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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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Wind and Sea Level Pressure Interpolation |
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========================================= |
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Interpolate sea level pressure, as well as wind component data, |
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to make a consistent looking analysis, featuring contours of pressure and wind barbs. |
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""" |
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import cartopy |
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import cartopy.crs as ccrs |
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from matplotlib.colors import BoundaryNorm |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from metpy.calc import get_wind_components |
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from metpy.cbook import get_test_data |
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from metpy.gridding.gridding_functions import interpolate, remove_nan_observations |
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from metpy.units import units |
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from_proj = ccrs.Geodetic() |
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to_proj = ccrs.AlbersEqualArea(central_longitude=-97., central_latitude=38.) |
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def station_test_data(variable_names, proj_from=None, proj_to=None): |
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View Code Duplication |
f = get_test_data('station_data.txt') |
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all_data = np.loadtxt(f, skiprows=1, delimiter=',', |
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usecols=(1, 2, 3, 4, 5, 6, 7, 17, 18, 19), |
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dtype=np.dtype([('stid', '3S'), ('lat', 'f'), ('lon', 'f'), |
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('slp', 'f'), ('air_temperature', 'f'), |
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('cloud_fraction', 'f'), ('dewpoint', 'f'), |
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('weather', '16S'), |
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('wind_dir', 'f'), ('wind_speed', 'f')])) |
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all_stids = [s.decode('ascii') for s in all_data['stid']] |
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data = np.concatenate([all_data[all_stids.index(site)].reshape(1, ) for site in all_stids]) |
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value = data[variable_names] |
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lon = data['lon'] |
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lat = data['lat'] |
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if proj_from is not None and proj_to is not None: |
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proj_points = proj_to.transform_points(proj_from, lon, lat) |
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return proj_points[:, 0], proj_points[:, 1], value |
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return lon, lat, value |
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########################################### |
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# Get pressure information using the sample station data |
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xp, yp, pres = station_test_data(['slp'], from_proj, to_proj) |
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########################################### |
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# Remove all missing data from pressure |
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pres = np.array([p[0] for p in pres]) |
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xp, yp, pres = remove_nan_observations(xp, yp, pres) |
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########################################### |
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# Interpolate pressure as usual |
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slpgridx, slpgridy, slp = interpolate(xp, yp, pres, interp_type='cressman', |
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minimum_neighbors=1, search_radius=400000, hres=100000) |
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########################################### |
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# Get wind information |
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x, y, wind = station_test_data(['wind_speed', 'wind_dir'], from_proj, to_proj) |
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########################################### |
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# Remove bad data from wind information |
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wind_speed = np.array([w[0] for w in wind]) |
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wind_dir = np.array([w[1] for w in wind]) |
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good_indices = np.where((~np.isnan(wind_dir)) & (~np.isnan(wind_speed))) |
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x = x[good_indices] |
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y = y[good_indices] |
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wind_speed = wind_speed[good_indices] |
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wind_dir = wind_dir[good_indices] |
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########################################### |
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# Calculate u and v components of wind and then interpolate both. |
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# |
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# Both will have the same underlying grid so throw away grid returned from v interpolation. |
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u, v = get_wind_components((wind_speed * units('m/s')).to('knots'), |
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wind_dir * units.degree) |
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windgridx, windgridy, uwind = interpolate(x, y, np.array(u), interp_type='cressman', |
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search_radius=400000, hres=100000) |
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_, _, vwind = interpolate(x, y, np.array(v), interp_type='cressman', search_radius=400000, |
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hres=100000) |
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########################################### |
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# Get temperature information |
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levels = list(range(-20, 20, 1)) |
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cmap = plt.get_cmap('viridis') |
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norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) |
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xt, yt, t = station_test_data('air_temperature', from_proj, to_proj) |
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xt, yt, t = remove_nan_observations(xt, yt, t) |
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tempx, tempy, temp = interpolate(xt, yt, t, interp_type='cressman', minimum_neighbors=3, |
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search_radius=400000, hres=35000) |
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temp = np.ma.masked_where(np.isnan(temp), temp) |
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########################################### |
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# Set up the map and plot the interpolated grids appropriately. |
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fig = plt.figure(figsize=(20, 10)) |
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view = fig.add_subplot(1, 1, 1, projection=to_proj) |
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view.set_extent([-120, -70, 20, 50]) |
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view.add_feature(cartopy.feature.NaturalEarthFeature(category='cultural', |
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name='admin_1_states_provinces_lakes', |
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scale='50m', facecolor='none')) |
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view.add_feature(cartopy.feature.OCEAN) |
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view.add_feature(cartopy.feature.COASTLINE) |
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view.add_feature(cartopy.feature.BORDERS, linestyle=':') |
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cs = view.contour(slpgridx, slpgridy, slp, colors='k', levels=list(range(990, 1034, 4))) |
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plt.clabel(cs, inline=1, fontsize=12, fmt='%i') |
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mmb = view.pcolormesh(tempx, tempy, temp, cmap=cmap, norm=norm) |
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plt.colorbar(mmb, shrink=.4, pad=0.02, boundaries=levels) |
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view.barbs(windgridx, windgridy, uwind, vwind, alpha=.4, length=5) |
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plt.title('Surface Temperature (shaded), SLP, and Wind.') |
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plt.show() |
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Unidata /
MetPy
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