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# Copyright (c) 2008-2016 MetPy Developers. |
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# Distributed under the terms of the BSD 3-Clause License. |
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# SPDX-License-Identifier: BSD-3-Clause |
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""" |
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Four Panel Map |
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=============== |
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By reading model output data from a netCDF file, we can create a four panel plot showing: |
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* 300 hPa heights and winds |
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* 500 hPa heights and absolute vorticity |
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* Surface temperatures |
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* Precipitable water |
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""" |
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########################################### |
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import netCDF4 |
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import numpy as np |
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import scipy.ndimage as ndimage |
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import matplotlib.pyplot as plt |
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import matplotlib.gridspec as gridspec |
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import cartopy.crs as ccrs |
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import cartopy.feature as cfeature |
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from metpy.cbook import get_test_data |
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########################################### |
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# Function used to create the map subplots |
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def plot_background(ax): |
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ax.set_extent([235., 290., 20., 55.]) |
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ax.coastlines('50m', edgecolor='black', linewidth=0.5) |
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ax.add_feature(states_provinces, edgecolor='black', linewidth=0.5) |
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ax.add_feature(country_borders, edgecolor='black', linewidth=0.5) |
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return ax |
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########################################### |
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# Open the example netCDF data |
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ds = netCDF4.Dataset(get_test_data('gfs_output.nc', False)) |
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print(ds) |
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########################################### |
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# Convert number of hours since the reference time into an actual date |
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time_vals = netCDF4.num2date(ds.variables['time'][:].squeeze(), ds.variables['time'].units) |
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########################################### |
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# Combine 1D latitude and longitudes into a 2D grid of locations |
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lon_2d, lat_2d = np.meshgrid(ds.variables['lon'][:], ds.variables['lat'][:]) |
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########################################### |
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# Convert data to common formats |
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vort_500 = ds.variables['vort_500'][0] * 10**5 # To plot a e-5 s^-1 |
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surface_temp = ds.variables['temp'][0] * 1.8 - 459.67 |
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precip_water = ds.variables['precip_water'][0] * 0.0393700787 |
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winds_300 = ds.variables['winds_300'][0] * 1.94384 |
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########################################### |
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# Smooth the height data |
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heights_300 = ndimage.gaussian_filter(ds.variables['heights_300'][0], sigma=1.5, order=0) |
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heights_500 = ndimage.gaussian_filter(ds.variables['heights_500'][0], sigma=1.5, order=0) |
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########################################### |
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# Add state boundaries to plot |
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states_provinces = cfeature.NaturalEarthFeature(category='cultural', |
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name='admin_1_states_provinces_lines', |
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scale='50m', facecolor='none') |
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# Add country borders to plot |
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country_borders = cfeature.NaturalEarthFeature(category='cultural', |
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name='admin_0_countries', |
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scale='50m', facecolor='none') |
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crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0) |
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########################################### |
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# Create the figure |
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fig = plt.figure(figsize=(20, 15)) |
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gs = gridspec.GridSpec(5, 2, height_ratios=[1, .05, 1, .05, 0], bottom=.05, top=.95, wspace=.1) |
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# Upper left plot - 300-hPa winds and geopotential heights |
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ax1 = plt.subplot(gs[0, 0], projection=crs) |
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plot_background(ax1) |
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cf1 = ax1.contourf(lon_2d, lat_2d, winds_300, cmap='cool', transform=ccrs.PlateCarree()) |
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c1 = ax1.contour(lon_2d, lat_2d, heights_300, colors='black', linewidth=2, |
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transform=ccrs.PlateCarree()) |
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plt.clabel(c1, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True) |
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ax2 = plt.subplot(gs[1, 0]) |
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cb1 = plt.colorbar(cf1, cax=ax2, orientation='horizontal') |
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cb1.set_label(r'kts', size='x-large') |
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ax1.set_title('300-hPa Wind Speeds and Heights \nfor {0:%d %B %Y %H:%MZ}'.format(time_vals), |
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fontsize=16) |
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# Upper right plot - 500mb absolute vorticity and geopotential heights |
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ax3 = plt.subplot(gs[0, 1], projection=crs) |
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plot_background(ax3) |
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cf2 = ax3.contourf(lon_2d, lat_2d, vort_500, cmap='BrBG', transform=ccrs.PlateCarree(), |
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zorder=0, norm=plt.Normalize(-32, 32), latlon=True) |
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c2 = ax3.contour(lon_2d, lat_2d, heights_500, colors='k', lw=2, transform=ccrs.PlateCarree()) |
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plt.clabel(c2, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True) |
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ax4 = plt.subplot(gs[1, 1]) |
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cb2 = plt.colorbar(cf2, cax=ax4, orientation='horizontal') |
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cb2.set_label(r'$10^{-5}$ s$^{-1}$', size='x-large') |
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ax3.set_title('500-hPa Absolute Vorticity and Heights \n' |
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'for {0:%d %B %Y %H:%MZ}'.format(time_vals), fontsize=16) |
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# Lower left plot - surface temperatures |
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ax5 = plt.subplot(gs[2, 0], projection=crs) |
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plot_background(ax5) |
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cf3 = ax5.contourf(lon_2d, lat_2d, surface_temp, cmap='YlOrRd', |
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transform=ccrs.PlateCarree(), zorder=0) |
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ax6 = plt.subplot(gs[3, 0]) |
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cb3 = plt.colorbar(cf3, cax=ax6, orientation='horizontal') |
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cb3.set_label(r'$^o$F', size='x-large') |
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ax5.set_title('Surface Temperatures for {0:%d %B %Y %H:%MZ}'.format(time_vals), fontsize=16) |
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# Lower right plot - precipitable water entire atmosphere |
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ax7 = plt.subplot(gs[2, 1], projection=crs) |
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plot_background(ax7) |
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cf4 = plt.contourf(lon_2d, lat_2d, precip_water, cmap='Greens', |
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transform=ccrs.PlateCarree(), zorder=0) |
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ax8 = plt.subplot(gs[3, 1]) |
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cb4 = plt.colorbar(cf4, cax=ax8, orientation='horizontal') |
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cb4.set_label(r'in.', size='x-large') |
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ax7.set_title('Precipitable Water for {0:%d %B %Y %H:%MZ}'.format(time_vals), fontsize=16) |
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# Display the plot |
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plt.show() |
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Unidata /
MetPy
