66823' class='xr-var-data-in' type='checkbox'><label for='data-ce16c9be-515b-436b-b5e0-1dbbedd66823' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>50.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([50.   , 50.125, 50.25 , ..., 89.75 , 89.875, 90.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.8 179.9</div><input id='attrs-8b39f0e0-223b-4db7-bd0d-b963999e95e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b39f0e0-223b-4db7-bd0d-b963999e95e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa4e9b4-e1aa-46c4-a480-df63e5dc3e26' class='xr-var-data-in' type='checkbox'><label for='data-caa4e9b4-e1aa-46c4-a480-df63e5dc3e26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>179.875</dd></dl></div><div class='xr-var-data'><pre>array([-180.   , -179.875, -179.75 , ...,  179.625,  179.75 ,  179.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2024-02-08</div><input id='attrs-37db210c-dc2b-434e-b6e9-e8d81d4d3212' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37db210c-dc2b-434e-b6e9-e8d81d4d3212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc8dd892-dbf9-47be-9734-b981732e8de1' class='xr-var-data-in' type='checkbox'><label for='data-fc8dd892-dbf9-47be-9734-b981732e8de1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>2018-01-01T00:00:00.000000000</dd><dt><span>valid_max :</span></dt><dd>2024-02-08T00:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-03T00:00:00.000000000&#x27;, ..., &#x27;2024-02-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-07T00:00:00.000000000&#x27;, &#x27;2024-02-08T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56dda418-c812-4b35-b4e9-1919c8d22253' class='xr-section-summary-in' type='checkbox'  ><label for='section-56dda418-c812-4b35-b4e9-1919c8d22253' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albedo</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f57b643e-c30b-4cd5-8ae8-25b31cd2125a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f57b643e-c30b-4cd5-8ae8-25b31cd2125a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b010543a-f4b0-47f1-838b-0775c135e431' class='xr-var-data-in' type='checkbox'><label for='data-b010543a-f4b0-47f1-838b-0775c135e431' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>sea_ice_albedo</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bsfd</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa7854ea-bf0f-4ff8-9778-109f6238d298' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa7854ea-bf0f-4ff8-9778-109f6238d298' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4be57798-855b-4fd3-8c7b-40766be7e607' class='xr-var-data-in' type='checkbox'><label for='data-4be57798-855b-4fd3-8c7b-40766be7e607' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>ocean_barotropic_streamfunction</dd><dt><span>units :</span></dt><dd>m3 s-1</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>btemp</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-848b0709-4b92-4173-8dc2-b46644c0b6f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-848b0709-4b92-4173-8dc2-b46644c0b6f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-693dfe30-0cee-401c-ada5-d51d1f153b1e' class='xr-var-data-in' type='checkbox'><label for='data-693dfe30-0cee-401c-ada5-d51d1f153b1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>Sea floor potential temperature</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature_at_sea_floor</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fice</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9bbfb52f-a5dd-423f-83f0-c41600fac58c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bbfb52f-a5dd-423f-83f0-c41600fac58c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b946ef2-0cbf-43e4-b9c5-43fc62eccbb3' class='xr-var-data-in' type='checkbox'><label for='data-6b946ef2-0cbf-43e4-b9c5-43fc62eccbb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fy_age</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b10cdc4-61cf-4a94-969c-a227543340d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b10cdc4-61cf-4a94-969c-a227543340d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f39e87b4-79dd-4073-810c-39a14e393d62' class='xr-var-data-in' type='checkbox'><label for='data-f39e87b4-79dd-4073-810c-39a14e393d62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>age_of_first_year_ice</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>units :</span></dt><dd>day</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fy_frac</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ffebbdcf-524f-4428-b386-71a20f4c31df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffebbdcf-524f-4428-b386-71a20f4c31df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c480c389-1e63-4491-b95e-4f76bb02b41d' class='xr-var-data-in' type='checkbox'><label for='data-c480c389-1e63-4491-b95e-4f76bb02b41d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>fraction_of_first_year_ice</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hice</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9d2d578-743a-4b51-8d7d-17a6a0c26fd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9d2d578-743a-4b51-8d7d-17a6a0c26fd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cddeb6a-c2a1-4995-923c-a6a9f687b0d2' class='xr-var-data-in' type='checkbox'><label for='data-2cddeb6a-c2a1-4995-923c-a6a9f687b0d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean where sea_ice</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>sea_ice_thickness</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hsnow</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f50aa6b1-2f9d-4d52-8f6c-464ab6763cf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f50aa6b1-2f9d-4d52-8f6c-464ab6763cf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-492f1bb4-7857-4d0a-a565-6b65f7d6a670' class='xr-var-data-in' type='checkbox'><label for='data-492f1bb4-7857-4d0a-a565-6b65f7d6a670' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean where sea_ice</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>surface_snow_thickness</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mlp</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d5113eff-b008-4611-999d-dca2f7ffbb19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5113eff-b008-4611-999d-dca2f7ffbb19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f8133a8-bef0-4242-9a2b-4d9801508b66' class='xr-var-data-in' type='checkbox'><label for='data-4f8133a8-bef0-4242-9a2b-4d9801508b66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_depth</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-635428e7-e209-4da7-ab03-45a51bdda246' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-635428e7-e209-4da7-ab03-45a51bdda246' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4256efdc-7621-4302-8e27-410a0bace1f1' class='xr-var-data-in' type='checkbox'><label for='data-4256efdc-7621-4302-8e27-410a0bace1f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>sea_floor_depth_below_sea_level</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>[924480 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f904c4b8-b695-4497-89a2-8c10f0791f18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f904c4b8-b695-4497-89a2-8c10f0791f18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3aa264d-e873-4012-b9f5-4be0b97e413d' class='xr-var-data-in' type='checkbox'><label for='data-e3aa264d-e873-4012-b9f5-4be0b97e413d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd></dl></div><div class='xr-var-data'><pre>[24739084800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssh</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c86f025-98f4-4946-935e-d19b8725e6dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c86f025-98f4-4946-935e-d19b8725e6dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f977cd1-7e1b-4fbf-96e1-84672182a829' class='xr-var-data-in' type='checkbox'><label for='data-6f977cd1-7e1b-4fbf-96e1-84672182a829' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>sea_surface_elevation</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c08843c6-a2e5-4082-9248-6985d4a5e400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c08843c6-a2e5-4082-9248-6985d4a5e400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-678e42b7-f5aa-4f81-b5d0-4dd9ed92d938' class='xr-var-data-in' type='checkbox'><label for='data-678e42b7-f5aa-4f81-b5d0-4dd9ed92d938' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><pre>[24739084800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4aba4b0e-9dcc-43e2-b37a-64c9c9384494' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4aba4b0e-9dcc-43e2-b37a-64c9c9384494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f89be148-5147-454e-b529-418549961c47' class='xr-var-data-in' type='checkbox'><label for='data-f89be148-5147-454e-b529-418549961c47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>[24739084800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uice</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5a4a0fe3-68cf-479c-a719-564d7cbc217a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a4a0fe3-68cf-479c-a719-564d7cbc217a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ea0e8b0-3ba1-4619-9c83-5179a4d5b709' class='xr-var-data-in' type='checkbox'><label for='data-3ea0e8b0-3ba1-4619-9c83-5179a4d5b709' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean where sea_ice</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_ice_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94bd2ed0-412b-48ec-89e7-e28f180d6b78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94bd2ed0-412b-48ec-89e7-e28f180d6b78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83c6b8eb-68a2-4fd5-991c-2206a82c7707' class='xr-var-data-in' type='checkbox'><label for='data-83c6b8eb-68a2-4fd5-991c-2206a82c7707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>[24739084800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vice</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bbcc4a9d-4404-4ec8-98d7-dc1d29673f0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbcc4a9d-4404-4ec8-98d7-dc1d29673f0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dcbf924-b551-4f4d-8ec4-9244acd8d688' class='xr-var-data-in' type='checkbox'><label for='data-8dcbf924-b551-4f4d-8ec4-9244acd8d688' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean where sea_ice</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>standard_name :</span></dt><dd>northward_sea_ice_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>[2061590400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ac19dd9-b666-4416-bf00-95ed72300882' class='xr-section-summary-in' type='checkbox'  ><label for='section-8ac19dd9-b666-4416-bf00-95ed72300882' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37520d55-bd7b-4c4e-985d-d0dc0e6da6f8' class='xr-index-data-in' type='checkbox'/><label for='index-37520d55-bd7b-4c4e-985d-d0dc0e6da6f8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   5.0,   30.0,   50.0,  100.0,  200.0,  400.0,  700.0, 1000.0, 1500.0,\n",
       "       2000.0, 2500.0, 3000.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-395e0427-b9d6-473b-87ae-ee195ae9d158' class='xr-index-data-in' type='checkbox'/><label for='index-395e0427-b9d6-473b-87ae-ee195ae9d158' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  50.0, 50.125,  50.25, 50.375,   50.5, 50.625,  50.75, 50.875,   51.0,\n",
       "       51.125,\n",
       "       ...\n",
       "       88.875,   89.0, 89.125,  89.25, 89.375,   89.5, 89.625,  89.75, 89.875,\n",
       "         90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=321))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0c76ea14-6968-49aa-bab7-9a14d67c1f6f' class='xr-index-data-in' type='checkbox'/><label for='index-0c76ea14-6968-49aa-bab7-9a14d67c1f6f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -180.0, -179.875,  -179.75, -179.625,   -179.5, -179.375,  -179.25,\n",
       "       -179.125,   -179.0, -178.875,\n",
       "       ...\n",
       "         178.75,  178.875,    179.0,  179.125,   179.25,  179.375,    179.5,\n",
       "        179.625,   179.75,  179.875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=2880))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b3975236-bdfd-4c31-bfd2-f4166f8afe22' class='xr-index-data-in' type='checkbox'/><label for='index-b3975236-bdfd-4c31-bfd2-f4166f8afe22' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2018-01-01&#x27;, &#x27;2018-01-02&#x27;, &#x27;2018-01-03&#x27;, &#x27;2018-01-04&#x27;,\n",
       "               &#x27;2018-01-05&#x27;, &#x27;2018-01-06&#x27;, &#x27;2018-01-07&#x27;, &#x27;2018-01-08&#x27;,\n",
       "               &#x27;2018-01-09&#x27;, &#x27;2018-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-01-30&#x27;, &#x27;2024-01-31&#x27;, &#x27;2024-02-01&#x27;, &#x27;2024-02-02&#x27;,\n",
       "               &#x27;2024-02-03&#x27;, &#x27;2024-02-04&#x27;, &#x27;2024-02-05&#x27;, &#x27;2024-02-06&#x27;,\n",
       "               &#x27;2024-02-07&#x27;, &#x27;2024-02-08&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2230, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6afb7d0-8fa4-4dbd-ae4d-5ee3341cb542' class='xr-section-summary-in' type='checkbox'  ><label for='section-a6afb7d0-8fa4-4dbd-ae4d-5ee3341cb542' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>bulletin_date :</span></dt><dd>2018-01-08</dd><dt><span>bulletin_type :</span></dt><dd>Hindcast</dd><dt><span>field_date :</span></dt><dd>2018-01-01</dd><dt><span>field_type :</span></dt><dd>Files based on file type nersc_daily</dd><dt><span>history :</span></dt><dd>20180108:Created by program hyc2proj, version V0.3</dd><dt><span>institution :</span></dt><dd>MET Norway, Henrik Mohns plass 1, N-0313 Oslo, Norway</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>source :</span></dt><dd>NERSC-HYCOM model fields</dd><dt><span>title :</span></dt><dd>Arctic Ocean Physics Analysis and Forecast, 12.5km daily mean (dataset-topaz4-arc-myoceanv2-be)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (depth: 12, latitude: 321, longitude: 2880, time: 2230)\n",
       "Coordinates:\n",
       "  * depth        (depth) float32 5.0 30.0 50.0 100.0 ... 2e+03 2.5e+03 3e+03\n",
       "  * latitude     (latitude) float64 50.0 50.12 50.25 50.38 ... 89.75 89.88 90.0\n",
       "  * longitude    (longitude) float64 -180.0 -179.9 -179.8 ... 179.6 179.8 179.9\n",
       "  * time         (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2024-02-08\n",
       "Data variables: (12/17)\n",
       "    albedo       (time, latitude, longitude) float32 ...\n",
       "    bsfd         (time, latitude, longitude) float32 ...\n",
       "    btemp        (time, latitude, longitude) float32 ...\n",
       "    fice         (time, latitude, longitude) float32 ...\n",
       "    fy_age       (time, latitude, longitude) float32 ...\n",
       "    fy_frac      (time, latitude, longitude) float32 ...\n",
       "    ...           ...\n",
       "    ssh          (time, latitude, longitude) float32 ...\n",
       "    temperature  (time, depth, latitude, longitude) float32 ...\n",
       "    u            (time, depth, latitude, longitude) float32 ...\n",
       "    uice         (time, latitude, longitude) float32 ...\n",
       "    v            (time, depth, latitude, longitude) float32 ...\n",
       "    vice         (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:    CF-1.4\n",
       "    bulletin_date:  2018-01-08\n",
       "    bulletin_type:  Hindcast\n",
       "    field_date:     2018-01-01\n",
       "    field_type:     Files based on file type nersc_daily\n",
       "    history:        20180108:Created by program hyc2proj, version V0.3\n",
       "    institution:    MET Norway, Henrik Mohns plass 1, N-0313 Oslo, Norway\n",
       "    references:     http://marine.copernicus.eu\n",
       "    source:         NERSC-HYCOM model fields\n",
       "    title:          Arctic Ocean Physics Analysis and Forecast, 12.5km daily ..."
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " copernicusmarine.open_dataset( dataset_id='dataset-topaz4-arc-myoceanv2-be')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f4dd9b9-0391-453a-8cbb-67347b63ebee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#pprint(catalogue)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, product \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[43mcatalogue\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mproducts\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatasets\u001b[39m\u001b[38;5;124m'\u001b[39m]):\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;28mprint\u001b[39m( index,product[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdataset_id\u001b[39m\u001b[38;5;124m'\u001b[39m] )\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28mprint\u001b[39m(index)\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "#pprint(catalogue)\n",
    "for index, product in enumerate(catalogue['products'][2]['datasets']):\n",
    "    print( index,product['dataset_id'] )\n",
    "    print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd767c1-2789-414e-a917-cf002550a659",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set parameters\n",
    "data_request = {\n",
    "   \"dataset_id_sst_gap_l3s\" : \"cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m\",\n",
    "\n",
    "}\n",
    "\n",
    "# Load xarray dataset\n",
    "ibi = copernicusmarine.open_dataset(\n",
    "    dataset_id = \"cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63dfd181-278c-4efb-81ef-e460292a4231",
   "metadata": {},
   "outputs": [],
   "source": [
    "nws = copernicusmarine.open_dataset(\n",
    "    dataset_id = \"cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m\"##data_request[\"dataset_id_sst_gap_l3s\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "91d991c0-d1a8-4f31-814d-ba4fed8c7017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (depth: 49, latitude: 1014, longitude: 840, time: 7488)\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494 1.541 2.646 ... 4.833e+03 5.275e+03\n",
       "  * latitude   (latitude) float32 27.47 27.5 27.53 27.56 ... 55.56 55.58 55.61\n",
       "  * longitude  (longitude) float32 -18.56 -18.53 -18.5 ... 4.695 4.723 4.751\n",
       "  * time       (time) datetime64[ns] 2023-03-30 ... 2024-02-04T23:00:00\n",
       "Data variables:\n",
       "    so         (time, depth, latitude, longitude) float32 ...\n",
       "    thetao     (time, depth, latitude, longitude) float32 ...\n",
       "    uo         (time, depth, latitude, longitude) float32 ...\n",
       "    vo         (time, depth, latitude, longitude) float32 ...\n",
       "Attributes: (12/21)\n",
       "    Conventions:     CF-1.0\n",
       "    bulletin_date:   2023-03-31\n",
       "    bulletin_type:   Operational\n",
       "    contact:         mailto: servicedesk.cmems@mercator-ocean.eu\n",
       "    domain_name:     IBI36\n",
       "    easting:         longitude\n",
       "    ...              ...\n",
       "    northing:        latitude\n",
       "    references:      http://marine.copernicus.eu\n",
       "    source:          NEMO3.6\n",
       "    title:           Ocean 3D IBI hourly mean fields\n",
       "    z_max:           5274.7841796875\n",
       "    z_min:           0.49399998784065247</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1895bc33-bbd8-49cd-b9f9-3d723fe78887' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1895bc33-bbd8-49cd-b9f9-3d723fe78887' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>depth</span>: 49</li><li><span class='xr-has-index'>latitude</span>: 1014</li><li><span class='xr-has-index'>longitude</span>: 840</li><li><span class='xr-has-index'>time</span>: 7488</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-da268ffe-cfa0-43e5-9c69-93d143b117aa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da268ffe-cfa0-43e5-9c69-93d143b117aa' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.494 1.541 ... 4.833e+03 5.275e+03</div><input id='attrs-efb3a859-cfe5-4574-952b-9c33c2e83444' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efb3a859-cfe5-4574-952b-9c33c2e83444' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61b2ad1d-2988-40a6-b5ad-5a9cb91039ae' class='xr-var-data-in' type='checkbox'><label for='data-61b2ad1d-2988-40a6-b5ad-5a9cb91039ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0.494</dd><dt><span>valid_max :</span></dt><dd>5274.784</dd></dl></div><div class='xr-var-data'><pre>array([4.940000e-01, 1.541000e+00, 2.646000e+00, 3.819000e+00, 5.078000e+00,\n",
       "       6.441000e+00, 7.930000e+00, 9.573000e+00, 1.140500e+01, 1.346700e+01,\n",
       "       1.581000e+01, 1.849600e+01, 2.159900e+01, 2.521100e+01, 2.944500e+01,\n",
       "       3.443400e+01, 4.034400e+01, 4.737400e+01, 5.576400e+01, 6.580700e+01,\n",
       "       7.785400e+01, 9.232600e+01, 1.097290e+02, 1.306660e+02, 1.558510e+02,\n",
       "       1.861260e+02, 2.224750e+02, 2.660400e+02, 3.181270e+02, 3.802130e+02,\n",
       "       4.539380e+02, 5.410890e+02, 6.435670e+02, 7.633330e+02, 9.023390e+02,\n",
       "       1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,\n",
       "       2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,\n",
       "       3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>27.47 27.5 27.53 ... 55.58 55.61</div><input id='attrs-e3251fc5-4187-4728-8dba-5746309ff4fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3251fc5-4187-4728-8dba-5746309ff4fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c0466c2-fbdf-400f-b3e0-585ef02ef0cf' class='xr-var-data-in' type='checkbox'><label for='data-2c0466c2-fbdf-400f-b3e0-585ef02ef0cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>step :</span></dt><dd>0.02777862921357155</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>27.47227</dd><dt><span>valid_max :</span></dt><dd>55.61202</dd></dl></div><div class='xr-var-data'><pre>array([27.47227, 27.50005, 27.52783, ..., 55.55646, 55.58424, 55.61202],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-18.56 -18.53 -18.5 ... 4.723 4.751</div><input id='attrs-f77f195d-02d3-4f93-a855-d90fc639e0de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f77f195d-02d3-4f93-a855-d90fc639e0de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e38f2e0f-ca50-4ade-b938-1ba6aa0c979e' class='xr-var-data-in' type='checkbox'><label for='data-e38f2e0f-ca50-4ade-b938-1ba6aa0c979e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>step :</span></dt><dd>0.02777862921357155</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-18.55554</dd><dt><span>valid_max :</span></dt><dd>4.75073</dd></dl></div><div class='xr-var-data'><pre>array([-18.55554, -18.52776, -18.49998, ...,   4.69517,   4.72295,   4.75073],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-03-30 ... 2024-02-04T23:00:00</div><input id='attrs-0747be4a-46e5-4eca-838c-451785613992' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0747be4a-46e5-4eca-838c-451785613992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-762307f8-0266-4be7-9cb1-7c5e7807ac1f' class='xr-var-data-in' type='checkbox'><label for='data-762307f8-0266-4be7-9cb1-7c5e7807ac1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>2023-03-30T00:00:00.000000000</dd><dt><span>valid_max :</span></dt><dd>2024-02-04T23:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-03-30T00:00:00.000000000&#x27;, &#x27;2023-03-30T01:00:00.000000000&#x27;,\n",
       "       &#x27;2023-03-30T02:00:00.000000000&#x27;, ..., &#x27;2024-02-04T21:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-04T22:00:00.000000000&#x27;, &#x27;2024-02-04T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9ebcbf2-a5fc-451c-90fd-e1d4ea6b3665' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9ebcbf2-a5fc-451c-90fd-e1d4ea6b3665' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>so</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-025873e0-1163-4a58-821b-44a220d82e5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-025873e0-1163-4a58-821b-44a220d82e5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ade6e37-538d-465c-b429-a6c1a8da7a1c' class='xr-var-data-in' type='checkbox'><label for='data-2ade6e37-538d-465c-b429-a6c1a8da7a1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_max :</span></dt><dd>20000</dd><dt><span>valid_min :</span></dt><dd>-20000</dd></dl></div><div class='xr-var-data'><pre>[312520965120 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac7691e1-6538-4eda-9424-36368cab2119' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac7691e1-6538-4eda-9424-36368cab2119' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c1a7dc1-0c9c-45a1-aea3-91e5b89eed73' class='xr-var-data-in' type='checkbox'><label for='data-6c1a7dc1-0c9c-45a1-aea3-91e5b89eed73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>degrees_C</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_max :</span></dt><dd>22000</dd><dt><span>valid_min :</span></dt><dd>-12000</dd></dl></div><div class='xr-var-data'><pre>[312520965120 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01ff395b-11af-4691-9224-9d4151f05420' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01ff395b-11af-4691-9224-9d4151f05420' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc641025-ae8a-4c63-987c-14e28334b919' class='xr-var-data-in' type='checkbox'><label for='data-bc641025-ae8a-4c63-987c-14e28334b919' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Eastward velocity</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>3000</dd><dt><span>valid_min :</span></dt><dd>-3000</dd></dl></div><div class='xr-var-data'><pre>[312520965120 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4c0f0d0-2615-4864-91b0-21ae89ef441c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4c0f0d0-2615-4864-91b0-21ae89ef441c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37b3597e-c5de-48eb-acd6-e2cd040eb44c' class='xr-var-data-in' type='checkbox'><label for='data-37b3597e-c5de-48eb-acd6-e2cd040eb44c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Northward velocity</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>3000</dd><dt><span>valid_min :</span></dt><dd>-3000</dd></dl></div><div class='xr-var-data'><pre>[312520965120 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56e54411-c730-495e-b369-ba640a2fea26' class='xr-section-summary-in' type='checkbox'  ><label for='section-56e54411-c730-495e-b369-ba640a2fea26' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3b9c8434-2f2a-4c6e-ba5f-228ac8e1914e' class='xr-index-data-in' type='checkbox'/><label for='index-3b9c8434-2f2a-4c6e-ba5f-228ac8e1914e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.49399998784065247,  1.5410000085830688,  2.6459999084472656,\n",
       "         3.819000005722046,   5.078000068664551,   6.440999984741211,\n",
       "         7.929999828338623,   9.572999954223633,  11.404999732971191,\n",
       "        13.467000007629395,    15.8100004196167,  18.496000289916992,\n",
       "        21.599000930786133,  25.211000442504883,   29.44499969482422,\n",
       "        34.433998107910156,   40.34400177001953,  47.374000549316406,\n",
       "        55.763999938964844,   65.80699920654297,   77.85399627685547,\n",
       "         92.32599639892578,  109.72899627685547,  130.66600036621094,\n",
       "         155.8509979248047,  186.12600708007812,  222.47500610351562,\n",
       "         266.0400085449219,  318.12701416015625,   380.2130126953125,\n",
       "           453.93798828125,   541.0889892578125,   643.5670166015625,\n",
       "            763.3330078125,   902.3389892578125,    1062.43994140625,\n",
       "            1245.291015625,     1452.2509765625,  1684.2840576171875,\n",
       "        1941.8929443359375,   2225.077880859375,        2533.3359375,\n",
       "         2865.702880859375,   3220.820068359375,   3597.031982421875,\n",
       "          3992.48388671875,    4405.22412109375,      4833.291015625,\n",
       "           5274.7841796875],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dc111969-d33a-4d31-9531-b21aae7ea3eb' class='xr-index-data-in' type='checkbox'/><label for='index-dc111969-d33a-4d31-9531-b21aae7ea3eb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 27.47226905822754, 27.500049591064453, 27.527830123901367,\n",
       "        27.55561065673828,   27.5833797454834, 27.611160278320312,\n",
       "       27.638940811157227, 27.666719436645508, 27.694499969482422,\n",
       "       27.722280502319336,\n",
       "       ...\n",
       "        55.36201095581055,  55.38978958129883,  55.41756820678711,\n",
       "       55.445350646972656,  55.47312927246094, 55.500911712646484,\n",
       "       55.528690338134766, 55.556461334228516,   55.5842399597168,\n",
       "        55.61201858520508],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=1014))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-14531d35-f532-4e32-b625-9a0899c72387' class='xr-index-data-in' type='checkbox'/><label for='index-14531d35-f532-4e32-b625-9a0899c72387' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-18.555540084838867, -18.527759552001953, -18.499980926513672,\n",
       "       -18.472200393676758, -18.444429397583008, -18.416650772094727,\n",
       "       -18.388870239257812,   -18.3610897064209, -18.333309173583984,\n",
       "       -18.305530548095703,\n",
       "       ...\n",
       "         4.500720024108887,   4.528500080108643,   4.556280136108398,\n",
       "         4.584060192108154,   4.611839771270752,   4.639619827270508,\n",
       "         4.667389869689941,   4.695169925689697,   4.722949981689453,\n",
       "         4.750730037689209],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=840))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-da204597-ad29-4473-9e86-89dda33f0237' class='xr-index-data-in' type='checkbox'/><label for='index-da204597-ad29-4473-9e86-89dda33f0237' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-03-30 00:00:00&#x27;, &#x27;2023-03-30 01:00:00&#x27;,\n",
       "               &#x27;2023-03-30 02:00:00&#x27;, &#x27;2023-03-30 03:00:00&#x27;,\n",
       "               &#x27;2023-03-30 04:00:00&#x27;, &#x27;2023-03-30 05:00:00&#x27;,\n",
       "               &#x27;2023-03-30 06:00:00&#x27;, &#x27;2023-03-30 07:00:00&#x27;,\n",
       "               &#x27;2023-03-30 08:00:00&#x27;, &#x27;2023-03-30 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-02-04 14:00:00&#x27;, &#x27;2024-02-04 15:00:00&#x27;,\n",
       "               &#x27;2024-02-04 16:00:00&#x27;, &#x27;2024-02-04 17:00:00&#x27;,\n",
       "               &#x27;2024-02-04 18:00:00&#x27;, &#x27;2024-02-04 19:00:00&#x27;,\n",
       "               &#x27;2024-02-04 20:00:00&#x27;, &#x27;2024-02-04 21:00:00&#x27;,\n",
       "               &#x27;2024-02-04 22:00:00&#x27;, &#x27;2024-02-04 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=7488, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9bbb0396-c577-4982-82f8-ca8148bac135' class='xr-section-summary-in' type='checkbox'  ><label for='section-9bbb0396-c577-4982-82f8-ca8148bac135' class='xr-section-summary' >Attributes: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>bulletin_date :</span></dt><dd>2023-03-31</dd><dt><span>bulletin_type :</span></dt><dd>Operational</dd><dt><span>contact :</span></dt><dd>mailto: servicedesk.cmems@mercator-ocean.eu</dd><dt><span>domain_name :</span></dt><dd>IBI36</dd><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>field_date :</span></dt><dd>20230330</dd><dt><span>field_type :</span></dt><dd>mean</dd><dt><span>forecast_range :</span></dt><dd>01-day_hindcast</dd><dt><span>forecast_type :</span></dt><dd>hindcast</dd><dt><span>institution :</span></dt><dd>Nologin (Spain)</dd><dt><span>latitude_max :</span></dt><dd>55.639801025390625</dd><dt><span>latitude_min :</span></dt><dd>27.47226905822754</dd><dt><span>longitude_max :</span></dt><dd>4.778507232666016</dd><dt><span>longitude_min :</span></dt><dd>-18.555540084838867</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>source :</span></dt><dd>NEMO3.6</dd><dt><span>title :</span></dt><dd>Ocean 3D IBI hourly mean fields</dd><dt><span>z_max :</span></dt><dd>5274.7841796875</dd><dt><span>z_min :</span></dt><dd>0.49399998784065247</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (depth: 49, latitude: 1014, longitude: 840, time: 7488)\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494 1.541 2.646 ... 4.833e+03 5.275e+03\n",
       "  * latitude   (latitude) float32 27.47 27.5 27.53 27.56 ... 55.56 55.58 55.61\n",
       "  * longitude  (longitude) float32 -18.56 -18.53 -18.5 ... 4.695 4.723 4.751\n",
       "  * time       (time) datetime64[ns] 2023-03-30 ... 2024-02-04T23:00:00\n",
       "Data variables:\n",
       "    so         (time, depth, latitude, longitude) float32 ...\n",
       "    thetao     (time, depth, latitude, longitude) float32 ...\n",
       "    uo         (time, depth, latitude, longitude) float32 ...\n",
       "    vo         (time, depth, latitude, longitude) float32 ...\n",
       "Attributes: (12/21)\n",
       "    Conventions:     CF-1.0\n",
       "    bulletin_date:   2023-03-31\n",
       "    bulletin_type:   Operational\n",
       "    contact:         mailto: servicedesk.cmems@mercator-ocean.eu\n",
       "    domain_name:     IBI36\n",
       "    easting:         longitude\n",
       "    ...              ...\n",
       "    northing:        latitude\n",
       "    references:      http://marine.copernicus.eu\n",
       "    source:          NEMO3.6\n",
       "    title:           Ocean 3D IBI hourly mean fields\n",
       "    z_max:           5274.7841796875\n",
       "    z_min:           0.49399998784065247"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibi#.time.isel(time=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4630acbb-3b73-4ab8-952a-c7830a424ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;thetao&#x27; (time: 7488, depth: 49, latitude: 1014,\n",
       "                            longitude: 840)&gt;\n",
       "[312520965120 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494 1.541 2.646 ... 4.833e+03 5.275e+03\n",
       "  * latitude   (latitude) float32 27.47 27.5 27.53 27.56 ... 55.56 55.58 55.61\n",
       "  * longitude  (longitude) float32 -18.56 -18.53 -18.5 ... 4.695 4.723 4.751\n",
       "  * time       (time) datetime64[ns] 2023-03-30 ... 2024-02-04T23:00:00\n",
       "Attributes:\n",
       "    long_name:      Temperature\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    unit_long:      degrees_C\n",
       "    units:          degrees_C\n",
       "    valid_max:      22000\n",
       "    valid_min:      -12000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'thetao'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7488</li><li><span class='xr-has-index'>depth</span>: 49</li><li><span class='xr-has-index'>latitude</span>: 1014</li><li><span class='xr-has-index'>longitude</span>: 840</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d1a4eb8c-6d09-4149-8a25-1b919816e975' class='xr-array-in' type='checkbox' checked><label for='section-d1a4eb8c-6d09-4149-8a25-1b919816e975' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[312520965120 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-459c0256-8122-4881-aca1-461c1b8baa22' class='xr-section-summary-in' type='checkbox'  checked><label for='section-459c0256-8122-4881-aca1-461c1b8baa22' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.494 1.541 ... 4.833e+03 5.275e+03</div><input id='attrs-39448574-1380-455d-8a2f-7505c47e6ca6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39448574-1380-455d-8a2f-7505c47e6ca6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b9347e8-5c5b-4b15-b98a-b126a0500c27' class='xr-var-data-in' type='checkbox'><label for='data-9b9347e8-5c5b-4b15-b98a-b126a0500c27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0.494</dd><dt><span>valid_max :</span></dt><dd>5274.784</dd></dl></div><div class='xr-var-data'><pre>array([4.940000e-01, 1.541000e+00, 2.646000e+00, 3.819000e+00, 5.078000e+00,\n",
       "       6.441000e+00, 7.930000e+00, 9.573000e+00, 1.140500e+01, 1.346700e+01,\n",
       "       1.581000e+01, 1.849600e+01, 2.159900e+01, 2.521100e+01, 2.944500e+01,\n",
       "       3.443400e+01, 4.034400e+01, 4.737400e+01, 5.576400e+01, 6.580700e+01,\n",
       "       7.785400e+01, 9.232600e+01, 1.097290e+02, 1.306660e+02, 1.558510e+02,\n",
       "       1.861260e+02, 2.224750e+02, 2.660400e+02, 3.181270e+02, 3.802130e+02,\n",
       "       4.539380e+02, 5.410890e+02, 6.435670e+02, 7.633330e+02, 9.023390e+02,\n",
       "       1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,\n",
       "       2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,\n",
       "       3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>27.47 27.5 27.53 ... 55.58 55.61</div><input id='attrs-b62d0680-26cc-4bbb-b03d-92ea38ad2672' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b62d0680-26cc-4bbb-b03d-92ea38ad2672' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2b4cad4-202d-4577-ab91-057820117418' class='xr-var-data-in' type='checkbox'><label for='data-e2b4cad4-202d-4577-ab91-057820117418' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>step :</span></dt><dd>0.02777862921357155</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>27.47227</dd><dt><span>valid_max :</span></dt><dd>55.61202</dd></dl></div><div class='xr-var-data'><pre>array([27.47227, 27.50005, 27.52783, ..., 55.55646, 55.58424, 55.61202],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-18.56 -18.53 -18.5 ... 4.723 4.751</div><input id='attrs-4b25f043-2f2e-4d8c-aeb7-b1b40cdcdd8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b25f043-2f2e-4d8c-aeb7-b1b40cdcdd8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28a7e7e4-b7fd-4885-8f10-4077e9949949' class='xr-var-data-in' type='checkbox'><label for='data-28a7e7e4-b7fd-4885-8f10-4077e9949949' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>step :</span></dt><dd>0.02777862921357155</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-18.55554</dd><dt><span>valid_max :</span></dt><dd>4.75073</dd></dl></div><div class='xr-var-data'><pre>array([-18.55554, -18.52776, -18.49998, ...,   4.69517,   4.72295,   4.75073],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-03-30 ... 2024-02-04T23:00:00</div><input id='attrs-2659ec7e-b9fc-4a72-9d1b-41fc0f3a59ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2659ec7e-b9fc-4a72-9d1b-41fc0f3a59ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec998d5e-2faa-4b5c-9054-af9f8afaefd6' class='xr-var-data-in' type='checkbox'><label for='data-ec998d5e-2faa-4b5c-9054-af9f8afaefd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>2023-03-30T00:00:00.000000000</dd><dt><span>valid_max :</span></dt><dd>2024-02-04T23:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-03-30T00:00:00.000000000&#x27;, &#x27;2023-03-30T01:00:00.000000000&#x27;,\n",
       "       &#x27;2023-03-30T02:00:00.000000000&#x27;, ..., &#x27;2024-02-04T21:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-04T22:00:00.000000000&#x27;, &#x27;2024-02-04T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44ff154f-8669-474a-8d2d-6b7d442d13ca' class='xr-section-summary-in' type='checkbox'  ><label for='section-44ff154f-8669-474a-8d2d-6b7d442d13ca' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff7f2416-ece0-46b4-a318-ef277deb81b5' class='xr-index-data-in' type='checkbox'/><label for='index-ff7f2416-ece0-46b4-a318-ef277deb81b5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.49399998784065247,  1.5410000085830688,  2.6459999084472656,\n",
       "         3.819000005722046,   5.078000068664551,   6.440999984741211,\n",
       "         7.929999828338623,   9.572999954223633,  11.404999732971191,\n",
       "        13.467000007629395,    15.8100004196167,  18.496000289916992,\n",
       "        21.599000930786133,  25.211000442504883,   29.44499969482422,\n",
       "        34.433998107910156,   40.34400177001953,  47.374000549316406,\n",
       "        55.763999938964844,   65.80699920654297,   77.85399627685547,\n",
       "         92.32599639892578,  109.72899627685547,  130.66600036621094,\n",
       "         155.8509979248047,  186.12600708007812,  222.47500610351562,\n",
       "         266.0400085449219,  318.12701416015625,   380.2130126953125,\n",
       "           453.93798828125,   541.0889892578125,   643.5670166015625,\n",
       "            763.3330078125,   902.3389892578125,    1062.43994140625,\n",
       "            1245.291015625,     1452.2509765625,  1684.2840576171875,\n",
       "        1941.8929443359375,   2225.077880859375,        2533.3359375,\n",
       "         2865.702880859375,   3220.820068359375,   3597.031982421875,\n",
       "          3992.48388671875,    4405.22412109375,      4833.291015625,\n",
       "           5274.7841796875],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8c74659-5efd-4a29-bacf-1fb1f7e8a254' class='xr-index-data-in' type='checkbox'/><label for='index-f8c74659-5efd-4a29-bacf-1fb1f7e8a254' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 27.47226905822754, 27.500049591064453, 27.527830123901367,\n",
       "        27.55561065673828,   27.5833797454834, 27.611160278320312,\n",
       "       27.638940811157227, 27.666719436645508, 27.694499969482422,\n",
       "       27.722280502319336,\n",
       "       ...\n",
       "        55.36201095581055,  55.38978958129883,  55.41756820678711,\n",
       "       55.445350646972656,  55.47312927246094, 55.500911712646484,\n",
       "       55.528690338134766, 55.556461334228516,   55.5842399597168,\n",
       "        55.61201858520508],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=1014))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9a1aba3-0d69-4831-8614-72062daa71d3' class='xr-index-data-in' type='checkbox'/><label for='index-c9a1aba3-0d69-4831-8614-72062daa71d3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-18.555540084838867, -18.527759552001953, -18.499980926513672,\n",
       "       -18.472200393676758, -18.444429397583008, -18.416650772094727,\n",
       "       -18.388870239257812,   -18.3610897064209, -18.333309173583984,\n",
       "       -18.305530548095703,\n",
       "       ...\n",
       "         4.500720024108887,   4.528500080108643,   4.556280136108398,\n",
       "         4.584060192108154,   4.611839771270752,   4.639619827270508,\n",
       "         4.667389869689941,   4.695169925689697,   4.722949981689453,\n",
       "         4.750730037689209],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=840))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6f42f02c-ca8f-496f-8cdd-869a55370d46' class='xr-index-data-in' type='checkbox'/><label for='index-6f42f02c-ca8f-496f-8cdd-869a55370d46' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-03-30 00:00:00&#x27;, &#x27;2023-03-30 01:00:00&#x27;,\n",
       "               &#x27;2023-03-30 02:00:00&#x27;, &#x27;2023-03-30 03:00:00&#x27;,\n",
       "               &#x27;2023-03-30 04:00:00&#x27;, &#x27;2023-03-30 05:00:00&#x27;,\n",
       "               &#x27;2023-03-30 06:00:00&#x27;, &#x27;2023-03-30 07:00:00&#x27;,\n",
       "               &#x27;2023-03-30 08:00:00&#x27;, &#x27;2023-03-30 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-02-04 14:00:00&#x27;, &#x27;2024-02-04 15:00:00&#x27;,\n",
       "               &#x27;2024-02-04 16:00:00&#x27;, &#x27;2024-02-04 17:00:00&#x27;,\n",
       "               &#x27;2024-02-04 18:00:00&#x27;, &#x27;2024-02-04 19:00:00&#x27;,\n",
       "               &#x27;2024-02-04 20:00:00&#x27;, &#x27;2024-02-04 21:00:00&#x27;,\n",
       "               &#x27;2024-02-04 22:00:00&#x27;, &#x27;2024-02-04 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=7488, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90535b3f-a94a-4225-8e94-4908b8ff21c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90535b3f-a94a-4225-8e94-4908b8ff21c9' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>degrees_C</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_max :</span></dt><dd>22000</dd><dt><span>valid_min :</span></dt><dd>-12000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'thetao' (time: 7488, depth: 49, latitude: 1014,\n",
       "                            longitude: 840)>\n",
       "[312520965120 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494 1.541 2.646 ... 4.833e+03 5.275e+03\n",
       "  * latitude   (latitude) float32 27.47 27.5 27.53 27.56 ... 55.56 55.58 55.61\n",
       "  * longitude  (longitude) float32 -18.56 -18.53 -18.5 ... 4.695 4.723 4.751\n",
       "  * time       (time) datetime64[ns] 2023-03-30 ... 2024-02-04T23:00:00\n",
       "Attributes:\n",
       "    long_name:      Temperature\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    unit_long:      degrees_C\n",
       "    units:          degrees_C\n",
       "    valid_max:      22000\n",
       "    valid_min:      -12000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ibi.thetao#.sel(time='2023-07-03 T00')#.sel(depth=0)#.plot()#.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "59fe5375-4dde-48a4-b25c-9dbbfe990462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;thetao&#x27; (depth: 50, latitude: 551, longitude: 936)&gt;\n",
       "[25786800 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * latitude   (latitude) float32 46.0 46.03 46.06 46.09 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "    time       datetime64[ns] 2023-07-03\n",
       "Attributes: (12/14)\n",
       "    easting:        longitude\n",
       "    latitude_max:   61.2819f\n",
       "    latitude_min:   46.0036f\n",
       "    long_name:      Temperature\n",
       "    longitude_max:  9.977f\n",
       "    longitude_min:  -15.996f\n",
       "    ...             ...\n",
       "    unit_long:      degrees_C\n",
       "    units:          degrees_C\n",
       "    valid_max:      22000\n",
       "    valid_min:      -12000\n",
       "    z_max:          5727.917f\n",
       "    z_min:          0.49402f</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'thetao'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>depth</span>: 50</li><li><span class='xr-has-index'>latitude</span>: 551</li><li><span class='xr-has-index'>longitude</span>: 936</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7d1b9a42-a1f3-466c-83c8-5dc4e821f9f3' class='xr-array-in' type='checkbox' checked><label for='section-7d1b9a42-a1f3-466c-83c8-5dc4e821f9f3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[25786800 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-b4290daa-c844-4053-af50-52621288bb46' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4290daa-c844-4053-af50-52621288bb46' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.494 1.541 ... 5.275e+03 5.728e+03</div><input id='attrs-18ecfc14-bed2-424d-9979-babb1080df3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18ecfc14-bed2-424d-9979-babb1080df3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0778771c-5e29-4e63-bf62-4fd52ae796e1' class='xr-var-data-in' type='checkbox'><label for='data-0778771c-5e29-4e63-bf62-4fd52ae796e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([4.940254e-01, 1.541375e+00, 2.645669e+00, 3.819495e+00, 5.078224e+00,\n",
       "       6.440614e+00, 7.929560e+00, 9.572997e+00, 1.140500e+01, 1.346714e+01,\n",
       "       1.581007e+01, 1.849556e+01, 2.159882e+01, 2.521141e+01, 2.944473e+01,\n",
       "       3.443415e+01, 4.034405e+01, 4.737369e+01, 5.576429e+01, 6.580727e+01,\n",
       "       7.785385e+01, 9.232607e+01, 1.097293e+02, 1.306660e+02, 1.558507e+02,\n",
       "       1.861256e+02, 2.224752e+02, 2.660403e+02, 3.181274e+02, 3.802130e+02,\n",
       "       4.539377e+02, 5.410889e+02, 6.435668e+02, 7.633331e+02, 9.023393e+02,\n",
       "       1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,\n",
       "       2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,\n",
       "       3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03, 5.727917e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>46.0 46.03 46.06 ... 61.25 61.28</div><input id='attrs-de5d7db8-acd7-4457-9ebe-3826992ef58c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de5d7db8-acd7-4457-9ebe-3826992ef58c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb13bbfe-8597-4aa0-9021-aae4bf8806d5' class='xr-var-data-in' type='checkbox'><label for='data-fb13bbfe-8597-4aa0-9021-aae4bf8806d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>step :</span></dt><dd>0.02777863f</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([46.003643, 46.03142 , 46.0592  , ..., 61.22633 , 61.25411 , 61.281887],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-16.0 -15.97 -15.94 ... 9.949 9.977</div><input id='attrs-ce40743b-8367-42bc-822b-643cab2d96bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce40743b-8367-42bc-822b-643cab2d96bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d71bb5f-204d-4f2d-98a1-d5f33cdb0de5' class='xr-var-data-in' type='checkbox'><label for='data-4d71bb5f-204d-4f2d-98a1-d5f33cdb0de5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>step :</span></dt><dd>0.02777863f</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-15.996015, -15.968236, -15.940457, ...,   9.921447,   9.949225,\n",
       "         9.977004], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-07-03</div><input id='attrs-cbe9953d-8d27-4192-9ac1-87faf3318c94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbe9953d-8d27-4192-9ac1-87faf3318c94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05227ee4-d564-4c7f-b842-0fef3b19c04f' class='xr-var-data-in' type='checkbox'><label for='data-05227ee4-d564-4c7f-b842-0fef3b19c04f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2023-07-03T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d74ee0b-4d5e-4e32-a1b3-b64158e10445' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d74ee0b-4d5e-4e32-a1b3-b64158e10445' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95dd959e-f024-4407-ba15-22f9d059639a' class='xr-index-data-in' type='checkbox'/><label for='index-95dd959e-f024-4407-ba15-22f9d059639a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.4940253794193268, 1.5413753986358643, 2.6456685066223145,\n",
       "       3.8194947242736816,  5.078223705291748,  6.440614223480225,\n",
       "         7.92956018447876,  9.572997093200684,  11.40500259399414,\n",
       "       13.467138290405273, 15.810072898864746, 18.495559692382812,\n",
       "        21.59881591796875, 25.211408615112305,  29.44472885131836,\n",
       "        34.43415451049805, 40.344051361083984, 47.373687744140625,\n",
       "        55.76428985595703,   65.8072738647461,  77.85385131835938,\n",
       "         92.3260726928711, 109.72927856445312, 130.66598510742188,\n",
       "       155.85072326660156,  186.1255645751953,  222.4751739501953,\n",
       "        266.0402526855469,    318.12744140625,  380.2130126953125,\n",
       "         453.937744140625,  541.0889282226562,  643.5668334960938,\n",
       "        763.3330688476562,  902.3392944335938,  1062.439697265625,\n",
       "       1245.2911376953125,    1452.2509765625,  1684.284423828125,\n",
       "       1941.8934326171875,  2225.077880859375,  2533.336181640625,\n",
       "         2865.70263671875,       3220.8203125,  3597.031982421875,\n",
       "         3992.48388671875,   4405.22412109375,   4833.29052734375,\n",
       "          5274.7841796875,   5727.91650390625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-815093a6-be20-49a9-8a79-44aaffd455d9' class='xr-index-data-in' type='checkbox'/><label for='index-815093a6-be20-49a9-8a79-44aaffd455d9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 46.00364303588867,  46.03142166137695, 46.059200286865234,\n",
       "       46.086978912353516,   46.1147575378418,  46.14253616333008,\n",
       "        46.17031478881836,  46.19809341430664,  46.22587203979492,\n",
       "         46.2536506652832,\n",
       "       ...\n",
       "        61.03187942504883,  61.05965805053711,  61.08743667602539,\n",
       "        61.11521530151367,  61.14299392700195, 61.170772552490234,\n",
       "       61.198551177978516,   61.2263298034668,  61.25410842895508,\n",
       "        61.28188705444336],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=551))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8168767d-7184-4fcf-a70f-324875ea1d70' class='xr-index-data-in' type='checkbox'/><label for='index-8168767d-7184-4fcf-a70f-324875ea1d70' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-15.996014595031738, -15.968235969543457, -15.940457344055176,\n",
       "       -15.912678718566895, -15.884900093078613, -15.857121467590332,\n",
       "        -15.82934284210205,  -15.80156421661377, -15.773785591125488,\n",
       "       -15.746006965637207,\n",
       "       ...\n",
       "         9.726996421813965,   9.754775047302246,   9.782553672790527,\n",
       "         9.810332298278809,    9.83811092376709,   9.865889549255371,\n",
       "         9.893668174743652,   9.921446800231934,   9.949225425720215,\n",
       "         9.977004051208496],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=936))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f1a921a-1169-44fd-b394-831ae38c5c56' class='xr-section-summary-in' type='checkbox'  ><label for='section-5f1a921a-1169-44fd-b394-831ae38c5c56' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>latitude_max :</span></dt><dd>61.2819f</dd><dt><span>latitude_min :</span></dt><dd>46.0036f</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>longitude_max :</span></dt><dd>9.977f</dd><dt><span>longitude_min :</span></dt><dd>-15.996f</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>degrees_C</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_max :</span></dt><dd>22000</dd><dt><span>valid_min :</span></dt><dd>-12000</dd><dt><span>z_max :</span></dt><dd>5727.917f</dd><dt><span>z_min :</span></dt><dd>0.49402f</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'thetao' (depth: 50, latitude: 551, longitude: 936)>\n",
       "[25786800 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * latitude   (latitude) float32 46.0 46.03 46.06 46.09 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "    time       datetime64[ns] 2023-07-03\n",
       "Attributes: (12/14)\n",
       "    easting:        longitude\n",
       "    latitude_max:   61.2819f\n",
       "    latitude_min:   46.0036f\n",
       "    long_name:      Temperature\n",
       "    longitude_max:  9.977f\n",
       "    longitude_min:  -15.996f\n",
       "    ...             ...\n",
       "    unit_long:      degrees_C\n",
       "    units:          degrees_C\n",
       "    valid_max:      22000\n",
       "    valid_min:      -12000\n",
       "    z_max:          5727.917f\n",
       "    z_min:          0.49402f"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nws.thetao.sel(time='2023-07-03 T00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6c01778-1e70-4802-beeb-44335e95b36f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}