lat_ts=35,
resolution='i',
projection='stere',
urcrnrlat=37.,
urcrnrlon=-94.25,
llcrnrlat=33.7,
llcrnrlon=-103.,
ax=ax)
m.bluemarble()
#Objectively analyze wind components
sig = 15000
lon_grid, lat_grid, x_grid, y_grid = m.makegrid(125, 50, returnxy=True)
x, y = m(data['longitude'], data['latitude'])
u_grid = griddata(x, y, data['u'], x_grid, y_grid)
u_grid = gaussian_filter(x_grid.T, y_grid.T, u_grid.T, sig, sig)
v_grid = griddata(x, y, data['v'], x_grid, y_grid)
v_grid = gaussian_filter(x_grid.T, y_grid.T, v_grid.T, sig, sig)
conv = h_convergence(u_grid.filled(0), v_grid.filled(0), x_grid[0, 1],
y_grid[1, 0])
conv = np.ma.array(conv, mask=u_grid.mask)
plt.pcolor(x_grid.T,
y_grid.T,
conv,
zorder=0,
cmap=plt.get_cmap('RdBu'),
antialiased=False,
norm=plt.Normalize(-2e-4, 2e-4))
#Convert wind speeds to MPH
# use RBF - multiquadric epsm = 0.15 rbfm = Rbf(x, y, z, function='multiquadric', epsilon=epsm, smooth=0.05) rbfm_interp = rbfm(XI, YI) rbfm_rms = rms(rbfm_interp - truth) # use RBF - gaussian epsg = .4 rbfg = Rbf(x, y, z, function='gaussian', epsilon=epsg, smooth=0.1) rbfg_interp = rbfg(XI, YI) rbfg_rms = rms(rbfg_interp - truth) # Natural neighbor nat_interp = griddata(x, y, z, XI, YI) nat_filtered = gaussian_filter(XI, YI, nat_interp, 0.1, 0.1) nat_rms = rms(nat_interp - truth) # Cressman Rc = 0.5 cress_interp = grid_data(z, XI, YI, x, y, cressman_weights, Rc) cress_rms = rms(cress_interp - truth) # Barnes kstar = 0.1 barnes_interp = grid_data(z, XI, YI, x, y, barnes_weights, (0.5, kstar)) barnes_rms = rms(barnes_interp - truth) # plot the results val_norm = plt.normalize(-0.45, 0.45) diff_norm = plt.normalize(-1.0, 1.0)
u,v = get_wind_components(data['WSPD'], data['WDIR'])
data = append_fields(data, ('u', 'v'), (u, v))
fig = plt.figure(figsize=(20,12))
ax = fig.add_subplot(1,1,1)
m = Basemap(lon_0=-99, lat_0=35, lat_ts=35, resolution='i',
projection='stere', urcrnrlat=37., urcrnrlon=-94.25, llcrnrlat=33.7,
llcrnrlon=-103., ax=ax)
m.bluemarble()
#Objectively analyze wind components
sig = 15000
lon_grid, lat_grid, x_grid, y_grid = m.makegrid(125, 50, returnxy=True)
x,y = m(data['longitude'], data['latitude'])
u_grid = griddata(x, y, data['u'], x_grid, y_grid)
u_grid = gaussian_filter(x_grid.T, y_grid.T, u_grid.T, sig, sig)
v_grid = griddata(x, y, data['v'], x_grid, y_grid)
v_grid = gaussian_filter(x_grid.T, y_grid.T, v_grid.T, sig, sig)
conv = h_convergence(u_grid.filled(0), v_grid.filled(0), x_grid[0, 1], y_grid[1, 0])
conv = np.ma.array(conv, mask=u_grid.mask)
plt.pcolor(x_grid.T, y_grid.T, conv, zorder=0, cmap=plt.get_cmap('RdBu'),
antialiased=False, norm=plt.Normalize(-2e-4, 2e-4))
#Convert wind speeds to MPH
data['u'] *= sconsts.hour / sconsts.mile
data['v'] *= sconsts.hour / sconsts.mile
station_plot(data, ax=ax, proj=m,
styles=dict(dewpoint=dict(color='lightgreen')), zorder=10)
m.drawstates(ax=ax, zorder=1)
mapfile = os.path.join(os.environ['HOME'], 'mapdata', 'c_03oc08.shp')
# use RBF - multiquadric epsm = 0.15 rbfm = Rbf(x, y, z, function='multiquadric', epsilon=epsm, smooth=0.05) rbfm_interp = rbfm(XI, YI) rbfm_rms = rms(rbfm_interp - truth) # use RBF - gaussian epsg = .4 rbfg = Rbf(x, y, z, function='gaussian', epsilon=epsg, smooth=0.1) rbfg_interp = rbfg(XI, YI) rbfg_rms = rms(rbfg_interp - truth) # Natural neighbor nat_interp = griddata(x, y, z, XI, YI) nat_filtered = gaussian_filter(XI, YI, nat_interp, 0.1, 0.1) nat_rms = rms(nat_interp - truth) # Cressman Rc = 0.5 cress_interp = grid_data(z, XI, YI, x, y, cressman_weights, Rc) cress_rms = rms(cress_interp - truth) # Barnes kstar = 0.1 barnes_interp = grid_data(z, XI, YI, x, y, barnes_weights, (0.5, kstar)) barnes_rms = rms(barnes_interp - truth) # plot the results val_norm = plt.normalize(-0.45, 0.45) diff_norm = plt.normalize(-1.0, 1.0)
lat_0=35,
lat_ts=35,
resolution='i',
projection='stere',
urcrnrlat=37.,
urcrnrlon=-94.25,
llcrnrlat=33.7,
llcrnrlon=-103.,
ax=ax)
m.bluemarble()
#Objectively analyze dewpoint
lon_grid, lat_grid, x_grid, y_grid = m.makegrid(125, 50, returnxy=True)
x, y = m(data['longitude'], data['latitude'])
dew_grid = griddata(x, y, data['dewpoint'], x_grid, y_grid)
dew_grid = gaussian_filter(x_grid.T, y_grid.T, dew_grid.T, 10000, 10000)
plt.pcolor(x_grid.T,
y_grid.T,
dew_grid,
zorder=0,
cmap=plt.get_cmap('Greens'),
antialiased=False)
station_plot(data,
ax=ax,
proj=m,
styles=dict(dewpoint=dict(color='lightgreen')),
zorder=10)
m.drawstates(ax=ax, zorder=1)
plt.title(data['datetime'][0].strftime('%H%MZ %d %b %Y'))
#Convert temperature and dewpoint to Farenheit
data['TAIR'] = C2F(data['TAIR'])
#Convert wind speeds to MPH
data['WSPD'] *= sconsts.hour / sconsts.mile
u,v = get_wind_components(data['WSPD'], data['WDIR'])
data = append_fields(data, ('u', 'v'), (u, v))
fig = plt.figure(figsize=(20,12))
ax = fig.add_subplot(1,1,1)
m = Basemap(lon_0=-99, lat_0=35, lat_ts=35, resolution='i',
projection='stere', urcrnrlat=37., urcrnrlon=-94.25, llcrnrlat=33.7,
llcrnrlon=-103., ax=ax)
m.bluemarble()
#Objectively analyze dewpoint
lon_grid, lat_grid, x_grid, y_grid = m.makegrid(125, 50, returnxy=True)
x,y = m(data['longitude'], data['latitude'])
dew_grid = griddata(x, y, data['dewpoint'], x_grid, y_grid)
dew_grid = gaussian_filter(x_grid.T, y_grid.T, dew_grid.T, 10000, 10000)
plt.pcolor(x_grid.T, y_grid.T, dew_grid, zorder=0, cmap=plt.get_cmap('Greens'),
antialiased=False)
station_plot(data, ax=ax, proj=m,
styles=dict(dewpoint=dict(color='lightgreen')), zorder=10)
m.drawstates(ax=ax, zorder=1)
plt.title(data['datetime'][0].strftime('%H%MZ %d %b %Y'))
plt.show()
