def test_frontogenesis_asym():
"""Test vorticity and convergence calculation with a complicated field."""
u = np.array([[2, 4, 8], [0, 2, 2], [4, 6, 8]]) * units('m/s')
v = np.array([[6, 4, 8], [2, 6, 0], [2, 2, 6]]) * units('m/s')
theta = np.array([[303, 295, 305], [308, 310, 312], [299, 293, 289]
]) * units('K')
fronto = frontogenesis(theta,
u,
v,
1 * units.meters,
2 * units.meters,
dim_order='yx')
true_fronto = np.array([[-20.93890452, -7.83070042, -36.43293256],
[0.89442719, -2.12218672, -8.94427191],
[-16.8, -7.65600391, -61.65921479]
]) * units.K / units.meter / units.sec
assert_almost_equal(fronto, true_fronto)
# Now try for xy ordered
fronto = frontogenesis(theta.T,
u.T,
v.T,
1 * units.meters,
2 * units.meters,
dim_order='xy')
assert_almost_equal(fronto, true_fronto.T)
def test_frontogenesis_asym():
"""Test frontogensis calculation with a complicated field."""
u = np.array([[2, 4, 8], [0, 2, 2], [4, 6, 8]]) * units('m/s')
v = np.array([[6, 4, 8], [2, 6, 0], [2, 2, 6]]) * units('m/s')
theta = np.array([[303, 295, 305], [308, 310, 312], [299, 293, 289]
]) * units('K')
fronto = frontogenesis(theta,
u,
v,
1 * units.meters,
2 * units.meters,
dim_order='yx')
true_fronto = np.array([[-52.4746386, -37.3658646, -50.3996939],
[3.5777088, -2.1221867, -16.9941166],
[-23.1417334, 26.0499143, -158.4839684]
]) * units.K / units.meter / units.sec
assert_almost_equal(fronto, true_fronto)
# Now try for xy ordered
fronto = frontogenesis(theta.T,
u.T,
v.T,
1 * units.meters,
2 * units.meters,
dim_order='xy')
assert_almost_equal(fronto, true_fronto.T)
QVEC700 = mpcalc.q_vector(U700, V700, T700, 700 * units.mbar, DX, DY)
QC700 = mpcalc.divergence(QVEC700[0], QVEC700[1], DX, DY,
dim_order='yx') * (10**18)
QVECX = QVEC700[0]
QVECY = QVEC700[1]
# =============================================================================
# FIG #4: 850: GEOPOTENTIAL HEIGHT, TEMP, WINDS, TEMP-ADVECTION, FRONTOGENESIS
# =============================================================================
H850 = HGT_DATA.variables['hgt'][TIME_INDEX, 2, :, :]
T850 = AIR_DATA.variables['air'][TIME_INDEX, 2, :, :] * units('kelvin')
U850 = UWND_DATA.variables['uwnd'][TIME_INDEX, 2, :, :] * units('m/s')
V850 = VWND_DATA.variables['vwnd'][TIME_INDEX, 2, :, :] * units('m/s')
T_ADV850 = mpcalc.advection(T850 * units.kelvin, [U850, V850], (DX, DY),
dim_order='yx') * units('K/sec')
PT850 = mpcalc.potential_temperature(850 * units.mbar, T850)
FRONT_850 = mpcalc.frontogenesis(PT850, U850, V850, DX, DY, dim_order='YX')
# =============================================================================
# FIG #5: 850: GEOPOTENTIAL HEIGHT, EQUIV. POT. TEMP, WINDS, LAPSE RATES
# =============================================================================
H500 = HGT_DATA.variables['hgt'][TIME_INDEX, 5, :, :]
H700 = HGT_DATA.variables['hgt'][TIME_INDEX, 3, :, :]
T500 = AIR_DATA.variables['air'][TIME_INDEX, 5, :, :] * units('degC')
T700 = AIR_DATA.variables['air'][TIME_INDEX, 3, :, :] * units('degC')
LR = -1000 * (T500 - T700) / (H500 - H700)
H850 = HGT_DATA.variables['hgt'][TIME_INDEX, 2, :, :]
T850 = AIR_DATA.variables['air'][TIME_INDEX, 2, :, :] * units('kelvin')
SH850 = SHUM_DATA.variables['shum'][TIME_INDEX, 2, :, :]
DP850 = mpcalc.dewpoint_from_specific_humidity(SH850, T850, 850 * units.mbar)
EPT850 = mpcalc.equivalent_potential_temperature(850 * units.mbar, T850, DP850)
# =============================================================================
# FIG #6: 925: MOISTURE FLUX, MOISTURE FLUX CONVERGENCE,
def fg_uv_tmp(initTime=None, fhour=6, day_back=0,model='ECMWF',
fg_lev=500,
map_ratio=16/9,zoom_ratio=20,cntr_pnt=[102,34],
south_China_sea=True,area = None,city=False,output_dir=None,data_source='MICAPS',
**kwargs):
if(area != None):
south_China_sea=False
# micaps data directory
if(data_source=='MICAPS'):
try:
data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',lvl=fg_lev),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=fg_lev),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=fg_lev),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=fg_lev),
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
gh = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
if gh is None:
return
u = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if u is None:
return
v = MICAPS_IO.get_model_grid(data_dir[2], filename=filename)
if v is None:
return
tmp = MICAPS_IO.get_model_grid(data_dir[3], filename=filename)
psfc = MICAPS_IO.get_model_grid(data_dir[4], 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)
# retrieve data from CIMISS server
try:
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'),
levattrs={'long_name':'pressure_level', 'units':'hPa', '_CoordinateAxisType':'-'},
fcst_level=fg_lev, fcst_ele="GPH", units='gpm')
if gh is None:
return
gh['data'].values=gh['data'].values/10.
u=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'),
levattrs={'long_name':'pressure_level', 'units':'hPa', '_CoordinateAxisType':'-'},
fcst_level=fg_lev, fcst_ele="WIU", units='m/s')
if u is None:
return
v=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
levattrs={'long_name':'pressure_level', 'units':'hPa', '_CoordinateAxisType':'-'},
fcst_level=fg_lev, fcst_ele="WIV", units='m/s')
if v is None:
return
tmp=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
levattrs={'long_name':'pressure_level', 'units':'hPa', '_CoordinateAxisType':'-'},
fcst_level=fg_lev, fcst_ele="TEM", units='K')
if tmp is None:
return
tmp['data'].values=tmp['data'].values-273.15
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')
#caluculate_fg
pressure=fg_lev*units('hPa')
theta = mpcalc.potential_temperature(pressure, tmp['data'].values.squeeze()*units('degC'))
dx,dy=mpcalc.lat_lon_grid_deltas(tmp['lon'].values.squeeze(),tmp['lat'].values.squeeze())
fg=mpcalc.frontogenesis(theta,u['data'].values.squeeze()*units('mps'),v['data'].values.squeeze()*units('mps'),dx,dy,dim_order='yx')
if(area != None):
cntr_pnt,zoom_ratio=utl.get_map_area(area_name=area)
map_extent,delt_x,delt_y=utl.get_map_extent(cntr_pnt, zoom_ratio, map_ratio)
#+ to solve the problem of labels on all the contours
fg_xr=u.copy()
fg_xr['data'].values=fg.magnitude[np.newaxis,np.newaxis,:,:]
fg_xr=utl.cut_xrdata(map_extent, fg_xr, delt_x=delt_x, delt_y=delt_y)
u=utl.cut_xrdata(map_extent, u, delt_x=delt_x, delt_y=delt_y)
v=utl.cut_xrdata(map_extent, v, delt_x=delt_x, delt_y=delt_y)
tmp=utl.cut_xrdata(map_extent, tmp, delt_x=delt_x, delt_y=delt_y)
fg_xr=utl.mask_terrian(fg_lev,psfc,fg_xr)
u=utl.mask_terrian(fg_lev,psfc,u)
v=utl.mask_terrian(fg_lev,psfc,v)
tmp=utl.mask_terrian(fg_lev,psfc,tmp)
fg_xr.attrs['model']=model
fg_xr.attrs['units']='K*s${^{-1}}$ m${^{-1}}$'
uv=xr.merge([u.rename({'data': 'u'}),v.rename({'data': 'v'})])
#- to solve the problem of labels on all the contours
dynamic_graphics.draw_fg_uv_tmp(
fg=fg_xr, tmp=tmp, uv=uv,
map_extent=map_extent, regrid_shape=20,
city=city,south_China_sea=south_China_sea,
output_dir=output_dir)