Deploy v1.6 to GitHub Pages

v1.6/_downloads/02e36c50288f0c1e7f04c9fda54da101/Mean_Pressure_Weighted.ipynb

@@ -0,0 +1,115 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Mean Pressure Weighted\n\nUse `metpy.calc.mean_pressure_weighted` as well as pint's unit support to perform calculations.\n\nThe code below uses example data from our test suite to calculate the pressure-weighted mean\ntemperature over a depth of 500 hPa.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n\nfrom metpy.calc import mean_pressure_weighted\nfrom metpy.cbook import get_test_data\nfrom metpy.units import units"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Upper air data can be obtained using the siphon package, but for this example we will use\nsome of MetPy's sample data.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Set column names\ncol_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n\n# Read in test data using col_names\ndf = pd.read_fwf(get_test_data('jan20_sounding.txt', as_file_obj=False),\n                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Drop any rows with all NaN values for T, Td, winds\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'),\n               how='all').reset_index(drop=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Isolate pressure, temperature, and height and add units\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "p = df['pressure'].values * units.hPa\nT = df['temperature'].values * units.degC\nh = df['height'].values * units.meters"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calculate the mean pressure weighted temperature over a depth of 500 hPa\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(mean_pressure_weighted(p, T, height=h, depth=500 * units.hPa))"
+      ]
+    }
+  ],
+  "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": "3.11.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

v1.6/_downloads/0bc0f944d103bfc8b7b684caf2f22481/Sounding_Calculations.py

@@ -0,0 +1,216 @@
+# Copyright (c) 2022 MetPy Developers.
+# Distributed under the terms of the BSD 3-Clause License.
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+=============================
+Sounding Calculation Examples
+=============================
+
+Use functions from `metpy.calc` to perform a number of calculations using sounding data.
+
+The code below uses example data to perform many sounding calculations for a severe weather
+event on May 22, 2011 from the Norman, OK sounding.
+"""
+import numpy as np
+import pandas as pd
+
+import metpy.calc as mpcalc
+from metpy.cbook import get_test_data
+from metpy.units import units
+
+###########################################
+# Effective Shear Algorithm for use in Supercell Composite Calculation
+
+
+def effective_layer(p, t, td, h, height_layer=False):
+    """A function that determines the effective inflow layer for a convective sounding.
+
+    Uses the default values of Thompason et al. (2004) for CAPE (100 J/kg) and CIN (-250 J/kg).
+
+    Input:
+      - p: sounding pressure with units
+      - T: sounding temperature with units
+      - Td: sounding dewpoint temperature with units
+      - h: sounding heights with units
+
+    Returns:
+      - pbot/hbot, ptop/htop: pressure/height of the bottom level,
+                              pressure/height of the top level
+    """
+    from metpy.calc import cape_cin, parcel_profile
+    from metpy.units import units
+
+    pbot = None
+
+    for i in range(p.shape[0]):
+        prof = parcel_profile(p[i:], t[i], td[i])
+        sbcape, sbcin = cape_cin(p[i:], t[i:], td[i:], prof)
+        if sbcape >= 100 * units('J/kg') and sbcin > -250 * units('J/kg'):
+            pbot = p[i]
+            hbot = h[i]
+            bot_idx = i
+            break
+    if not pbot:
+        return None, None
+
+    for i in range(bot_idx + 1, p.shape[0]):
+        prof = parcel_profile(p[i:], t[i], td[i])
+        sbcape, sbcin = cape_cin(p[i:], t[i:], td[i:], prof)
+        if sbcape < 100 * units('J/kg') or sbcin < -250 * units('J/kg'):
+            ptop = p[i]
+            htop = h[i]
+            break
+
+    if height_layer:
+        return hbot, htop
+    else:
+        return pbot, ptop
+
+
+###########################################
+# Upper air data can be obtained using the siphon package, but for this example we will use
+# some of MetPy's sample data.
+col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']
+
+df = pd.read_fwf(get_test_data('20110522_OUN_12Z.txt', as_file_obj=False),
+                 skiprows=7, usecols=[0, 1, 2, 3, 6, 7], names=col_names)
+
+# Drop any rows with all NaN values for T, Td, winds
+df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'
+                       ), how='all').reset_index(drop=True)
+
+###########################################
+# Isolate needed variables from our data file and attach units
+p = df['pressure'].values * units.hPa
+T = df['temperature'].values * units.degC
+Td = df['dewpoint'].values * units.degC
+wdir = df['direction'].values * units.degree
+sped = df['speed'].values * units.knot
+height = df['height'].values * units.meter
+
+###########################################
+# Compute needed variables from our data file and attach units
+relhum = mpcalc.relative_humidity_from_dewpoint(T, Td)
+mixrat = mpcalc.mixing_ratio_from_relative_humidity(p, T, relhum)
+
+###########################################
+# Compute the wind components
+u, v = mpcalc.wind_components(sped, wdir)
+
+###########################################
+# Compute common sounding index parameters
+ctotals = mpcalc.cross_totals(p, T, Td)
+kindex = mpcalc.k_index(p, T, Td)
+gdi = mpcalc.galvez_davison_index(p, T, mixrat, p[0])
+showalter = mpcalc.showalter_index(p, T, Td)
+total_totals = mpcalc.total_totals_index(p, T, Td)
+vert_totals = mpcalc.vertical_totals(p, T)
+
+###########################################
+# Compture the parcel profile for a surface-based parcel
+prof = mpcalc.parcel_profile(p, T[0], Td[0])
+
+###########################################
+# Compute the corresponding LI, CAPE, CIN values for a surface parcel
+lift_index = mpcalc.lifted_index(p, T, prof)
+cape, cin = mpcalc.cape_cin(p, T, Td, prof)
+
+###########################################
+# Determine the LCL, LFC, and EL for our surface parcel
+lclp, lclt = mpcalc.lcl(p[0], T[0], Td[0])
+lfcp, _ = mpcalc.lfc(p, T, Td)
+el_pressure, _ = mpcalc.el(p, T, Td, prof)
+
+###########################################
+# Compute the characteristics of a mean layer parcel (50-hPa depth)
+ml_t, ml_td = mpcalc.mixed_layer(p, T, Td, depth=50 * units.hPa)
+ml_p, _, _ = mpcalc.mixed_parcel(p, T, Td, depth=50 * units.hPa)
+mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, prof, depth=50 * units.hPa)
+
+###########################################
+# Compute the characteristics of the most unstable parcel (50-hPa depth)
+mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(p, T, Td, depth=50 * units.hPa)
+mucape, mucin = mpcalc.most_unstable_cape_cin(p, T, Td, depth=50 * units.hPa)
+
+###########################################
+# Compute the Bunkers Storm Motion vector and use to calculate the critical angle
+(u_storm, v_storm), *_ = mpcalc.bunkers_storm_motion(p, u, v, height)
+critical_angle = mpcalc.critical_angle(p, u, v, height, u_storm, v_storm)
+
+###########################################
+# Work on the calculations needed to compute the significant tornado parameter
+
+# Estimate height of LCL in meters from hydrostatic thickness
+new_p = np.append(p[p > lclp], lclp)
+new_t = np.append(T[p > lclp], lclt)
+lcl_height = mpcalc.thickness_hydrostatic(new_p, new_t)
+
+# Compute Surface-based CAPE
+sbcape, _ = mpcalc.surface_based_cape_cin(p, T, Td)
+
+# Compute SRH, given a motion vector toward the NE at 9.9 m/s
+*_, total_helicity = mpcalc.storm_relative_helicity(height, u, v, depth=1 * units.km,
+                                                    storm_u=u_storm, storm_v=v_storm)
+
+# Copmute Bulk Shear components and then magnitude
+ubshr, vbshr = mpcalc.bulk_shear(p, u, v, height=height, depth=6 * units.km)
+bshear = mpcalc.wind_speed(ubshr, vbshr)
+
+# Use all computed pieces to calculate the Significant Tornado parameter
+sig_tor = mpcalc.significant_tornado(sbcape, lcl_height,
+                                     total_helicity, bshear).to_base_units()
+
+###########################################
+# Compute the supercell composite parameter, if possible
+
+# Determine the top and bottom of the effective layer using our own function
+hbot, htop = effective_layer(p, T, Td, height, height_layer=True)
+
+# Perform the calculation of supercell composite if an effective layer exists
+if hbot:
+    esrh = mpcalc.storm_relative_helicity(height, u, v, depth=htop - hbot, bottom=hbot)
+    eubshr, evbshr = mpcalc.bulk_shear(p, u, v, height=height, depth=htop - hbot, bottom=hbot)
+    ebshear = mpcalc.wind_speed(eubshr, evbshr)
+
+    super_comp = mpcalc.supercell_composite(mucape, esrh[0], ebshear)
+else:
+    super_comp = np.nan
+
+###########################################
+# Print Important Sounding Parameters
+print('Important Sounding Parameters for KOUN on 22 Mary 2011 12 UTC')
+print()
+print(f'        CAPE: {cape:.2f}')
+print(f'         CIN: {cin:.2f}')
+print(f'LCL Pressure: {lclp:.2f}')
+print(f'LFC Pressure: {lfcp:.2f}')
+print(f' EL Pressure: {el_pressure:.2f}')
+print()
+print(f'   Lifted Index: {lift_index:.2f}')
+print(f'        K-Index: {kindex:.2f}')
+print(f'Showalter Index: {showalter:.2f}')
+print(f'   Cross Totals: {ctotals:.2f}')
+print(f'   Total Totals: {total_totals:.2f}')
+print(f'Vertical Totals: {vert_totals:.2f}')
+print()
+print('Mixed Layer - Lowest 50-hPa')
+print(f'     ML Temp: {ml_t:.2f}')
+print(f'     ML Dewp: {ml_td:.2f}')
+print(f'     ML CAPE: {mlcape:.2f}')
+print(f'      ML CIN: {mlcin:.2f}')
+print()
+print('Most Unstable - Lowest 50-hPa')
+print(f'     MU Temp: {mu_t:.2f}')
+print(f'     MU Dewp: {mu_td:.2f}')
+print(f' MU Pressure: {mu_p:.2f}')
+print(f'     MU CAPE: {mucape:.2f}')
+print(f'      MU CIN: {mucin:.2f}')
+print()
+print('Bunkers Storm Motion Vector')
+print(f'  u_storm: {u_storm:.2f}')
+print(f'  v_storm: {v_storm:.2f}')
+print(f'Critical Angle: {critical_angle:.2f}')
+print()
+print(f'Storm Relative Helicity: {total_helicity:.2f}')
+print(f'Significant Tornado Parameter: {sig_tor:.2f}')
+print(f'Supercell Composite Parameter: {super_comp:.2f}')