Station_Plot-with-OKTA-USA-in-Python3-and-MetPy

https://unidata.github.io/MetPy/latest/examples/plots/Station_Plot.html#sphx-glr-examples-plots-station-plot-py

Station Plot

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.

import cartopy.crs as ccrs import cartopy.feature as cfeature import matplotlib.pyplot as plt

from metpy.calc import reduce_point_density from metpy.cbook import get_test_data from metpy.io import metar from metpy.plots import add_metpy_logo, current_weather, sky_cover, StationPlot

The setup

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

data = metar.parse_metar_file(get_test_data('metar_20190701_1200.txt', as_file_obj=False))

Drop rows with missing winds

data = data.dropna(how='any', subset=['wind_direction', 'wind_speed'])

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=[35])

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.)]

The payoff

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()