def test_cross_section_components(test_cross_lonlat):
"""Test getting cross section components of a 2D vector field."""
tang_wind, norm_wind = cross_section_components(test_cross_lonlat['u_wind'],
test_cross_lonlat['v_wind'])
true_tang_wind_values = np.array([[3.24812563, 2.9994653, 2.75080496, 2.50214463,
2.47106208, 2.22240175, 1.97374141],
[1.94265887, 1.69399854, 1.4453382, 1.19667786,
1.16559532, 0.91693499, 0.66827465],
[0.63719211, 0.38853177, 0.13987144, -0.1087889,
-0.13987144, -0.38853177, -0.63719211],
[-0.66827465, -0.91693499, -1.16559532, -1.41425566,
-1.4453382, -1.69399854, -1.94265887],
[-1.97374141, -2.22240175, -2.47106208, -2.71972242,
-2.75080496, -2.9994653, -3.24812563]])
true_norm_wind_values = np.array([[56.47521297, 52.15175169, 47.82829041, 43.50482913,
42.96439647, 38.64093519, 34.31747391],
[33.77704125, 29.45357997, 25.13011869, 20.80665741,
20.26622475, 15.94276347, 11.61930219],
[11.07886953, 6.75540825, 2.43194697, -1.89151431,
-2.43194697, -6.75540825, -11.07886953],
[-11.61930219, -15.94276347, -20.26622475, -24.58968603,
-25.13011869, -29.45357997, -33.77704125],
[-34.31747391, -38.64093519, -42.96439647, -47.28785775,
-47.82829041, -52.15175169, -56.47521297]])
true_tang_wind = xr.DataArray(true_tang_wind_values,
coords=test_cross_lonlat['u_wind'].coords,
dims=test_cross_lonlat['u_wind'].dims,
attrs=test_cross_lonlat['u_wind'].attrs)
true_norm_wind = xr.DataArray(true_norm_wind_values,
coords=test_cross_lonlat['u_wind'].coords,
dims=test_cross_lonlat['u_wind'].dims,
attrs=test_cross_lonlat['u_wind'].attrs)
assert_xarray_allclose(tang_wind, true_tang_wind)
assert_xarray_allclose(norm_wind, true_norm_wind)
def test_cross_section_components(test_cross_lonlat):
"""Test getting cross section components of a 2D vector field."""
tang_wind, norm_wind = cross_section_components(test_cross_lonlat['u_wind'],
test_cross_lonlat['v_wind'])
true_tang_wind_values = np.array([[3.24812563, 2.9994653, 2.75080496, 2.50214463,
2.47106208, 2.22240175, 1.97374141],
[1.94265887, 1.69399854, 1.4453382, 1.19667786,
1.16559532, 0.91693499, 0.66827465],
[0.63719211, 0.38853177, 0.13987144, -0.1087889,
-0.13987144, -0.38853177, -0.63719211],
[-0.66827465, -0.91693499, -1.16559532, -1.41425566,
-1.4453382, -1.69399854, -1.94265887],
[-1.97374141, -2.22240175, -2.47106208, -2.71972242,
-2.75080496, -2.9994653, -3.24812563]])
true_norm_wind_values = np.array([[56.47521297, 52.15175169, 47.82829041, 43.50482913,
42.96439647, 38.64093519, 34.31747391],
[33.77704125, 29.45357997, 25.13011869, 20.80665741,
20.26622475, 15.94276347, 11.61930219],
[11.07886953, 6.75540825, 2.43194697, -1.89151431,
-2.43194697, -6.75540825, -11.07886953],
[-11.61930219, -15.94276347, -20.26622475, -24.58968603,
-25.13011869, -29.45357997, -33.77704125],
[-34.31747391, -38.64093519, -42.96439647, -47.28785775,
-47.82829041, -52.15175169, -56.47521297]])
true_tang_wind = xr.DataArray(true_tang_wind_values * units.knots,
coords=test_cross_lonlat['u_wind'].coords,
dims=test_cross_lonlat['u_wind'].dims,
attrs=test_cross_lonlat['u_wind'].attrs)
true_norm_wind = xr.DataArray(true_norm_wind_values * units.knots,
coords=test_cross_lonlat['u_wind'].coords,
dims=test_cross_lonlat['u_wind'].dims,
attrs=test_cross_lonlat['u_wind'].attrs)
assert_xarray_allclose(tang_wind, true_tang_wind)
assert_xarray_allclose(norm_wind, true_norm_wind)
def Crosssection_Wind_Temp_RH(
initial_time=None,
fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 200],
day_back=0,
model='ECMWF',
output_dir=None,
st_point=[43.5, 111.5],
ed_point=[33, 125.0],
map_extent=[70, 140, 15, 55],
h_pos=[0.125, 0.665, 0.25, 0.2]):
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='RH',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl='500'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PSFC')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
# retrieve data from micaps server
rh = get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
rh = rh.metpy.parse_cf().squeeze()
u = get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
u = u.metpy.parse_cf().squeeze()
v = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
v = v.metpy.parse_cf().squeeze()
v2 = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v2 is None:
return
v2 = v2.metpy.parse_cf().squeeze()
t = get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if t is None:
return
t = t.metpy.parse_cf().squeeze()
gh = get_model_grid(data_dir[4], filename=filename)
psfc = get_model_grid(data_dir[5], filename=filename)
psfc = psfc.metpy.parse_cf().squeeze()
mask1 = ((psfc['lon'] >= t['lon'].values.min()) &
(psfc['lon'] <= t['lon'].values.max()) &
(psfc['lat'] >= t['lat'].values.min()) &
(psfc['lat'] <= t['lat'].values.max()))
t2, psfc_bdcst = xr.broadcast(t['data'], psfc['data'].where(mask1,
drop=True))
mask2 = (psfc_bdcst > -10000)
psfc_bdcst = psfc_bdcst.where(mask2, drop=True)
#psfc_bdcst=psfc_bdcst.metpy.parse_cf().squeeze()
if t is None:
return
resolution = u['lon'][1] - u['lon'][0]
x, y = np.meshgrid(u['lon'], u['lat'])
dx, dy = mpcalc.lat_lon_grid_deltas(u['lon'], u['lat'])
#rh=rh.rename(dict(lat='latitude',lon='longitude'))
cross = cross_section(rh, st_point, ed_point)
cross_rh = cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u = cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v = cross.set_coords(('lat', 'lon'))
cross_psfc = cross_section(psfc_bdcst, st_point, ed_point)
#cross_psfc=cross.set_coords(('lat', 'lon'))
cross_u['data'].attrs['units'] = units.meter / units.second
cross_v['data'].attrs['units'] = units.meter / units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(
cross_u['data'], cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_Temp = cross.set_coords(('lat', 'lon'))
cross_Td = mpcalc.dewpoint_rh(cross_Temp['data'].values * units.celsius,
cross_rh['data'].values * units.percent)
rh, pressure = xr.broadcast(cross_rh['data'], cross_Temp['level'])
cross_terrain = pressure - cross_psfc
crossection_graphics.draw_Crosssection_Wind_Temp_RH(
cross_rh=cross_rh,
cross_Temp=cross_Temp,
cross_u=cross_u,
cross_v=cross_v,
cross_terrain=cross_terrain,
gh=gh,
h_pos=h_pos,
st_point=st_point,
ed_point=ed_point,
levels=levels,
map_extent=map_extent,
model=model,
output_dir=output_dir)
def Crosssection_Wind_Theta_e_Qv(
initial_time=None,
fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 200],
day_back=0,
model='ECMWF',
output_dir=None,
st_point=[20, 120.0],
ed_point=[50, 130.0],
map_extent=[70, 140, 15, 55],
h_pos=[0.125, 0.665, 0.25, 0.2]):
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='RH',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl='500')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
# retrieve data from micaps server
rh = get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
rh = rh.metpy.parse_cf().squeeze()
u = get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
u = u.metpy.parse_cf().squeeze()
v = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
v = v.metpy.parse_cf().squeeze()
v2 = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v2 is None:
return
v2 = v2.metpy.parse_cf().squeeze()
t = get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if t is None:
return
t = t.metpy.parse_cf().squeeze()
gh = get_model_grid(data_dir[4], filename=filename)
if t is None:
return
resolution = u['lon'][1] - u['lon'][0]
x, y = np.meshgrid(u['lon'], u['lat'])
dx, dy = mpcalc.lat_lon_grid_deltas(u['lon'], u['lat'])
for ilvl in levels:
u2d = u.sel(level=ilvl)
#u2d['data'].attrs['units']=units.meter/units.second
v2d = v.sel(level=ilvl)
#v2d['data'].attrs['units']=units.meter/units.second
absv2d = mpcalc.absolute_vorticity(
u2d['data'].values * units.meter / units.second,
v2d['data'].values * units.meter / units.second, dx, dy,
y * units.degree)
if (ilvl == levels[0]):
absv3d = v2
absv3d['data'].loc[dict(level=ilvl)] = np.array(absv2d)
else:
absv3d['data'].loc[dict(level=ilvl)] = np.array(absv2d)
absv3d['data'].attrs['units'] = absv2d.units
#rh=rh.rename(dict(lat='latitude',lon='longitude'))
cross = cross_section(rh, st_point, ed_point)
cross_rh = cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u = cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v = cross.set_coords(('lat', 'lon'))
cross_u['data'].attrs['units'] = units.meter / units.second
cross_v['data'].attrs['units'] = units.meter / units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(
cross_u['data'], cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_t = cross.set_coords(('lat', 'lon'))
cross = cross_section(absv3d, st_point, ed_point)
cross_Td = mpcalc.dewpoint_rh(cross_t['data'].values * units.celsius,
cross_rh['data'].values * units.percent)
rh, pressure = xr.broadcast(cross_rh['data'], cross_t['level'])
Qv = mpcalc.specific_humidity_from_dewpoint(cross_Td, pressure)
cross_Qv = xr.DataArray(np.array(Qv) * 1000.,
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': units('g/kg')})
Theta_e = mpcalc.equivalent_potential_temperature(
pressure, cross_t['data'].values * units.celsius, cross_Td)
cross_Theta_e = xr.DataArray(np.array(Theta_e),
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': Theta_e.units})
crossection_graphics.draw_Crosssection_Wind_Theta_e_Qv(
cross_Qv=cross_Qv,
cross_Theta_e=cross_Theta_e,
cross_u=cross_u,
cross_v=cross_v,
gh=gh,
h_pos=h_pos,
st_point=st_point,
ed_point=ed_point,
levels=levels,
map_extent=map_extent,
output_dir=output_dir)
print(max(theta.flatten()))
print(min(theta.flatten()))
# These calculations return unit arrays, so put those back into DataArrays in our Dataset
cross['Potential_temperature'] = xr.DataArray(theta,
coords=temperature.coords,
dims=temperature.dims,
attrs={'units': theta.units})
cross['rhum'] = xr.DataArray(relative_humidity / 100,
coords=relative_humidity.coords,
dims=relative_humidity.dims,
attrs={'units': relative_humidity.units})
cross['uwnd'].metpy.convert_units('knots')
cross['vwnd'].metpy.convert_units('knots')
cross['t_wind'], cross['n_wind'] = mpcalc.cross_section_components(
cross['uwnd'], cross['vwnd'])
print(cross)
##############################
# Now, we can make the plot.
# Define the figure object and primary axes
fig = plt.figure(1, figsize=(16., 9.))
ax = plt.axes()
# Plot potential temperature using contour, with some custom labeling
rh_contour = ax.contourf(cross['lon'],
cross['level'],
cross['rhum'],
def metpy_read_wrf_cross(fname, plevels, tidx_in, lons_out, lats_out, start,
end):
ds = xr.open_dataset(fname).metpy.parse_cf().squeeze()
ds = ds.isel(Time=tidx)
print(ds)
ds1 = xr.Dataset()
p = units.Quantity(to_np(ds.p), 'hPa')
z = units.Quantity(to_np(ds.z), 'meter')
u = units.Quantity(to_np(ds.u), 'm/s')
v = units.Quantity(to_np(ds.v), 'm/s')
w = units.Quantity(to_np(ds.w), 'm/s')
tk = units.Quantity(to_np(ds.tk), 'kelvin')
th = units.Quantity(to_np(ds.th), 'kelvin')
eth = units.Quantity(to_np(ds.eth), 'kelvin')
wspd = units.Quantity(to_np(ds.wspd), 'm/s')
omega = units.Quantity(to_np(ds.omega), 'Pa/s')
plevels_unit = plevels * units.hPa
z, u, v, w, tk, th, eth, wspd, omega = log_interpolate_1d(plevels_unit,
p,
z,
u,
v,
w,
tk,
th,
eth,
wspd,
omega,
axis=0)
coords, dims = [plevs, ds.lat.values,
ds.lon.values], ["level", "lat", "lon"]
for name, var in zip(
['z', 'u', 'v', 'w', 'tk', 'th', 'eth', 'wspd', 'omega'],
[z, u, v, w, tk, th, eth, wspd, omega]):
#g = ndimage.gaussian_filter(var, sigma=3, order=0)
ds1[name] = xr.DataArray(to_np(mpcalc.smooth_n_point(var, 9)),
coords=coords,
dims=dims)
dx, dy = mpcalc.lat_lon_grid_deltas(ds.lon.values * units('degrees_E'),
ds.lat.values * units('degrees_N'))
# Calculate temperature advection using metpy function
for i, plev in enumerate(plevs):
adv = mpcalc.advection(eth[i, :, :], [u[i, :, :], v[i, :, :]],
(dx, dy),
dim_order='yx') * units('K/sec')
adv = ndimage.gaussian_filter(adv, sigma=3, order=0) * units('K/sec')
ds1['eth_adv_{:03d}'.format(plev)] = xr.DataArray(
np.array(adv),
coords=[ds.lat.values, ds.lon.values],
dims=["lat", "lon"])
div = mpcalc.divergence(u[i, :, :], v[i, :, :], dx, dy, dim_order='yx')
div = ndimage.gaussian_filter(div, sigma=3, order=0) * units('1/sec')
ds1['div_{:03d}'.format(plev)] = xr.DataArray(
np.array(div),
coords=[ds.lat.values, ds.lon.values],
dims=["lat", "lon"])
ds1['accrain'] = xr.DataArray(ds.accrain.values,
coords=[ds.lat.values, ds.lon.values],
dims=["lat", "lon"])
eth2 = mpcalc.equivalent_potential_temperature(
ds.slp.values * units.hPa, ds.t2m.values * units('K'),
ds.td2.values * units('celsius'))
ds1['eth2'] = xr.DataArray(eth2,
coords=[ds.lat.values, ds.lon.values],
dims=["lat", "lon"])
#ds1['sst'] = xr.DataArray(ndimage.gaussian_filter(ds.sst.values, sigma=3, order=0)-273.15, coords=[ds.lat.values,ds.lon.values], dims=["lat","lon"])
ds1 = ds1.metpy.parse_cf().squeeze()
cross = cross_section(ds1, start, end).set_coords(('lat', 'lon'))
cross.u.attrs['units'] = 'm/s'
cross.v.attrs['units'] = 'm/s'
cross['t_wind'], cross['n_wind'] = mpcalc.cross_section_components(
cross['u'], cross['v'])
weights = np.cos(np.deg2rad(ds.lat))
ds_weighted = ds.weighted(weights)
weighted = ds_weighted.mean(("lat"))
return ds1, cross, weighted
theta = mpcalc.potential_temperature(pressure, temperature)
rh = mpcalc.relative_humidity_from_specific_humidity(specific_humidity, temperature, pressure)
# These calculations return unit arrays, so put those back into DataArrays in our Dataset
cross['Potential_temperature'] = xr.DataArray(theta,
coords=temperature.coords,
dims=temperature.dims,
attrs={'units': theta.units})
cross['Relative_humidity'] = xr.DataArray(rh,
coords=specific_humidity.coords,
dims=specific_humidity.dims,
attrs={'units': rh.units})
cross['u_wind'].metpy.convert_units('knots')
cross['v_wind'].metpy.convert_units('knots')
cross['t_wind'], cross['n_wind'] = mpcalc.cross_section_components(cross['u_wind'],
cross['v_wind'])
print(cross)
##############################
# Now, we can make the plot.
# Define the figure object and primary axes
fig = plt.figure(1, figsize=(16., 9.))
ax = plt.axes()
# Plot RH using contourf
rh_contour = ax.contourf(cross['lon'], cross['isobaric'], cross['Relative_humidity'],
levels=np.arange(0, 1.05, .05), cmap='YlGnBu')
rh_colorbar = fig.colorbar(rh_contour)
def Crosssection_Wind_Temp_RH(
initTime=None, fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,200],
day_back=0,model='ECMWF',data_source='MICAPS',
output_dir=None,
st_point = [43.5, 111.5],
ed_point = [33, 125.0],
map_extent=[70,140,15,55],
lw_ratio=[16,9],
h_pos=[0.125, 0.665, 0.25, 0.2],**kwargs ):
if(data_source == 'MICAPS'):
try:
data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',lvl='500'),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')]
except KeyError:
raise ValueError('Can not find all directories needed')
if(initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour)
rh=get_model_3D_grid(directory=data_dir[0][0:-1],filename=filename,levels=levels, allExists=False)
u=get_model_3D_grid(directory=data_dir[1][0:-1],filename=filename,levels=levels, allExists=False)
v=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
t=get_model_3D_grid(directory=data_dir[3][0:-1],filename=filename,levels=levels, allExists=False)
gh=get_model_grid(data_dir[4], filename=filename)
psfc=get_model_grid(data_dir[5], filename=filename)
if(data_source == 'CIMISS'):
if(initTime != None):
filename = utl.model_filename(initTime, fhour,UTC=True)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour,UTC=True)
try:
rh=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'),
fcst_levels=levels, fcst_ele="RHU", units='%')
u=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'),
fcst_levels=levels, fcst_ele="WIU", units='m/s')
v=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s')
t=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
fcst_levels=levels, fcst_ele="TEM", units='K')
t['data'].values=t['data'].values-273.15
gh=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='GPH'),
fcst_level=500, fcst_ele="GPH", units='gpm')
gh['data'].values=gh['data'].values/10.
psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'),
fcst_level=0, fcst_ele="PRS", units='Pa')
psfc['data']=psfc['data']/100.
except KeyError:
raise ValueError('Can not find all data needed')
rh = rh.metpy.parse_cf().squeeze()
u = u.metpy.parse_cf().squeeze()
v = v.metpy.parse_cf().squeeze()
t = t.metpy.parse_cf().squeeze()
psfc=psfc.metpy.parse_cf().squeeze()
#if(psfc['lon'].values[0] != t['lon'].values[0]):
mask1 = (
(psfc['lon']>=t['lon'].values.min())&
(psfc['lon']<=t['lon'].values.max())&
(psfc['lat']>=t['lat'].values.min())&
(psfc['lat']<=t['lat'].values.max())
)
t2,psfc_bdcst=xr.broadcast(t['data'],psfc['data'].where(mask1, drop=True))
mask2=(psfc_bdcst > -10000)
psfc_bdcst=psfc_bdcst.where(mask2, drop=True)
#else:
# psfc_bdcst=psfc['data'].copy
resolution=u['lon'][1]-u['lon'][0]
x,y=np.meshgrid(u['lon'], u['lat'])
dx,dy=mpcalc.lat_lon_grid_deltas(u['lon'],u['lat'])
cross = cross_section(rh, st_point, ed_point)
cross_rh=cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u=cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v=cross.set_coords(('lat', 'lon'))
cross_psfc = cross_section(psfc_bdcst, st_point, ed_point)
cross_u['data'].attrs['units']=units.meter/units.second
cross_v['data'].attrs['units']=units.meter/units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(cross_u['data'],cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_Temp=cross.set_coords(('lat', 'lon'))
cross_Td = mpcalc.dewpoint_rh(cross_Temp['data'].values*units.celsius,
cross_rh['data'].values* units.percent)
rh,pressure = xr.broadcast(cross_rh['data'],cross_Temp['level'])
cross_terrain=pressure-cross_psfc
crossection_graphics.draw_Crosssection_Wind_Temp_RH(
cross_rh=cross_rh, cross_Temp=cross_Temp, cross_u=cross_u,
cross_v=cross_v,cross_terrain=cross_terrain,gh=gh,
h_pos=h_pos,st_point=st_point,ed_point=ed_point,lw_ratio=lw_ratio,
levels=levels,map_extent=map_extent,model=model,
output_dir=output_dir)
def Crosssection_Wind_Theta_e_Qv(
initTime=None, fhour=24,
levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,200],
day_back=0,model='GRAPES_GFS',data_source='MICAPS',
lw_ratio=[16,9],
output_dir=None,
st_point = [20, 120.0],
ed_point = [50, 130.0],
map_extent=[70,140,15,55],
h_pos=[0.125, 0.665, 0.25, 0.2],**kwargs ):
if(data_source == 'MICAPS'):
try:
data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',lvl='500'),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if(initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour)
# retrieve data from micaps server
rh=get_model_3D_grid(directory=data_dir[0][0:-1],filename=filename,levels=levels, allExists=False)
u=get_model_3D_grid(directory=data_dir[1][0:-1],filename=filename,levels=levels, allExists=False)
v=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
v2=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
t=get_model_3D_grid(directory=data_dir[3][0:-1],filename=filename,levels=levels, allExists=False)
gh=get_model_grid(data_dir[4], filename=filename)
psfc=get_model_grid(data_dir[5], filename=filename)
if(data_source == 'CIMISS'):
# get filename
if(initTime != None):
filename = utl.model_filename(initTime, fhour,UTC=True)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour,UTC=True)
try:
rh=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'),
fcst_levels=levels, fcst_ele="RHU", units='%')
u=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'),
fcst_levels=levels, fcst_ele="WIU", units='m/s')
v=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s')
v2=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s')
t=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
fcst_levels=levels, fcst_ele="TEM", units='K')
t['data'].values=t['data'].values-273.15
gh=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='GPH'),
fcst_level=500, fcst_ele="GPH", units='gpm')
gh['data'].values=gh['data'].values/10.
psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'),
fcst_level=0, fcst_ele="PRS", units='Pa')
psfc['data']=psfc['data']/100.
except KeyError:
raise ValueError('Can not find all data needed')
rh = rh.metpy.parse_cf().squeeze()
u = u.metpy.parse_cf().squeeze()
v = v.metpy.parse_cf().squeeze()
v2 = v2.metpy.parse_cf().squeeze()
psfc=psfc.metpy.parse_cf().squeeze()
t = t.metpy.parse_cf().squeeze()
resolution=u['lon'][1]-u['lon'][0]
x,y=np.meshgrid(u['lon'], u['lat'])
# +form 3D psfc
mask1 = (
(psfc['lon']>=t['lon'].values.min())&
(psfc['lon']<=t['lon'].values.max())&
(psfc['lat']>=t['lat'].values.min())&
(psfc['lat']<=t['lat'].values.max())
)
t2,psfc_bdcst=xr.broadcast(t['data'],psfc['data'].where(mask1, drop=True))
mask2=(psfc_bdcst > -10000)
psfc_bdcst=psfc_bdcst.where(mask2, drop=True)
# -form 3D psfc
dx,dy=mpcalc.lat_lon_grid_deltas(u['lon'],u['lat'])
#rh=rh.rename(dict(lat='latitude',lon='longitude'))
cross = cross_section(rh, st_point, ed_point)
cross_rh=cross.set_coords(('lat', 'lon'))
cross = cross_section(u, st_point, ed_point)
cross_u=cross.set_coords(('lat', 'lon'))
cross = cross_section(v, st_point, ed_point)
cross_v=cross.set_coords(('lat', 'lon'))
cross_psfc = cross_section(psfc_bdcst, st_point, ed_point)
cross_u['data'].attrs['units']=units.meter/units.second
cross_v['data'].attrs['units']=units.meter/units.second
cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(cross_u['data'],cross_v['data'])
cross = cross_section(t, st_point, ed_point)
cross_t=cross.set_coords(('lat', 'lon'))
cross_Td = mpcalc.dewpoint_rh(cross_t['data'].values*units.celsius,
cross_rh['data'].values* units.percent)
rh,pressure = xr.broadcast(cross_rh['data'],cross_t['level'])
pressure.attrs['units']='hPa'
Qv = mpcalc.specific_humidity_from_dewpoint(cross_Td,
pressure)
cross_Qv = xr.DataArray(np.array(Qv)*1000.,
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': units('g/kg')})
Theta_e=mpcalc.equivalent_potential_temperature(pressure,
cross_t['data'].values*units.celsius,
cross_Td)
cross_terrain=pressure-cross_psfc
cross_Theta_e = xr.DataArray(np.array(Theta_e),
coords=cross_rh['data'].coords,
dims=cross_rh['data'].dims,
attrs={'units': Theta_e.units})
crossection_graphics.draw_Crosssection_Wind_Theta_e_Qv(
cross_Qv=cross_Qv, cross_Theta_e=cross_Theta_e, cross_u=cross_u,
cross_v=cross_v,cross_terrain=cross_terrain,gh=gh,
h_pos=h_pos,st_point=st_point,ed_point=ed_point,
levels=levels,map_extent=map_extent,lw_ratio=lw_ratio,
output_dir=output_dir)