71823,53.8,-73.7,5150.0,210.0,12.3
71836,51.3,-80.6,5120.0,240.0,12.3
71906,58.1,-68.4,5140.0,255.0,17.4
71964,60.7,-135.1,5310.0,250.0,36.5
'''
# Extracts data from IDV output and masks out stations outside of North America
lat,lon,height,wdir,speed = np.loadtxt(StringIO(data), delimiter=',',
unpack=True, usecols=(1,2,3,4,5))
# Create a map for plotting
bm = Basemap(projection='tmerc', lat_0=90.0, lon_0=-100.0, lat_ts=40.0,
llcrnrlon=-121, llcrnrlat=24, urcrnrlon=-65, urcrnrlat=46,
resolution='l')
# Get U,V components from wind speed and direction
u,v = metpy.get_wind_components(speed, wdir)
# Rotate the vectors to be properly aligned in the map projection
u,v = bm.rotate_vector(u, v, lon, lat)
# Generate grid of x,y positions
lon_grid, lat_grid, x_grid, y_grid = bm.makegrid(130, 60, returnxy=True)
# Transform the obs to basemap space for gridding
obx,oby = bm(lon, lat)
# Perform analysis of height obs using Cressman weights
heights_oban = grid_data(height, x_grid, y_grid, obx, oby,
obans[which_oban][0], obans[which_oban][1])
heights_oban = maskoceans(lon_grid, lat_grid, heights_oban)
from metpy.vis import station_plot
from metpy.tools.oban import gaussian_filter
# TODO: Find a way to fix the bad convergence values at the edge of the
# masked data
data = read_mesonet_data('data/200905082110.mdf',
fields=('STID', 'TIME', 'TAIR', 'RELH', 'WSPD', 'WDIR'))
#Calculate dewpoint in F from relative humidity and temperature
dewpt = C2F(dewpoint(data['TAIR'], data['RELH']/100.))
data = append_fields(data, ('dewpoint',), (dewpt,))
#Convert temperature and dewpoint to Farenheit
data['TAIR'] = C2F(data['TAIR'])
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)
from metpy.tools.oban import gaussian_filter
# TODO: Find a way to fix the bad convergence values at the edge of the
# masked data
data = read_mesonet_data('data/200905082110.mdf',
fields=('STID', 'TIME', 'TAIR', 'RELH', 'WSPD',
'WDIR'))
#Calculate dewpoint in F from relative humidity and temperature
dewpt = C2F(dewpoint(data['TAIR'], data['RELH'] / 100.))
data = append_fields(data, ('dewpoint', ), (dewpt, ))
#Convert temperature and dewpoint to Farenheit
data['TAIR'] = C2F(data['TAIR'])
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()
unpack=True,
usecols=(1, 2, 3, 4, 5))
# Create a map for plotting
bm = Basemap(projection='tmerc',
lat_0=90.0,
lon_0=-100.0,
lat_ts=40.0,
llcrnrlon=-121,
llcrnrlat=24,
urcrnrlon=-65,
urcrnrlat=46,
resolution='l')
# Get U,V components from wind speed and direction
u, v = metpy.get_wind_components(speed, wdir)
# Rotate the vectors to be properly aligned in the map projection
u, v = bm.rotate_vector(u, v, lon, lat)
# Generate grid of x,y positions
lon_grid, lat_grid, x_grid, y_grid = bm.makegrid(130, 60, returnxy=True)
# Transform the obs to basemap space for gridding
obx, oby = bm(lon, lat)
# Perform analysis of height obs using Cressman weights
heights_oban = grid_data(height, x_grid, y_grid, obx, oby,
obans[which_oban][0], obans[which_oban][1])
heights_oban = maskoceans(lon_grid, lat_grid, heights_oban)
