implementation_lwp_retrieval_modifNC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-20 12:10:57.311765: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-03-20 12:10:57.311790: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import sys\n",
    "from keras.models import load_model\n",
    "from netCDF4 import Dataset\n",
    "import os\n",
    "from tools import Ps_to_sea_level, Ps_to_sea_level_old, load_ERA_variables, load_WProf_variables\n",
    "from scipy import interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GEO = True\n",
    "ERA = True\n",
    "SURF = True\n",
    "\n",
    "ALTITUDE = 1340\n",
    "LATITUDE = -71.9\n",
    "LONGITUDE = 23.3\n",
    "\n",
    "PLOT = True\n",
    "MODIFY_NC = True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Function which defines the corresponding dataframe and runs the retrieval model\n",
    "## Note: this uses some of the pre-defined fields!!\n",
    "def predict(lwp_or_pwv):\n",
    "    if (not GEO) and ERA and SURF:\n",
    "        df = pd.DataFrame({'Ts':Ts,'Ps':Ps,'RHs':RHs, 'LWPera':LWPera, 'PWVera':PWVera, 'TB':TB, 'TB2': TB**2})\n",
    "        df = df.reindex(columns=['LWPera','PWVera','Ps','RHs','TB','Ts','TB2'])\n",
    "        mean = np.load('parameters/ERA_noGeo_Surf/mean_for_%s.npy'%lwp_or_pwv)\n",
    "        std = np.load('parameters/ERA_noGeo_Surf/std_for_%s.npy'%lwp_or_pwv)\n",
    "        model = load_model('parameters/ERA_noGeo_Surf/%smodel_ERA_noGeo_Surf_TB12'%lwp_or_pwv)\n",
    "    if GEO and ERA and SURF:\n",
    "        df = pd.DataFrame({'altitude':alt, 'latitude':lat, 'longitude':lon, 'date': date, 'Ts':Ts,'Ps':Ps,'RHs':RHs, 'LWPera':LWPera, 'PWVera':PWVera, 'TB':TB, 'TB2': TB**2})\n",
    "        df = df.reindex(columns=['LWPera','PWVera', 'Ps', 'RHs', 'TB', 'Ts', 'altitude', 'date', 'latitude', 'longitude', 'TB2'])\n",
    "        mean = np.load('parameters/ERA_Geo_Surf/mean_for_%s.npy'%lwp_or_pwv)\n",
    "        std = np.load('parameters/ERA_Geo_Surf/std_for_%s.npy'%lwp_or_pwv)\n",
    "        model = load_model('parameters/ERA_Geo_Surf/%smodel_ERA_Geo_Surf_TB12'%lwp_or_pwv)\n",
    "    if GEO and ERA and (not SURF):\n",
    "        df = pd.DataFrame({'altitude':alt, 'latitude':lat, 'longitude':lon, 'date': date,  'LWPera':LWPera, 'PWVera':PWVera, 'TB':TB, 'TB2': TB**2})\n",
    "        df = df.reindex(columns=['LWPera', 'PWVera', 'TB', 'altitude', 'date', 'latitude', 'longitude', 'TB2'])\n",
    "        mean = np.load('parameters/ERA_Geo_noSurf/mean_for_%s.npy'%lwp_or_pwv)\n",
    "        std = np.load('parameters/ERA_Geo_noSurf/std_for_%s.npy'%lwp_or_pwv)\n",
    "        model = load_model('parameters/ERA_Geo_noSurf/%smodel_ERA_Geo_noSurf_TB12'%lwp_or_pwv)\n",
    "\n",
    "    if (not GEO) and (not ERA) and SURF:\n",
    "        df = pd.DataFrame({'Ts':Ts,'Ps':Ps,'RHs':RHs, 'TB':TB, 'TB2': TB**2})\n",
    "        df = df.reindex(columns=['Ps','RHs','TB','Ts','TB2'])\n",
    "        mean = np.load('parameters/noERA_noGeo_Surf/mean_for_%s.npy'%lwp_or_pwv)\n",
    "        std = np.load('parameters/noERA_noGeo_Surf/std_for_%s.npy'%lwp_or_pwv)\n",
    "        model = load_model('parameters/noERA_noGeo_Surf/%smodel_noERA_noGeo_Surf_TB12'%lwp_or_pwv)\n",
    "    if GEO and (not ERA) and SURF:\n",
    "        df = pd.DataFrame({'altitude':alt, 'latitude':lat, 'longitude':lon, 'date': date, 'Ts':Ts,'Ps':Ps,'RHs':RHs, 'TB':TB, 'TB2': TB**2})\n",
    "        df = df.reindex(columns=['Ps', 'RHs', 'TB', 'Ts', 'altitude', 'date', 'latitude', 'longitude', 'TB2'])\n",
    "        mean = np.load('parameters/noERA_Geo_Surf/mean_for_%s.npy'%lwp_or_pwv)\n",
    "        std = np.load('parameters/noERA_Geo_Surf/std_for_%s.npy'%lwp_or_pwv)\n",
    "        model = load_model('parameters/noERA_Geo_Surf/%smodel_noERA_Geo_Surf_TB12'%lwp_or_pwv)\n",
    "    if GEO and (not ERA) and (not SURF):\n",
    "        df = pd.DataFrame({'altitude':alt, 'latitude':lat, 'longitude':lon, 'date': date,'TB':TB, 'TB2': TB**2})\n",
    "        df = df.reindex(columns=['TB', 'altitude', 'date', 'latitude', 'longitude', 'TB2'])\n",
    "        mean = np.load('parameters/noERA_Geo_noSurf/mean_for_%s.npy'%lwp_or_pwv)\n",
    "        std = np.load('parameters/noERA_Geo_noSurf/std_for_%s.npy'%lwp_or_pwv)\n",
    "        model = load_model('parameters/noERA_Geo_noSurf/%smodel_noERA_Geo_noSurf_TB12'%lwp_or_pwv)\n",
    "\n",
    "\n",
    "    # Normalize the input features\n",
    "    npdf = (df.to_numpy()-mean)/std\n",
    "    # Run the prediction\n",
    "    pred = model.predict(npdf)\n",
    "    # This ensures that there are no doubles (safety)\n",
    "    tpred,inds = np.unique(t,return_index=True)\n",
    "    pred = pred[inds]\n",
    "    \n",
    "    return (tpred,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'glob' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-ca15a19f0cbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfwps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/ltenas3/POPE_2020/WProf_w_LWP_IWV/2002*.nc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;31m#[20] #nas3/anneclaire/ICEGENESIS_2021/WProf/LV1_20210507/2101*.nc'))[:-2]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mPLOT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'glob' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "fwps = sorted(glob.glob('/ltenas3/POPE_2020/WProf_w_LWP_IWV/2002*.nc'))[:]#[20] #nas3/anneclaire/ICEGENESIS_2021/WProf/LV1_20210507/2101*.nc'))[:-2]\n",
    "\n",
    "if PLOT:\n",
    "    fig,axs = plt.subplots(2)\n",
    "\n",
    "for fwp in fwps[:]:\n",
    "    try:\n",
    "        print(fwp)\n",
    "        erapath = '/home/billault/Documents/In_progress/POPE_2020/ERA5_LWP_2020_02.nc'\n",
    "\n",
    "        (t, Ts, Ps, RHs, TB, LWPrpg) = load_WProf_variables(fwp)\n",
    "        (LWPera, PWVera) = load_ERA_variables(erapath, t)\n",
    "\n",
    "        Ps = Ps_to_sea_level(np.array(Ps), np.array(Ts), ALTITUDE)\n",
    "\n",
    "        Ts = Ts-273.15\n",
    "        Ps = Ps*1e2\n",
    "\n",
    "        daynum = []\n",
    "        for tt in t:\n",
    "            dt = datetime.datetime.utcfromtimestamp(tt)\n",
    "            yr = dt.year\n",
    "            daynum.append((dt-datetime.datetime(yr,1,1,0)).days+1+(dt-datetime.datetime(yr,1,1,0)).seconds/(24*3600))\n",
    "        date = np.array(daynum)\n",
    "\n",
    "        alt = np.zeros(len(TB))+ALTITUDE\n",
    "        lat = np.zeros(len(TB))+LATITUDE\n",
    "        lon = np.zeros(len(TB))+LONGITUDE\n",
    "\n",
    "        tpwv,PWVpred = predict('PWV')\n",
    "        tlwp,LWPpred = predict('LWP')\n",
    "\n",
    "        if PLOT:\n",
    "            axs[0].plot(tpwv,PWVpred)\n",
    "            axs[1].plot(tpwv,LWPpred)\n",
    "\n",
    "        if MODIFY_NC:\n",
    "            with Dataset(fwp,'r+') as nc:\n",
    "                if 'Liquid-water-path-RPG' in nc.variables.keys():\n",
    "                    print('already done')\n",
    "                    continue\n",
    "                    \n",
    "                nc.radar_name= 'WProf - RPG Demonstrator'\n",
    "                nc.radar_manufacturer= 'RPG-Radiometer Physics GmbH'\n",
    "                nc.radar_frequency= 94\n",
    "                nc.radar_frequency_unit= 'GHz'\n",
    "                nc.radar_type= 'Vertically pointing FMCW Doppler spectral'\n",
    "                nc.radar_latitude= LATITUDE\n",
    "                nc.radar_latitude_unit= 'degN'\n",
    "                nc.radar_longitude= LONGITUDE\n",
    "                nc.radar_longitude_unit= 'degE'\n",
    "                nc.radar_altitude= ALTITUDE\n",
    "                nc.radar_altitude_unit= 'm a.s.l.'\n",
    "                nc.liquid_water_path_and_integrated_water_vapor_computation = 'Billault-Roux, A.-C. and Berne, A.: https://doi.org/10.5194/amt-2020-311'\n",
    "\n",
    "\n",
    "                pwv = nc.createVariable('Integrated-water-vapor', 'f4',('Time',))\n",
    "                pwv[:] = PWVpred[:]\n",
    "                pwv.unit = 'kg.m^-2'\n",
    "                pwv.description = 'integrated water vapor - Billault-Roux and Berne 2021 (https://doi.org/10.5194/amt-2020-311)'\n",
    "\n",
    "                nc.renameVariable('Liquid-water-path', 'Liquid-water-path-RPG')\n",
    "                nc.variables['Liquid-water-path-RPG'].description= 'liquid water path - computed through radar built-in software'\n",
    "                nc.variables['Liquid-water-path-RPG'].unit= 'g.m^-2'\n",
    "\n",
    "                lwp = nc.createVariable('Liquid-water-path', 'f4',('Time',))\n",
    "                lwp[:] = LWPpred[:]\n",
    "                lwp.unit = 'g.m^-2'\n",
    "                lwp.description = 'liquid water path - Billault-Roux and Berne 2021 (https://doi.org/10.5194/amt-2020-311)'\n",
    "\n",
    "    except Exception as e:\n",
    "        print(fwp, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_000004_P01_ZEN_LV2.nc 11.451564\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_010004_P01_ZEN_LV2.nc 11.651341\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_020004_P01_ZEN_LV2.nc 11.837756\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_030002_P01_ZEN_LV2.nc 13.982589\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_040005_P01_ZEN_LV2.nc 17.236702\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_050004_P01_ZEN_LV2.nc 29.917408\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_060003_P01_ZEN_LV2.nc 25.087465\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_070003_P01_ZEN_LV2.nc 18.765194\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_080004_P01_ZEN_LV2.nc 15.066031\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_090005_P01_ZEN_LV2.nc 14.443489\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_100003_P01_ZEN_LV2.nc 15.476044\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_110004_P01_ZEN_LV2.nc 14.928123\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_120004_P01_ZEN_LV2.nc 15.530921\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_130002_P01_ZEN_LV2.nc 15.974454\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_140002_P01_ZEN_LV2.nc 15.860879\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_150003_P01_ZEN_LV2.nc 15.89735\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_160003_P01_ZEN_LV2.nc 15.320345\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_170000_P01_ZEN_LV2.nc 14.616974\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_180004_P01_ZEN_LV2.nc 14.410411\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_190000_P01_ZEN_LV2.nc 12.947837\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_200001_P01_ZEN_LV2.nc 13.28483\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_210002_P01_ZEN_LV2.nc 12.137188\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_220001_P01_ZEN_LV2.nc 12.206642\n",
      "/ltenas3/POPE_2020/WProf_w_LWP_IWV/191224_230001_P01_ZEN_LV2.nc 12.030571\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "for f in fwps[:]:\n",
    "    nc = Dataset(f)\n",
    "    plt.plot(nc.variables['Time'][:],nc.variables['Liquid-water-path'][:],'blue')\n",
    "    plt.plot(nc.variables['Time'][:],nc.variables['Liquid-water-path-RPG'][:],'red')\n",
    "    print(f, np.min(nc.variables['Direct-detection-brightness-temp'][:]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}