Make a station plot, complete with sky cover and weather symbols.
The station plot itself is pretty straightforward, but there is a bit of code to perform the data-wrangling (hopefully that situation will improve in the future). Certainly, if you have existing point data in a format you can work with trivially, the station plot will be simple.
First read in the data. We use the metar reader because it simplifies a lot of tasks, like dealing with separating text and assembling a pandas dataframe https://thredds.ucar.edu/thredds/catalog/noaaport/text/metar/catalog.html
This sample data has way too many stations to plot all of them. The number of stations plotted will be reduced using reduce_point_density.
# Set up the map projection proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35, standard_parallels=) # Use the Cartopy map projection to transform station locations to the map and # then refine the number of stations plotted by setting a 300km radius point_locs = proj.transform_points(ccrs.PlateCarree(), data['longitude'].values, data['latitude'].values) data = data[reduce_point_density(point_locs, 300000.)]
# Change the DPI of the resulting figure. Higher DPI drastically improves the # look of the text rendering. plt.rcParams['savefig.dpi'] = 255 # Create the figure and an axes set to the projection. fig = plt.figure(figsize=(20, 10)) add_metpy_logo(fig, 1100, 300, size='large') ax = fig.add_subplot(1, 1, 1, projection=proj) # Add some various map elements to the plot to make it recognizable. ax.add_feature(cfeature.LAND) ax.add_feature(cfeature.OCEAN) ax.add_feature(cfeature.LAKES) ax.add_feature(cfeature.COASTLINE) ax.add_feature(cfeature.STATES) ax.add_feature(cfeature.BORDERS) # Set plot bounds ax.set_extent((-118, -73, 23, 50)) # # Here's the actual station plot # # Start the station plot by specifying the axes to draw on, as well as the # lon/lat of the stations (with transform). We also the fontsize to 12 pt. stationplot = StationPlot(ax, data['longitude'].values, data['latitude'].values, clip_on=True, transform=ccrs.PlateCarree(), fontsize=12) # Plot the temperature and dew point to the upper and lower left, respectively, of # the center point. Each one uses a different color. stationplot.plot_parameter('NW', data['air_temperature'].values, color='red') stationplot.plot_parameter('SW', data['dew_point_temperature'].values, color='darkgreen') # A more complex example uses a custom formatter to control how the sea-level pressure # values are plotted. This uses the standard trailing 3-digits of the pressure value # in tenths of millibars. stationplot.plot_parameter('NE', data['air_pressure_at_sea_level'].values, formatter=lambda v: format(10 * v, '.0f')[-3:]) # Plot the cloud cover symbols in the center location. This uses the codes made above and # uses the `sky_cover` mapper to convert these values to font codes for the # weather symbol font. stationplot.plot_symbol('C', data['cloud_coverage'].values, sky_cover) # Same this time, but plot current weather to the left of center, using the # `current_weather` mapper to convert symbols to the right glyphs. stationplot.plot_symbol('W', data['current_wx1_symbol'].values, current_weather) # Add wind barbs stationplot.plot_barb(data['eastward_wind'].values, data['northward_wind'].values) # Also plot the actual text of the station id. Instead of cardinal directions, # plot further out by specifying a location of 2 increments in x and 0 in y. stationplot.plot_text((2, 0), data['station_id'].values) plt.show()
Total running time of the script: ( 0 minutes 20.711 seconds)