def sta_SkewT(model='ECMWF',points={'lon':[116.3833], 'lat':[39.9]},
levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,250,200,150,100],
fhour=3,output_dir=None):
try:
data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',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='HGT',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl='')]
except KeyError:
raise ValueError('Can not find all directories needed')
# # 度数据
initTime = get_latest_initTime(data_dir[0][0:-1]+"850")
filename = initTime+'.'+str(fhour).zfill(3)
TMP_4D=get_model_3D_grid(directory=data_dir[0][0:-1],filename=filename,levels=levels, allExists=False)
TMP_2D=TMP_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
u_4D=get_model_3D_grid(directory=data_dir[1][0:-1],filename=filename,levels=levels, allExists=False)
u_2D=u_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
v_4D=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False)
v_2D=v_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
HGT_4D=get_model_3D_grid(directory=data_dir[3][0:-1],filename=filename,levels=levels, allExists=False)
HGT_2D=HGT_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
HGT_2D.attrs['model']=model
HGT_2D.attrs['points']=points
RH_4D=get_model_3D_grid(directory=data_dir[4][0:-1],filename=filename,levels=levels, allExists=False)
RH_2D=RH_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
wind_dir_2D=mpcalc.wind_direction(u_2D['data'].values* units.meter / units.second,
v_2D['data'].values* units.meter / units.second)
wsp10m_2D=(u_2D['data']**2+v_2D['data']**2)**0.5
Td2m=mpcalc.dewpoint_rh(TMP_2D['data'].values*units('degC'),RH_2D['data'].values/100.)
p = np.squeeze(levels) * units.hPa
T = np.squeeze(TMP_2D['data'].values) * units.degC
Td = np.squeeze(np.array(Td2m)) * units.degC
wind_speed = np.squeeze(wsp10m_2D.values) * units.meter
wind_dir = np.squeeze(np.array(wind_dir_2D)) * units.degrees
u=np.squeeze(u_2D['data'].values)* units.meter
v=np.squeeze(v_2D['data'].values)* units.meter
fcst_info= xr.DataArray(np.array(u_2D['data'].values),
coords=u_2D['data'].coords,
dims=u_2D['data'].dims,
attrs={'points': points,
'model': model})
sta_graphics.draw_sta_skewT(
p=p,T=T,Td=Td,wind_speed=wind_speed,wind_dir=wind_dir,u=u,v=v,
fcst_info=fcst_info)
def point_fcst(
model='ECMWF',
output_dir=None,
t_range=[0,60],
t_gap=3,
points={'lon':[116.3833], 'lat':[39.9], 'altitude':[1351]},
initTime=None,day_back=0,
extra_info={
'output_head_name':' ',
'output_tail_name':' ',
'point_name':' '}
):
#+get all the directories needed
try:
dir_rqd=[utl.Cassandra_dir(data_type='surface',data_source=model,var_name='T2m'),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='u10m'),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='v10m'),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='RAIN'+str(t_gap).zfill(2))]
except KeyError:
raise ValueError('Can not find all required directories needed')
#-get all the directories needed
if(initTime == None):
initTime = get_latest_initTime(dir_rqd[0])
#initTime=utl.filename_day_back_model(day_back=day_back,fhour=0)[0:8]
directory=dir_rqd[0][0:-1]
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [initTime+'.'+str(fhour).zfill(3) for fhour in fhours]
t2m=utl.get_model_points_gy(dir_rqd[0], filenames, points,allExists=False)
u10m=utl.get_model_points_gy(dir_rqd[1], filenames, points,allExists=False)
v10m=utl.get_model_points_gy(dir_rqd[2], filenames, points,allExists=False)
rn=utl.get_model_points_gy(dir_rqd[3], filenames, points,allExists=False)
sta_graphics.draw_point_fcst(t2m=t2m,u10m=u10m,v10m=v10m,rn=rn,
model=model,
output_dir=output_dir,
points=points,
extra_info=extra_info
)
def point_fcst_rn_according_to_3D_field_box_line(output_dir=None,
t_range=[6, 60],
t_gap=6,
points={
'lon': [116.3833],
'lat': [39.9]
},
initTime=None,
obs_ID=54511,
extra_info={
'output_head_name': ' ',
'output_tail_name': ' ',
'point_name': ' ',
'drw_thr': True
},
**kwargs):
try:
dir_rqd = utl.Cassandra_dir(data_type='surface',
data_source='ECMWF_ENSEMBLE',
var_name='RAIN' + str(t_gap).zfill(2) +
'_RAW')
except KeyError:
raise ValueError('Can not find all required directories needed')
#-get all the directories needed
if (initTime == None):
initTime = get_latest_initTime(dir_rqd)
#initTime=utl.filename_day_back_model(day_back=day_back,fhour=0)[0:8]
if (t_range[1] > 72):
fhours = np.append(np.arange(t_range[0], 72, t_gap),
np.arange(72, t_range[1], 6))
else:
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
rn = utl.get_model_points_gy(dir_rqd, filenames, points, allExists=False)
rn.attrs['model'] = 'ECMWF_ENSEMBLE'
Ensemble_graphics.box_line_rn(rn=rn,
points=points,
extra_info=extra_info,
output_dir=output_dir)
def Time_Crossection_rh_uv_Temp(
initTime=None,
model='ECMWF',
points={
'lon': [116.3833],
'lat': [39.9]
},
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 200],
t_gap=3,
t_range=[0, 48],
output_dir=None):
fhours = np.arange(t_range[0], t_range[1], t_gap)
# 读数据
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
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='RH',
lvl=''),
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):
initTime = get_latest_initTime(data_dir[0][0:-1] + "850")
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
TMP_4D = get_model_3D_grids(directory=data_dir[0][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
TMP_2D = TMP_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
u_4D = get_model_3D_grids(directory=data_dir[1][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
u_2D = u_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
v_4D = get_model_3D_grids(directory=data_dir[2][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
v_2D = v_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
rh_4D = get_model_3D_grids(directory=data_dir[3][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
rh_2D = rh_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
rh_2D.attrs['model'] = model
rh_2D.attrs['points'] = points
Psfc_3D = get_model_grids(directory=data_dir[4][0:-1],
filenames=filenames,
allExists=False)
Psfc_1D = Psfc_3D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
v_2D2, pressure_2D = xr.broadcast(v_2D['data'], v_2D['level'])
v_2D2, Psfc_2D = xr.broadcast(v_2D['data'], Psfc_1D['data'])
terrain_2D = pressure_2D - Psfc_2D
crossection_graphics.draw_Time_Crossection_rh_uv_Temp(
rh_2D=rh_2D,
u_2D=u_2D,
v_2D=v_2D,
TMP_2D=TMP_2D,
terrain_2D=terrain_2D,
t_range=t_range,
model=model,
output_dir=output_dir)
def T2m_all_type(initTime=None,
fhour=24,
day_back=0,
model='中央台指导',
Var_plot='Tmn_2m',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
if (area != '全国'):
south_China_sea = False
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name=Var_plot)
]
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
T_2m = get_model_grid(data_dir[0], filename=filename)
if T_2m is None:
return
init_time = T_2m.coords['forecast_reference_time'].values
# prepare data
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
idx_x1 = np.where((T_2m.coords['lon'].values > map_extent[0] - delt_x)
& (T_2m.coords['lon'].values < map_extent[1] + delt_x))
idx_y1 = np.where((T_2m.coords['lat'].values > map_extent[2] - delt_y)
& (T_2m.coords['lat'].values < map_extent[3] + delt_y))
titles = {
'Tmn_2m': '过去24小时2米最低温度',
'Tmx_2m': '过去24小时2米最高温度',
'T2m': '2米温度'
}
#- to solve the problem of labels on all the contours
T_2m = {
'lon':
T_2m.coords['lon'].values[idx_x1],
'lat':
T_2m.coords['lat'].values[idx_y1],
'data':
T_2m['data'].values[0, 0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'model':
model,
'fhour':
fhour,
'title':
titles[Var_plot],
'init_time':
init_time
}
elements_graphics.draw_T_2m(T_2m=T_2m,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def point_wind_time_fcst_according_to_3D_wind(
model='ECMWF',
output_dir=None,
t_range=[0,60],
t_gap=3,
points={'lon':[116.3833], 'lat':[39.9], 'altitude':[1351]},
initTime=None,draw_obs=True,obs_ID=54511,day_back=0,
extra_info={
'output_head_name':' ',
'output_tail_name':' ',
'point_name':' ',
'drw_thr':True,
'levels_for_interp':[1000, 950, 925, 900, 850, 800, 700, 600, 500]}
):
#+get all the directories needed
try:
dir_rqd=[utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',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='')]
except KeyError:
raise ValueError('Can not find all required directories needed')
#-get all the directories needed
if(initTime == None):
initTime = get_latest_initTime(dir_rqd[0][0:-1]+'/850')
#initTime=utl.filename_day_back_model(day_back=day_back,fhour=0)[0:8]
directory=dir_rqd[0][0:-1]
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [initTime+'.'+str(fhour).zfill(3) for fhour in fhours]
HGT_4D=get_model_3D_grids(directory=directory,filenames=filenames,levels=extra_info['levels_for_interp'], allExists=False)
directory=dir_rqd[1][0:-1]
U_4D=get_model_3D_grids(directory=directory,filenames=filenames,levels=extra_info['levels_for_interp'], allExists=False)
directory=dir_rqd[2][0:-1]
V_4D=get_model_3D_grids(directory=directory,filenames=filenames,levels=extra_info['levels_for_interp'], allExists=False)
#obs
if(draw_obs == True):
initial_time=pd.to_datetime(str(V_4D['forecast_reference_time'].values)).replace(tzinfo=None).to_pydatetime()
sign=0
for ifhour in V_4D['forecast_period'].values:
temp=(initial_time+timedelta(hours=ifhour))
filenames_obs=temp.strftime("%Y%m%d%H")+'0000.000'
try:
obs_data=get_station_data('SURFACE/PLOT/',filename=filenames_obs)
except:
break
if(obs_data is not None):
temp=obs_data.where(obs_data['ID']==obs_ID).dropna(how='all')
if ((ifhour == V_4D['forecast_period'].values[0]) or ((ifhour > V_4D['forecast_period'].values[0]) and (sign==0))):
if(len(temp) > 0):
sta_obs_data=obs_data.where(obs_data['ID']==obs_ID).dropna(how='all').reset_index()
sign=1
else:
if(len(temp) > 0):
sta_obs_data=sta_obs_data.append(temp).reset_index()
if(obs_data is None):
break
try:
sta_obs_data
except:
draw_obs=False
delt_xy=HGT_4D['lon'].values[1]-HGT_4D['lon'].values[0]
mask = (HGT_4D['lon']<(points['lon']+2*delt_xy))&(HGT_4D['lon']>(points['lon']-2*delt_xy))&(HGT_4D['lat']<(points['lat']+2*delt_xy))&(HGT_4D['lat']>(points['lat']-2*delt_xy))
HGT_4D_sm=HGT_4D['data'].where(mask,drop=True)
U_4D_sm=U_4D['data'].where(mask,drop=True)
V_4D_sm=V_4D['data'].where(mask,drop=True)
lon_md=np.squeeze(HGT_4D_sm['lon'].values)
lat_md=np.squeeze(HGT_4D_sm['lat'].values)
alt_md=np.squeeze(HGT_4D_sm.values*10).flatten()
time_md=np.squeeze(HGT_4D_sm['forecast_period'].values)
coords = np.zeros((len(time_md),len(extra_info['levels_for_interp']),len(lat_md),len(lon_md),4))
coords[...,0]=time_md.reshape((len(time_md),1,1,1))
coords[...,2] = lat_md.reshape((1,1,len(lat_md),1))
coords[...,3] = lon_md.reshape((1,1,1,len(lon_md)))
coords = coords.reshape((alt_md.size,4))
coords[:,1]=alt_md
interpolator_U = LinearNDInterpolator(coords,U_4D_sm.values.reshape((U_4D_sm.values.size)),rescale=True)
interpolator_V = LinearNDInterpolator(coords,V_4D_sm.values.reshape((V_4D_sm.values.size)),rescale=True)
coords2 = np.zeros((len(time_md),1,1,1,4))
coords2[...,0]=time_md.reshape((len(time_md),1,1,1))
coords2[...,1]=points['altitude'][0]
coords2[...,2] = points['lat'][0]
coords2[...,3] = points['lon'][0]
coords2 = coords2.reshape((time_md.size,4))
U_interped=np.squeeze(interpolator_U(coords2))
V_interped=np.squeeze(interpolator_V(coords2))
time_info=HGT_4D_sm.coords
sta_graphics.draw_point_wind(U=U_interped,V=V_interped,
model=model,
output_dir=output_dir,
points=points,
time_info=time_info,
extra_info=extra_info
)
def mslp_rain_snow(initTime=None,
fhour=24,
day_back=0,
model='ECMWF',
atime=6,
data_source='MICAPS',
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
Global=False,
**kwargs):
'''
issues:
1. CIMISS 上上没有上没有GRAPES-GFS的降雪，所以当data_source='CIMISS',model='GRAPES_GFS'无法出图
'''
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PRMSL'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % atime),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='SNOW' + '%02d' % atime),
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour)
if (atime > 3):
filename_mslp = utl.model_filename(initTime,
int(fhour - atime / 2))
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
if (atime > 3):
filename_mslp = utl.filename_day_back_model(
day_back=day_back, fhour=int(fhour - atime / 2))
# retrieve data from micaps server
mslp = get_model_grid(data_dir[0], filename=filename)
if mslp is None:
return
rain = get_model_grid(data_dir[1], filename=filename)
if rain is None:
return
snow = get_model_grid(data_dir[2], filename=filename)
if snow is None:
return
if (data_source == 'CIMISS'):
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour, UTC=True)
if (atime > 3):
filename_gh = utl.filename_day_back_model(initTime,
fhour=int(fhour -
atime / 2),
UTC=True)
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)
if (atime > 3):
filename_gh = utl.filename_day_back_model(day_back=day_back,
fhour=int(fhour -
atime / 2),
UTC=True)
try:
# retrieve data from CIMISS server
if (model == 'ECMWF'):
mslp = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='GSSP'),
levattrs={
'long_name': 'Mean_sea_level',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="GSSP",
units='Pa')
else:
mslp = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='SSP'),
levattrs={
'long_name': 'Mean_sea_level',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="SSP",
units='Pa')
if mslp is None:
return
mslp['data'] = mslp['data'] / 100.
TPE1 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TPE'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE1 is None:
return
TPE2 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour - atime,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TPE'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE2 is None:
return
TTSP1 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TTSP'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TTSP",
units='kg*m^-2')
if TTSP1 is None:
return
TTSP2 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour - atime,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TTSP'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TTSP",
units='kg*m^-2')
if TTSP2 is None:
return
except KeyError:
raise ValueError('Can not find all data needed')
rain = TPE1.copy(deep=True)
rain['data'].values = (TPE1['data'].values -
TPE2['data'].values) * 1000
snow = TTSP1.copy(deep=True)
snow['data'].values = (TTSP1['data'].values -
TTSP2['data'].values) * 1000
# set map extent
if (area != None):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
mask1 = (mslp['lon'] > map_extent[0] - delt_x) & (
mslp['lon'] < map_extent[1] + delt_x) & (
mslp['lat'] > map_extent[2] - delt_y) & (mslp['lat'] <
map_extent[3] + delt_y)
mask2 = (rain['lon'] > map_extent[0] - delt_x) & (
rain['lon'] < map_extent[1] + delt_x) & (
rain['lat'] > map_extent[2] - delt_y) & (rain['lat'] <
map_extent[3] + delt_y)
mslp = mslp.where(mask1, drop=True)
mslp.attrs['model'] = model
rain = rain.where(mask2, drop=True)
snow = snow.where(mask2, drop=True)
snow.attrs['atime'] = atime
rain_snow = xr.merge(
[rain.rename({'data': 'rain'}),
snow.rename({'data': 'snow'})])
mask1 = ((rain_snow['rain'] - rain_snow['snow']) >
0.1) & (rain_snow['snow'] > 0.1)
sleet = rain_snow['rain'].where(mask1)
mask2 = ((rain_snow['rain'] - rain_snow['snow']) <
0.1) & (rain_snow['snow'] > 0.1)
snw = rain_snow['snow'].where(mask2)
mask3 = (rain_snow['rain'] > 0.1) & (rain_snow['snow'] < 0.1)
rn = rain_snow['rain'].where(mask3)
rn.attrs['atime'] = atime
# draw
QPF_graphics.draw_mslp_rain_snow(rain=rn,
snow=snw,
sleet=sleet,
mslp=mslp,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def point_uv_gust_tmp_rh_rn_fcst(output_dir=None,
t_range=[0, 60],
t_gap=3,
points={
'lon': [116.3833],
'lat': [39.9],
'altitude': [1351]
},
initTime=None,
day_back=0,
extra_info={
'output_head_name': ' ',
'output_tail_name': ' ',
'point_name': ' '
},
**kwargs):
#+get all the directories needed
try:
dir_rqd = [
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='T2m'),
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='u10m'),
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='v10m'),
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='rh2m'),
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='RAIN' + str(t_gap).zfill(2)),
utl.Cassandra_dir(data_type='surface',
data_source='OBS',
var_name='PLOT_GUST')
]
except KeyError:
raise ValueError('Can not find all required directories needed')
#-get all the directories needed
if (initTime == None):
initTime = MICAPS_IO.get_latest_initTime(dir_rqd[0])
#initTime=utl.filename_day_back_model(day_back=day_back,fhour=0)[0:8]
gust_sta = MICAPS_IO.get_station_data(directory=dir_rqd[5],
dropna=True,
cache=False)
datetime_sta = pd.to_datetime(str(
gust_sta.time[0])).replace(tzinfo=None).to_pydatetime()
datetime_model_initTime = datetime.strptime('20' + initTime, '%Y%m%d%H')
u10_his_md = []
v10_his_md = []
wsp_his_sta_point = []
model_filenames_his = None
for iinit in range(0, 240, 12):
for ifhour in range(0, 87, 3):
for iobs in range(0, 168, 1):
initTime_his = datetime_model_initTime - timedelta(hours=iinit)
validTime_his = initTime_his + timedelta(hours=ifhour)
staTime_his = datetime_sta - timedelta(hours=iobs)
if (staTime_his == validTime_his):
model_filename_his = initTime_his.strftime(
'%Y%m%d%H')[2:10] + '.' + str(ifhour).zfill(3)
sta_filename_his = validTime_his.strftime(
'%Y%m%d%H') + '0000.000'
data_md1 = MICAPS_IO.get_model_grid(
dir_rqd[1], filename=model_filename_his)
if (data_md1 is None):
continue
data_md2 = MICAPS_IO.get_model_grid(
dir_rqd[1], filename=model_filename_his)
if (data_md2 is None):
continue
data_sta = MICAPS_IO.get_station_data(
directory=dir_rqd[5],
filename=sta_filename_his,
dropna=True,
cache=True)
if (data_sta is None):
continue
u10_his_md.append(data_md1)
v10_his_md.append(data_md2)
wsp_his_sta_interp = utl.sta_to_point_interpolation(
points=points, sta=data_sta, var_name='Gust_speed')
wsp_his_sta_point.append(wsp_his_sta_interp[:])
u10_his_md = xr.concat(u10_his_md, dim='time')
v10_his_md = xr.concat(v10_his_md, dim='time')
wsp_his_md = (u10_his_md**2 + v10_his_md**2)**0.5
wsp_his_md_point = wsp_his_md.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
model = LinearRegression(copy_X=True,
fit_intercept=True,
n_jobs=1,
normalize=False)
x = np.squeeze(wsp_his_md_point['data'].values).reshape(-1, 1)
y = np.squeeze(wsp_his_sta_point).reshape(-1, 1)
model.fit(x, y)
if (model.coef_ < 0.2):
f2 = np.polyfit(np.squeeze(x), np.squeeze(y), 2)
model2 = np.poly1d(f2)
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
t2m = utl.get_model_points_gy(dir_rqd[0],
filenames,
points,
allExists=False)
u10m = utl.get_model_points_gy(dir_rqd[1],
filenames,
points,
allExists=False)
v10m = utl.get_model_points_gy(dir_rqd[2],
filenames,
points,
allExists=False)
rh = utl.get_model_points_gy(dir_rqd[3],
filenames,
points,
allExists=False)
rn = utl.get_model_points_gy(dir_rqd[4],
filenames,
points,
allExists=False)
gust10m_predict = u10m.copy()
if (model.coef_ > 0.2):
gust10m_predict['data'].values = np.squeeze(
model.predict(
np.squeeze((u10m['data'].values**2 +
v10m['data'].values**2)**0.5).reshape(-1,
1))).reshape(
-1, 1, 1)
else:
gust10m_predict['data'].values = np.squeeze(
model2(
np.squeeze((u10m['data'].values**2 +
v10m['data'].values**2)**0.5))).reshape(-1, 1, 1)
sta_graphics.draw_point_uv_tmp_rh_rn_gust_fcst(t2m=t2m,
u10m=u10m,
v10m=v10m,
rh=rh,
rn=rn,
gust=gust10m_predict,
model='中央气象台中短期指导',
output_dir=output_dir,
points=points,
extra_info=extra_info)
def Station_Synthetical_Forecast_From_Cassandra(
model='ECMWF',
output_dir=None,
t_range=[0,84],
t_gap=3,
points={'lon':[116.3833], 'lat':[39.9]},
initTime=None,
draw_VIS=True,drw_thr=False,
extra_info={
'output_head_name':' ',
'output_tail_name':' ',
'point_name':' '}
):
#+get all the directories needed
try:
dir_rqd=[
"ECMWF_HR/10_METRE_WIND_GUST_IN_THE_LAST_3_HOURS/",
"ECMWF_HR/10_METRE_WIND_GUST_IN_THE_LAST_6_HOURS/",
"ECMWF_HR/TCDC/",
"ECMWF_HR/LCDC/",
"ECMWF_HR/UGRD_100M/",
"ECMWF_HR/VGRD_100M/",
"NWFD_SCMOC/VIS/",
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='RAIN03'),
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='RAIN06'),
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='T2m'),
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='u10m'),
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='v10m'),
]
except KeyError:
raise ValueError('Can not find all required directories needed')
try:
dir_opt=[
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='Td2m')
]
name_opt=['Td2m']
except:
dir_opt=[
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='rh2m')
]
name_opt=['rh2m']
#+get all the directories needed
if(initTime == None):
last_file={model:get_latest_initTime(dir_rqd[0]),
'SCMOC':get_latest_initTime(dir_rqd[6]),
}
else:
last_file={model:initTime[0],
'SCMOC':initTime[1],
}
y_s={model:int('20'+last_file[model][0:2]),
'SCMOC':int('20'+last_file['SCMOC'][0:2])}
m_s={model:int(last_file[model][2:4]),
'SCMOC':int(last_file['SCMOC'][2:4])}
d_s={model:int(last_file[model][4:6]),
'SCMOC':int(last_file['SCMOC'][4:6])}
h_s={model:int(last_file[model][6:8]),
'SCMOC':int(last_file['SCMOC'][6:8])}
fhours = np.arange(t_range[0], t_range[1], t_gap)
for ifhour in fhours:
if (ifhour == fhours[0] ):
time_all=datetime(y_s['SCMOC'],m_s['SCMOC'],d_s['SCMOC'],h_s['SCMOC'])+timedelta(hours=int(ifhour))
else:
time_all=np.append(time_all,datetime(y_s['SCMOC'],m_s['SCMOC'],d_s['SCMOC'],h_s['SCMOC'])+timedelta(hours=int(ifhour)))
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
t2m=utl.get_model_points_gy(dir_rqd[9], filenames, points,allExists=False)
if(name_opt[0] == 'rh2m'):
rh2m=utl.get_model_points_gy(dir_opt[0], filenames, points,allExists=False)
Td2m=mpcalc.dewpoint_rh(t2m['data'].values*units('degC'),rh2m['data'].values/100.)
p_vapor=(rh2m['data'].values/100.)*6.105*(math.e**((17.27*t2m['data'].values/(237.7+t2m['data'].values))))
if(name_opt[0] == 'Td2m'):
Td2m=utl.get_model_points_gy(dir_opt[0], filenames, points,allExists=False)
rh2m=mpcalc.relative_humidity_from_dewpoint(t2m['data'].values* units('degC'),
Td2m['data'].values* units('degC'))
p_vapor=(np.array(rh2m))*6.105*(math.e**((17.27*t2m['data'].values/(237.7+t2m['data'].values))))
Td2m=np.array(Td2m['data'].values)* units('degC')
u10m=utl.get_model_points_gy(dir_rqd[10], filenames, points,allExists=False)
v10m=utl.get_model_points_gy(dir_rqd[11], filenames, points,allExists=False)
wsp10m=(u10m['data']**2+v10m['data']**2)**0.5
AT=1.07*t2m['data'].values+0.2*p_vapor-0.65*wsp10m-2.7
if((t_range[1]) > 72):
fhours = np.arange(6, t_range[1], 6)
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
r03=utl.get_model_points_gy(dir_rqd[8], filenames, points,allExists=False)
else:
r03=utl.get_model_points_gy(dir_rqd[7], filenames, points,allExists=False)
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [last_file['SCMOC']+'.'+str(fhour).zfill(3) for fhour in fhours]
VIS=utl.get_model_points_gy(dir_rqd[6], filenames, points,allExists=False,fill_null=True,Null_value=-0.001)
if(last_file['SCMOC'] == last_file[model] and t_range[1] > 72):
fhours = np.append(np.arange(3,72,3),np.arange(72, (t_range[1]), 6))
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
filenames2 = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
if(last_file['SCMOC'] != last_file[model] and t_range[1] > 60):
fhours = np.append(np.arange(3,60,3),np.arange(60, (t_range[1]), 6))
filenames = [last_file[model]+'.'+str(fhour+12).zfill(3) for fhour in fhours]
filenames2 = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
if(last_file['SCMOC'] != last_file[model] and t_range[1] <= 60):
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [last_file[model]+'.'+str(fhour+12).zfill(3) for fhour in fhours]
filenames2 = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
if(last_file['SCMOC'] == last_file[model] and t_range[1] <= 72):
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
filenames2 = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
TCDC=utl.get_model_points_gy(dir_rqd[2], filenames2, points,allExists=False)
LCDC=utl.get_model_points_gy(dir_rqd[3], filenames2, points,allExists=False)
u100m=utl.get_model_points_gy(dir_rqd[4], filenames2, points,allExists=False)
v100m=utl.get_model_points_gy(dir_rqd[5], filenames2, points,allExists=False)
wsp100m=(u100m['data']**2+v100m['data']**2)**0.5
if(fhours[-1] < 120):
gust10m=utl.get_model_points_gy(dir_rqd[0], filenames, points,allExists=False)
if(fhours[-1] > 120):
if(last_file['SCMOC'] == last_file[model]):
fhours = np.arange(0, t_range[1], 6)
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
if(last_file['SCMOC'] != last_file[model]):
fhours = np.arange(0, t_range[1], 6)
filenames = [last_file[model]+'.'+str(fhour+12).zfill(3) for fhour in fhours]
gust10m=utl.get_model_points_gy(dir_rqd[1], filenames, points,allExists=False)
sta_graphics.draw_Station_Synthetical_Forecast_From_Cassandra(
t2m=t2m,Td2m=Td2m,AT=AT,u10m=u10m,v10m=v10m,u100m=u100m,v100m=v100m,
gust10m=gust10m,wsp10m=wsp10m,wsp100m=wsp100m,r03=r03,TCDC=TCDC,LCDC=LCDC,
draw_VIS=draw_VIS,VIS=VIS,drw_thr=drw_thr,
time_all=time_all,
model=model,points=points,
output_dir=output_dir,extra_info=extra_info)
def gh_uv_r6(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
gh_lev=500,
uv_lev=850,
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
data_source='MICAPS',
Global=False,
**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=gh_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=uv_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=uv_lev),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN06'),
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
r6 = MICAPS_IO.get_model_grid(data_dir[3], filename=filename)
if r6 is None:
return
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)
try:
# retrieve data from CIMISS server
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=gh_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=uv_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=uv_lev,
fcst_ele="WIV",
units='m/s')
if v is None:
return
TPE1 = CMISS_IO.cimiss_model_by_time('20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(
data_source=model,
var_name='TPE'),
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE1 is None:
return
TPE2 = CMISS_IO.cimiss_model_by_time('20' + filename[0:8],
valid_time=fhour - 6,
data_code=utl.CMISS_data_code(
data_source=model,
var_name='TPE'),
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE2 is None:
return
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')
r6 = TPE1.copy(deep=True)
r6['data'].values = TPE1['data'].values - TPE2['data'].values
# prepare data
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
gh = utl.cut_xrdata(map_extent, gh, 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)
r6 = utl.cut_xrdata(map_extent, r6, delt_x=delt_x, delt_y=delt_y)
gh = utl.mask_terrian(gh_lev, psfc, gh)
u = utl.mask_terrian(uv_lev, psfc, u)
v = utl.mask_terrian(uv_lev, psfc, v)
gh.attrs['model'] = model
uv = xr.merge([u.rename({'data': 'u'}), v.rename({'data': 'v'})])
synoptic_graphics.draw_gh_uv_r6(r6=r6,
gh=gh,
uv=uv,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def dT2m_mean24(initTime=None,
fhour=48,
day_back=0,
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source='ECMWF',
var_name='T2m')
]
fhours1 = np.arange(fhour - 21, fhour + 1, 3)
if (initTime is None):
initTime = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)[0:8]
filenames1 = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours1]
if (fhour >= 48):
fhours2 = np.arange(fhour - 21 - 24, fhour + 1 - 24, 3)
filenames2 = [
initTime + '.' + str(fhour).zfill(3) for fhour in fhours2
]
if (fhour >= 36 and fhour < 48):
fhours2 = np.arange(fhour - 21 + 12 - 24, fhour + 1 + 12 - 24, 3)
initTime2 = (datetime.strptime('20' + initTime, '%Y%m%d%H') -
timedelta(hours=12)).strftime('%Y%m%d%H')[2:10]
filenames2 = [
initTime2 + '.' + str(fhour).zfill(3) for fhour in fhours2
]
if (fhour >= 24 and fhour < 36):
fhours2 = np.arange(fhour - 21 + 24 - 24, fhour + 1 + 24 - 24, 3)
initTime2 = (datetime.strptime('20' + initTime, '%Y%m%d%H') -
timedelta(hours=24)).strftime('%Y%m%d%H')[2:10]
filenames2 = [
initTime2 + '.' + str(fhour).zfill(3) for fhour in fhours2
]
if (fhour < 24):
print('fhour should > 24')
return
# prepare data
T_2m1 = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames1)
Tmn_2m1 = T_2m1.isel(time=[-1]).copy()
Tmn_2m1['data'].values[0, :, :] = np.mean(T_2m1['data'].values, axis=0)
T_2m2 = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames2)
Tmn_2m2 = T_2m2.isel(time=[-1]).copy()
Tmn_2m2['data'].values[0, :, :] = np.mean(T_2m2['data'].values, axis=0)
dTmn_2m = Tmn_2m1.copy()
dTmn_2m['data'].values = Tmn_2m1['data'].values - Tmn_2m2['data'].values
# set map extent
if (area != '全国'):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
mask1 = (dTmn_2m['lon'] > map_extent[0] - delt_x) & (
dTmn_2m['lon'] < map_extent[1] + delt_x) & (
dTmn_2m['lat'] > map_extent[2] - delt_y) & (dTmn_2m['lat'] <
map_extent[3] + delt_y)
dTmn_2m = dTmn_2m.where(mask1, drop=True)
#- to solve the problem of labels on all the contours
dTmn_2m.attrs['model'] = 'ECMWF'
dTmn_2m.attrs['title'] = '2米最低温度24小时变温'
elements_graphics.draw_dT_2m(dT_2m=dTmn_2m,
T_type='dT2m_meann',
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def periodmean_gh_uv_pwat_ulj(initTimes=None,
fhours=[0],
day_back=0,
model='ECMWF',
gh_lev=500,
uv_lev=850,
ulj_lev=200,
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
data_source='MICAPS',
Global=False,
**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=gh_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=uv_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=uv_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=ulj_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=ulj_lev),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='TCWV'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PSFC')
]
except KeyError:
raise ValueError('Can not find all directories needed')
filenames = []
# get filename
if (initTimes != None):
for initTime in initTimes:
for fhour in fhours:
filenames.append(utl.model_filename(initTime, fhour))
else:
filenames = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
# retrieve data from micaps server
gh = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
u = MICAPS_IO.get_model_grids(data_dir[1], filenames=filenames)
v = MICAPS_IO.get_model_grids(data_dir[2], filenames=filenames)
u2 = MICAPS_IO.get_model_grids(data_dir[3], filenames=filenames)
v2 = MICAPS_IO.get_model_grids(data_dir[4], filenames=filenames)
pwat = MICAPS_IO.get_model_grids(data_dir[5], filenames=filenames)
psfc = MICAPS_IO.get_model_grids(data_dir[6], filenames=filenames)
if (data_source == 'CIMISS'):
# get filename
filenames = []
if (initTimes != None):
for initTime in initTimes:
for fhour in fhours:
filenames.append(
'20' + utl.model_filename(initTime, fhour, UTC=True))
else:
filenames = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)
try:
# retrieve data from CIMISS server
gh = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='GPH'),
filenames=filenames,
fcst_level=gh_lev,
fcst_ele="GPH",
units='gpm')
gh['data'].values = gh['data'].values / 10.
u = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='WIU'),
filenames=filenames,
fcst_level=uv_lev,
fcst_ele="WIU",
units='m/s')
v = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='WIV'),
filenames=filenames,
fcst_level=uv_lev,
fcst_ele="WIV",
units='m/s')
u2 = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='WIU'),
filenames=filenames,
fcst_level=ulj_lev,
fcst_ele="WIU",
units='m/s')
v2 = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='WIV'),
filenames=filenames,
fcst_level=ulj_lev,
fcst_ele="WIV",
units='m/s')
if (model == 'ECMWF'):
pwat = utl.cimiss_model_ana_grids(
data_code=utl.CMISS_data_code(data_source=model,
var_name='TCWV'),
filenames=filenames,
fcst_level=0,
fcst_ele="TCWV",
units='kg m-2')
else:
pwat = utl.cimiss_model_ana_grids(
data_code=utl.CMISS_data_code(data_source=model,
var_name='TIWV'),
filenames=filenames,
fcst_level=0,
fcst_ele="TIWV",
units='kg m-2')
psfc = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='PRS'),
filenames=filenames,
fcst_level=0,
fcst_ele="PRS",
units='Pa')
psfc['data'] = psfc['data'] / 100.
except KeyError:
raise ValueError('Can not find all data needed')
# prepare data
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=cntr_pnt,
zoom_ratio=zoom_ratio,
map_ratio=map_ratio)
gh = utl.cut_xrdata(map_extent, gh, 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)
u2 = utl.cut_xrdata(map_extent, u2, delt_x=delt_x, delt_y=delt_y)
v2 = utl.cut_xrdata(map_extent, v2, delt_x=delt_x, delt_y=delt_y)
pwat = utl.cut_xrdata(map_extent, pwat, delt_x=delt_x, delt_y=delt_y)
gh = utl.mask_terrian(gh_lev, psfc, gh)
u = utl.mask_terrian(uv_lev, psfc, u)
v = utl.mask_terrian(uv_lev, psfc, v)
u2 = utl.mask_terrian(ulj_lev, psfc, u2)
v2 = utl.mask_terrian(ulj_lev, psfc, v2)
uv = xr.merge([u.rename({'data': 'u'}), v.rename({'data': 'v'})])
ulj = mpcalc.wind_speed(u2['data'].values * units('m/s'),
v2['data'].values * units('m/s'))
ulj_xr = u2.copy(deep=True)
ulj_xr['data'].values = ulj.magnitude
pwat_mean = pwat.mean('time')
gh_mean = gh.mean('time')
ulj_mean = ulj_xr.mean('time')
uv_mean = uv.mean('time')
gh_mean.attrs['model'] = model
gh_mean.attrs['st_time'] = gh['time'].values[0]
gh_mean.attrs['ed_time'] = gh['time'].values[-1]
synoptic_graphics.draw_gh_uv_pwat_ulj(pwat=pwat_mean,
gh=gh_mean,
uv=uv_mean,
ulj=ulj_mean,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir)
def gh_uv_wsp(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
gh_lev=500,
uv_lev=850,
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
data_source='MICAPS',
Global=False,
**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=gh_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=uv_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=uv_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
print(initTime)
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
psfc = MICAPS_IO.get_model_grid(data_dir[3], 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:
# retrieve data from CIMISS server
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=gh_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=uv_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=uv_lev,
fcst_ele="WIV",
units='m/s')
if v is None:
return
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')
# prepare data
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)
gh = utl.mask_terrian(gh_lev, psfc, gh)
u = utl.mask_terrian(uv_lev, psfc, u)
v = utl.mask_terrian(uv_lev, psfc, v)
#+ to solve the problem of labels on all the contours
gh = utl.cut_xrdata(map_extent, gh, 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)
#- to solve the problem of labels on all the contours
uv = xr.merge([u.rename({'data': 'u'}), v.rename({'data': 'v'})])
wsp = (u['data']**2 + v['data']**2)**0.5
gh.attrs['model'] = model
synoptic_graphics.draw_gh_uv_wsp(wsp=wsp,
gh=gh,
uv=uv,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def gh_uv_VVEL(initial_time=None,
fhour=6,
day_back=0,
model='ECMWF',
gh_lev='500',
uvw_lev='850',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
if (area != '全国'):
south_China_sea = False
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl=gh_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=uvw_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=uvw_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VVEL',
lvl=uvw_lev)
]
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
gh = get_model_grid(data_dir[0], filename=filename)
if gh is None:
return
u = get_model_grid(data_dir[1], filename=filename)
if u is None:
return
v = get_model_grid(data_dir[2], filename=filename)
if v is None:
return
w = get_model_grid(data_dir[3], filename=filename)
init_time = gh.coords['forecast_reference_time'].values
# prepare data
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
idx_x1 = np.where((gh.coords['lon'].values > map_extent[0] - delt_x)
& (gh.coords['lon'].values < map_extent[1] + delt_x))
idx_y1 = np.where((gh.coords['lat'].values > map_extent[2] - delt_y)
& (gh.coords['lat'].values < map_extent[3] + delt_y))
idx_x2 = np.where((w.coords['lon'].values > map_extent[0] - delt_x)
& (w.coords['lon'].values < map_extent[1] + delt_x))
idx_y2 = np.where((w.coords['lat'].values > map_extent[2] - delt_y)
& (w.coords['lat'].values < map_extent[3] + delt_y))
#- to solve the problem of labels on all the contours
gh = {
'lon':
gh.coords['lon'].values[idx_x1],
'lat':
gh.coords['lat'].values[idx_y1],
'data':
gh['data'].values[0, 0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'lev':
gh_lev,
'model':
model,
'fhour':
fhour,
'init_time':
init_time
}
uv = {
'lon':
u.coords['lon'].values[idx_x1],
'lat':
u.coords['lat'].values[idx_y1],
'udata':
u['data'].values[0, 0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'vdata':
v['data'].values[0, 0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'lev':
uvw_lev
}
VVEL = {
'lon':
w.coords['lon'].values[idx_x2],
'lat':
w.coords['lat'].values[idx_y2],
'data':
np.squeeze(w['data'].values[0, 0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)]),
'lev':
uvw_lev
}
dynamic_graphics.draw_gh_uv_VVEL(VVEL=VVEL,
gh=gh,
uv=uv,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def cumulated_precip(initTime=None,
t_gap=6,
t_range=[6, 36],
day_back=0,
model='ECMWF',
data_source='MICAPS',
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
Global=False,
**kwargs):
fhours = np.arange(t_range[0], t_range[1] + 1, t_gap)
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % t_gap)
]
except KeyError:
raise ValueError('Can not find all directories needed')
if (initTime == None):
initTime = MICAPS_IO.get_latest_initTime(data_dir[0])
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
# retrieve data from micaps server
rain = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
rain2 = rain.sum('time')
if (data_source == 'CIMISS'):
if (initTime != None):
filename = utl.model_filename(initTime, 0, UTC=True)
else:
filename = utl.filename_day_back_model(day_back=0,
fhour=0,
UTC=True)
try:
TPE1 = CMISS_IO.cimiss_model_by_time('20' + filename[0:8],
valid_time=fhours[0],
data_code=utl.CMISS_data_code(
data_source=model,
var_name='TPE'),
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE1 is None:
return
TPE2 = CMISS_IO.cimiss_model_by_time('20' + filename[0:8],
valid_time=fhours[-1],
data_code=utl.CMISS_data_code(
data_source=model,
var_name='TPE'),
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE2 is None:
return
except KeyError:
raise ValueError('Can not find all data needed')
rain = TPE1.copy(deep=True)
rain['data'].values = (TPE2['data'].values - TPE1['data'].values)
rain2 = rain.sum('time')
# set map extent
if (area != None):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
else:
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
rain = utl.cut_xrdata(map_extent=map_extent,
xr_input=rain,
delt_y=delt_y,
delt_x=delt_x)
rain2.attrs['model'] = model
rain2.attrs['t_gap'] = t_gap
rain2.attrs['initTime'] = datetime.strptime(initTime, '%y%m%d%H')
rain2.attrs['fhour1'] = fhours[0]
rain2.attrs['fhour2'] = fhours[-1]
# draw
QPF_graphics.draw_cumulated_precip(rain=rain2,
map_extent=map_extent,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def cumulated_precip_evo(initTime=None,
t_gap=6,
t_range=[6, 36],
day_back=0,
model='ECMWF',
data_source='MICAPS',
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
Global=False,
**kwargs):
fhours = np.arange(t_range[0], t_range[1] + 1, t_gap)
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % t_gap)
]
except KeyError:
raise ValueError('Can not find all directories needed')
if (initTime == None):
initTime = MICAPS_IO.get_latest_initTime(data_dir[0])
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
# retrieve data from micaps server
rain = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
rain2 = rain.copy(deep=True)
for itime in range(1, len(rain['forecast_period'].values)):
rain2['data'].values[itime, :, :] = np.sum(
rain['data'].values[0:itime + 1, :, :], axis=0)
if (data_source == 'CIMISS'):
if (initTime != None):
filename = utl.model_filename(initTime, 0, UTC=True)
else:
filename = utl.filename_day_back_model(day_back=0,
fhour=0,
UTC=True)
try:
TPE1 = CMISS_IO.cimiss_model_by_times(
'20' + filename[0:8],
valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TPE'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
except KeyError:
raise ValueError('Can not find all data needed')
rain = TPE1.copy(deep=True)
rain['data'].values = (TPE1['data'].values)
# set map extent
if (area != None):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
else:
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
mask1 = (rain['lon'] > map_extent[0] - delt_x) & (
rain['lon'] < map_extent[1] + delt_x) & (
rain['lat'] > map_extent[2] - delt_y) & (rain['lat'] <
map_extent[3] + delt_y)
rain2 = rain2.where(mask1, drop=True)
rain2.attrs['model'] = model
rain2.attrs['t_gap'] = t_gap
# draw
QPF_graphics.draw_cumulated_precip_evo(rain=rain2,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def mslp_gust10m(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
if (area != '全国'):
south_China_sea = False
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PRMSL'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='10M_GUST_6H')
]
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
mslp = get_model_grid(data_dir[0], filename=filename)
if mslp is None:
return
gust = get_model_grid(data_dir[1], filename=filename)
if gust is None:
return
init_time = mslp.coords['forecast_reference_time'].values
# prepare data
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
mask1 = ((mslp['lon'] >
(map_extent[0] - delt_x)) & (mslp['lon'] <
(map_extent[1] + delt_x)) &
(mslp['lat'] >
(map_extent[2] - delt_y)) & (mslp['lat'] <
(map_extent[3] + delt_y)))
mask2 = ((mslp['lon'] >
(map_extent[0] - delt_x)) & (mslp['lon'] <
(map_extent[1] + delt_x)))
mask3 = ((mslp['lat'] >
(map_extent[2] - delt_y)) & (mslp['lat'] <
(map_extent[3] + delt_y)))
#- to solve the problem of labels on all the contours
mslp = {
'lon': mslp.coords['lon'].where(mask2, drop=True).values,
'lat': mslp.coords['lat'].where(mask3, drop=True).values,
'data': np.squeeze(mslp['data'].where(mask1, drop=True).values),
'model': model,
'fhour': fhour,
'init_time': init_time
}
gust = {
'lon': gust.coords['lon'].where(mask2, drop=True).values,
'lat': gust.coords['lat'].where(mask3, drop=True).values,
'data': np.squeeze(gust['data'].where(mask1, drop=True).values),
}
elements_graphics.draw_mslp_gust10m(gust=gust,
mslp=mslp,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def PV_Div_uv(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
levels=[
1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250,
200, 100
],
lvl_ana=250,
Global=False,
south_China_sea=True,
area=None,
city=False,
output_dir=None,
data_source='MICAPS',
**kwargs):
# micaps data directory
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='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='')
]
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 = MICAPS_IO.get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
u = MICAPS_IO.get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
v = MICAPS_IO.get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
t = MICAPS_IO.get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if t is None:
return
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:
# retrieve data from CIMISS server
rh = CMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='RHU'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
fcst_levels=levels,
fcst_ele="RHU",
units='%')
if rh is None:
return
u = CMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIU'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
fcst_levels=levels,
fcst_ele="WIU",
units='m/s')
if u is None:
return
v = CMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIV'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
fcst_levels=levels,
fcst_ele="WIV",
units='m/s')
if v is None:
return
t = CMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='TEM'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
fcst_levels=levels,
fcst_ele="TEM",
units='K')
if t is None:
return
t['data'].values = t['data'].values - 273.15
t['data'].attrs['units'] = 'C'
except KeyError:
raise ValueError('Can not find all data needed')
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
mask1 = (rh['lon'] >
map_extent[0] - delt_x) & (rh['lon'] < map_extent[1] + delt_x) & (
rh['lat'] > map_extent[2] - delt_y) & (rh['lat'] <
map_extent[3] + delt_y)
mask2 = (u['lon'] >
map_extent[0] - delt_x) & (u['lon'] < map_extent[1] + delt_x) & (
u['lat'] > map_extent[2] - delt_y) & (u['lat'] <
map_extent[3] + delt_y)
mask3 = (t['lon'] >
map_extent[0] - delt_x) & (t['lon'] < map_extent[1] + delt_x) & (
t['lat'] > map_extent[2] - delt_y) & (t['lat'] <
map_extent[3] + delt_y)
#- to solve the problem of labels on all the contours
rh = rh.where(mask1, drop=True)
u = u.where(mask2, drop=True)
v = v.where(mask2, drop=True)
t = t.where(mask3, drop=True)
uv = xr.merge([u.rename({'data': 'u'}), v.rename({'data': 'v'})])
lats = np.squeeze(rh['lat'].values)
lons = np.squeeze(rh['lon'].values)
pres = np.array(levels) * 100 * units('Pa')
tmpk = mpcalc.smooth_n_point(
(t['data'].values.squeeze() + 273.15), 9, 2) * units('kelvin')
thta = mpcalc.potential_temperature(pres[:, None, None], tmpk)
uwnd = mpcalc.smooth_n_point(u['data'].values.squeeze(), 9,
2) * units.meter / units.second
vwnd = mpcalc.smooth_n_point(v['data'].values.squeeze(), 9,
2) * units.meter / units.second
dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)
# Comput the PV on all isobaric surfaces
pv_raw = mpcalc.potential_vorticity_baroclinic(
thta, pres[:, None, None], uwnd, vwnd, dx[None, :, :], dy[None, :, :],
lats[None, :, None] * units('degrees'))
div_raw = mpcalc.divergence(uwnd,
vwnd,
dx[None, :, :],
dy[None, :, :],
dim_order='yx')
# prepare data
idx_z1 = list(pres.m).index(((lvl_ana * units('hPa')).to(pres.units)).m)
pv = rh.copy(deep=True)
pv['data'].values = np.array(pv_raw).reshape(
np.append(1,
np.array(pv_raw).shape))
pv['data'].attrs['units'] = str(pv_raw.units)
pv.attrs['model'] = model
pv = pv.where(pv['level'] == lvl_ana, drop=True)
div = u.copy(deep=True)
div['data'].values = np.array(div_raw).reshape(
np.append(1,
np.array(div_raw).shape))
div['data'].attrs['units'] = str(div_raw.units)
div = div.where(div['level'] == lvl_ana, drop=True)
uv = uv.where(uv['level'] == lvl_ana, drop=True)
synoptic_graphics.draw_PV_Div_uv(pv=pv,
uv=uv,
div=div,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def low_level_wind(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
wind_level='100m',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
if (area != '全国'):
south_China_sea = False
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='u' + wind_level),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='v' + wind_level)
]
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
u10m = get_model_grid(data_dir[0], filename=filename)
if u10m is None:
return
v10m = get_model_grid(data_dir[1], filename=filename)
if v10m is None:
return
init_time = v10m.coords['forecast_reference_time'].values
# prepare data
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
mask1 = ((u10m['lon'] >
(map_extent[0] - delt_x)) & (u10m['lon'] <
(map_extent[1] + delt_x)) &
(u10m['lat'] >
(map_extent[2] - delt_y)) & (u10m['lat'] <
(map_extent[3] + delt_y)))
mask2 = ((u10m['lon'] >
(map_extent[0] - delt_x)) & (u10m['lon'] <
(map_extent[1] + delt_x)))
mask3 = ((u10m['lat'] >
(map_extent[2] - delt_y)) & (u10m['lat'] <
(map_extent[3] + delt_y)))
#- to solve the problem of labels on all the contours
uv10m = {
'lon': u10m.coords['lon'].where(mask2, drop=True).values,
'lat': u10m.coords['lat'].where(mask3, drop=True).values,
'lev': wind_level,
'udata': np.squeeze(u10m['data'].where(mask1, drop=True).values),
'vdata': np.squeeze(v10m['data'].where(mask1, drop=True).values),
'model': model,
'fhour': fhour,
'init_time': init_time
}
wsp10m = {
'lon': u10m.coords['lon'].where(mask2, drop=True).values,
'lat': u10m.coords['lat'].where(mask3, drop=True).values,
'data': ((uv10m['udata'])**2 + (uv10m['vdata'])**2)**0.5
}
elements_graphics.draw_low_level_wind(uv=uv10m,
wsp=wsp10m,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def dT2m_mx24(initTime=None, fhour=48,
map_ratio=19/9,zoom_ratio=20,cntr_pnt=[102,34],area=None,south_China_sea=True,
**kargws):
data_dir = [utl.Cassandra_dir(data_type='surface',data_source='中央气象台中短期指导',var_name='Tmx_2m')]
fhours1 = np.arange(fhour-24, fhour+1, 24)
if(initTime is None):
initTime=utl.filename_day_back_model(day_back=day_back,fhour=fhour)[0:8]
filename1 = initTime+'.'+str(fhour).zfill(3)
if(fhour >= 48):
fhour2 = fhour-24
filename2 = initTime+'.'+str(fhour2).zfill(3)
if(fhour >=36 and fhour < 48):
fhour2 = fhour-12
initTime2=(datetime.strptime('20'+initTime,'%Y%m%d%H')-timedelta(hours=12)).strftime('%Y%m%d%H')[2:10]
filename2=initTime2+'.'+str(fhour2).zfill(3)
if(fhour >=24 and fhour < 36):
fhour2 = fhour
initTime2=(datetime.strptime('20'+initTime,'%Y%m%d%H')-timedelta(hours=24)).strftime('%Y%m%d%H')[2:10]
filename2=initTime2+'.'+str(fhour2).zfill(3)
if(fhour < 24):
print('fhour should > 24')
return
# prepare data
T_2m1 = MICAPS_IO.get_model_grid(data_dir[0], filename=filename1)
T_2m2 = MICAPS_IO.get_model_grid(data_dir[0], filename=filename2)
dTmx_2m=T_2m1.copy()
dTmx_2m['data'].values=T_2m1['data'].values-T_2m2['data'].values
# set map extent
if(area != '全国'):
south_China_sea=False
if(area != None):
cntr_pnt,zoom_ratio=utl.get_map_area(area_name=area)
map_extent=[0,0,0,0]
map_extent[0]=cntr_pnt[0]-zoom_ratio*1*map_ratio
map_extent[1]=cntr_pnt[0]+zoom_ratio*1*map_ratio
map_extent[2]=cntr_pnt[1]-zoom_ratio*1
map_extent[3]=cntr_pnt[1]+zoom_ratio*1
delt_x=(map_extent[1]-map_extent[0])*0.2
delt_y=(map_extent[3]-map_extent[2])*0.1
#+ to solve the problem of labels on all the contours
mask1 = (dTmx_2m['lon'] > map_extent[0]-delt_x) & (dTmx_2m['lon'] < map_extent[1]+delt_x) & (dTmx_2m['lat'] > map_extent[2]-delt_y) & (dTmx_2m['lat'] < map_extent[3]+delt_y)
dTmx_2m=dTmx_2m.where(mask1,drop=True)
#- to solve the problem of labels on all the contours
dTmx2=xr.DataArray(np.squeeze(dTmx_2m['data'].values,axis=0),name='data',
coords={'time':('time',[dTmx_2m['time'].values[0]]),
'fhour':('time',[fhour]),
'lat':('lat',dTmx_2m['lat'].values),
'lon':('lon',dTmx_2m['lon'].values)
},
dims=('time','lat','lon'),
attrs={'model_name':'中央气象台中短期指导',
'var_name':'2米最高温度24小时变温',
'vhours':24})
draw_SCMOC.draw_dT2m(dTmx2,map_extent=map_extent,south_China_sea=south_China_sea,**kargws)
def point_uv_ecgust_tmp_rh_rn_fcst(output_dir=None,
t_range=[0, 60],
t_gap=3,
points={
'lon': [116.3833],
'lat': [39.9],
'altitude': [1351]
},
initTime=None,
day_back=0,
extra_info={
'output_head_name': ' ',
'output_tail_name': ' ',
'point_name': ' '
},
**kwargs):
#+get all the directories needed
try:
dir_rqd = [
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='T2m'),
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='u10m'),
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='v10m'),
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='rh2m'),
utl.Cassandra_dir(data_type='surface',
data_source='中央气象台中短期指导',
var_name='RAIN' + str(t_gap).zfill(2)),
utl.Cassandra_dir(data_type='surface',
data_source='ECMWF',
var_name='10M_GUST_3H')
]
except KeyError:
raise ValueError('Can not find all required directories needed')
#-get all the directories needed
if (initTime == None):
initTime = MICAPS_IO.get_latest_initTime(dir_rqd[0])
initTime2 = MICAPS_IO.get_latest_initTime(dir_rqd[-1])
#initTime=utl.filename_day_back_model(day_back=day_back,fhour=0)[0:8]
fhours = np.arange(t_range[0], t_range[1], t_gap)
fhours2 = np.arange(t_range[0], t_range[1] + 12, t_gap)
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
filenames2 = [initTime2 + '.' + str(fhour).zfill(3) for fhour in fhours2]
t2m = utl.get_model_points_gy(dir_rqd[0],
filenames,
points,
allExists=False)
u10m = utl.get_model_points_gy(dir_rqd[1],
filenames,
points,
allExists=False)
v10m = utl.get_model_points_gy(dir_rqd[2],
filenames,
points,
allExists=False)
rh = utl.get_model_points_gy(dir_rqd[3],
filenames,
points,
allExists=False)
rn = utl.get_model_points_gy(dir_rqd[4],
filenames,
points,
allExists=False)
gust = utl.get_model_points_gy(dir_rqd[5],
filenames2,
points,
allExists=False)
sta_graphics.draw_point_uv_tmp_rh_rn_gust_fcst(t2m=t2m,
u10m=u10m,
v10m=v10m,
rh=rh,
rn=rn,
gust=gust,
model='中央气象台中短期指导',
output_dir=output_dir,
points=points,
extra_info=extra_info)
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)
def Station_Snow_Synthetical_Forecast_From_Cassandra(
model='ECMWF',
output_dir=None,
t_range=[0,84],
t_gap=3,
points={'lon':[116.3833], 'lat':[39.9]},
initTime=None,
draw_VIS=True,drw_thr=False,
extra_info={
'output_head_name':' ',
'output_tail_name':' ',
'point_name':' '}
):
#+get all the directories needed
try:
dir_rqd=[
"ECMWF_HR/10_METRE_WIND_GUST_IN_THE_LAST_3_HOURS/",
"ECMWF_HR/10_METRE_WIND_GUST_IN_THE_LAST_6_HOURS/",
"ECMWF_HR/SNOD/",
"ECMWF_HR/SDEN/",
"ECMWF_HR/UGRD_100M/",
"ECMWF_HR/VGRD_100M/",
"NWFD_SCMOC/VIS/",
"NCEP_GFS_HR/SNOD/",
"ECMWF_HR/SNOW06/",
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='T2m'),
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='u10m'),
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='v10m'),
'ECMWF_ENSEMBLE/RAW/SNOW06/'
]
except KeyError:
raise ValueError('Can not find all required directories needed')
try:
dir_opt=[
utl.Cassandra_dir(
data_type='surface',data_source=model,var_name='Td2m')
]
name_opt=['Td2m']
except:
dir_opt=[
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='rh2m')
]
name_opt=['rh2m']
#+get all the directories needed
if(initTime == None):
last_file={model:get_latest_initTime(dir_rqd[0]),
'SCMOC':get_latest_initTime(dir_rqd[6]),
}
else:
last_file={model:initTime[0],
'SCMOC':initTime[1],
}
y_s={model:int('20'+last_file[model][0:2]),
'SCMOC':int('20'+last_file['SCMOC'][0:2])}
m_s={model:int(last_file[model][2:4]),
'SCMOC':int(last_file['SCMOC'][2:4])}
d_s={model:int(last_file[model][4:6]),
'SCMOC':int(last_file['SCMOC'][4:6])}
h_s={model:int(last_file[model][6:8]),
'SCMOC':int(last_file['SCMOC'][6:8])}
fhours = np.arange(t_range[0], t_range[1], t_gap)
for ifhour in fhours:
if (ifhour == fhours[0] ):
time_all=datetime(y_s['SCMOC'],m_s['SCMOC'],d_s['SCMOC'],h_s['SCMOC'])+timedelta(hours=int(ifhour))
else:
time_all=np.append(time_all,datetime(y_s['SCMOC'],m_s['SCMOC'],d_s['SCMOC'],h_s['SCMOC'])+timedelta(hours=int(ifhour)))
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
t2m=utl.get_model_points_gy(dir_rqd[9], filenames, points,allExists=False)
if(name_opt[0] == 'rh2m'):
rh2m=utl.get_model_points_gy(dir_opt[0], filenames, points,allExists=False)
Td2m=mpcalc.dewpoint_rh(t2m['data'].values*units('degC'),rh2m['data'].values/100.)
p_vapor=(rh2m['data'].values/100.)*6.105*(math.e**((17.27*t2m['data'].values/(237.7+t2m['data'].values))))
if(name_opt[0] == 'Td2m'):
Td2m=utl.get_model_points_gy(dir_opt[0], filenames, points,allExists=False)
rh2m=mpcalc.relative_humidity_from_dewpoint(t2m['data'].values* units('degC'),
Td2m['data'].values* units('degC'))
p_vapor=(np.array(rh2m))*6.105*(math.e**((17.27*t2m['data'].values/(237.7+t2m['data'].values))))
Td2m=np.array(Td2m['data'].values)* units('degC')
#SN06_ensm=utl.get_model_points_gy(dir_rqd[12], filenames, points,allExists=False)
'''
for i in range(0,len(SN06_ensm['forecast_period'])):
SN06_std=np.std(np.squeeze(SN06_ensm['data'].values[i,:]))
SN06_mean=np.mean(np.squeeze(SN06_ensm['data'].values[i,:]))
if(i == 0):
SN06_01=norm.pdf(0.01, SN06_mean, SN06_std)
SN06_10=norm.pdf(0.1, SN06_mean, SN06_std)
SN06_25=norm.pdf(0.25, SN06_mean, SN06_std)
SN06_50=norm.pdf(0.5, SN06_mean, SN06_std)
SN06_75=norm.pdf(0.75, SN06_mean, SN06_std)
SN06_90=norm.pdf(0.9, SN06_mean, SN06_std)
SN06_99=norm.pdf(0.99, SN06_mean, SN06_std)
if(i > 0):
SN06_01=[SN06_01,norm.pdf(0.01, SN06_mean, SN06_std)]
SN06_10=[SN06_10,norm.pdf(0.1, SN06_mean, SN06_std)]
SN06_25=[SN06_25,norm.pdf(0.25, SN06_mean, SN06_std)]
SN06_50=[SN06_50,norm.pdf(0.5, SN06_mean, SN06_std)]
SN06_75=[SN06_75,norm.pdf(0.75, SN06_mean, SN06_std)]
SN06_90=[SN06_90,norm.pdf(0.9, SN06_mean, SN06_std)]
SN06_99=[SN06_99,norm.pdf(0.99, SN06_mean, SN06_std)]
SN06_ensm_stc={
'SN06_01'=SN06_01
'SN06_10'=SN06_10
'SN06_25'=SN06_25
'SN06_50'=SN06_50
'SN06_75'=SN06_75
'SN06_90'=SN06_90
'SN06_99'=SN06_99
}
'''
u10m=utl.get_model_points_gy(dir_rqd[10], filenames, points,allExists=False)
v10m=utl.get_model_points_gy(dir_rqd[11], filenames, points,allExists=False)
wsp10m=(u10m['data']**2+v10m['data']**2)**0.5
AT=1.07*t2m['data'].values+0.2*p_vapor-0.65*wsp10m-2.7
#https://en.wikipedia.org/wiki/Wind_chill
TWC=13.12+0.6215*t2m['data'].values-11.37*(wsp10m**0.16)+0.3965*t2m['data'].values*(wsp10m**0.16)
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [last_file['SCMOC']+'.'+str(fhour).zfill(3) for fhour in fhours]
VIS=utl.get_model_points_gy(dir_rqd[6], filenames, points,allExists=False,fill_null=True,Null_value=-0.001)
if(last_file['SCMOC'] == last_file[model] and t_range[1] > 72):
fhours = np.append(np.arange(3,72,3),np.arange(72, (t_range[1]), 6))
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
filenames2 = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
if(last_file['SCMOC'] != last_file[model] and t_range[1] > 60):
fhours = np.append(np.arange(3,60,3),np.arange(60, (t_range[1]), 6))
filenames = [last_file[model]+'.'+str(fhour+12).zfill(3) for fhour in fhours]
filenames2 = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
if(last_file['SCMOC'] != last_file[model] and t_range[1] <= 60):
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [last_file[model]+'.'+str(fhour+12).zfill(3) for fhour in fhours]
filenames2 = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
if(last_file['SCMOC'] == last_file[model] and t_range[1] <= 72):
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
filenames2 = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
SNOD1=utl.get_model_points_gy(dir_rqd[2], filenames2, points,allExists=False)
SNOD2=utl.get_model_points_gy(dir_rqd[7], filenames2, points,allExists=False)
SDEN=utl.get_model_points_gy(dir_rqd[3], filenames2, points,allExists=False)
SN06=utl.get_model_points_gy(dir_rqd[8], filenames2, points,allExists=False)
u100m=utl.get_model_points_gy(dir_rqd[4], filenames2, points,allExists=False)
v100m=utl.get_model_points_gy(dir_rqd[5], filenames2, points,allExists=False)
wsp100m=(u100m['data']**2+v100m['data']**2)**0.5
if(fhours[-1] < 120):
gust10m=utl.get_model_points_gy(dir_rqd[0], filenames, points,allExists=False)
if(fhours[-1] > 120):
if(last_file['SCMOC'] == last_file[model]):
fhours = np.arange(0, t_range[1], 6)
filenames = [last_file[model]+'.'+str(fhour).zfill(3) for fhour in fhours]
if(last_file['SCMOC'] != last_file[model]):
fhours = np.arange(0, t_range[1], 6)
filenames = [last_file[model]+'.'+str(fhour+12).zfill(3) for fhour in fhours]
gust10m=utl.get_model_points_gy(dir_rqd[1], filenames, points,allExists=False)
sta_graphics.draw_Station_Snow_Synthetical_Forecast_From_Cassandra(
TWC=TWC,AT=AT,u10m=u10m,v10m=v10m,u100m=u100m,v100m=v100m,
gust10m=gust10m,wsp10m=wsp10m,wsp100m=wsp100m,SNOD1=SNOD1,SNOD2=SNOD2,SDEN=SDEN,SN06=SN06,
draw_VIS=draw_VIS,VIS=VIS,drw_thr=drw_thr,
time_all=time_all,
model=model,points=points,
output_dir=output_dir,extra_info=extra_info)
def point_fcst_according_to_3D_field(
model='ECMWF',
output_dir=None,
t_range=[0,60],
t_gap=3,
points={'lon':[116.3833], 'lat':[39.9], 'altitude':[1351]},
initTime=None,obs_ID=54511,day_back=0,
extra_info={
'output_head_name':' ',
'output_tail_name':' ',
'point_name':' ',
'drw_thr':True,
'levels_for_interp':[1000, 950, 925, 900, 850, 800, 700, 600, 500]}
):
try:
dir_rqd=[utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',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='surface',data_source=model,var_name='RAIN'+str(t_gap).zfill(2))]
except KeyError:
raise ValueError('Can not find all required directories needed')
#-get all the directories needed
if(initTime == None):
initTime = get_latest_initTime(dir_rqd[0][0:-1]+'/850')
#initTime=utl.filename_day_back_model(day_back=day_back,fhour=0)[0:8]
directory=dir_rqd[0][0:-1]
fhours = np.arange(t_range[0], t_range[1], t_gap)
filenames = [initTime+'.'+str(fhour).zfill(3) for fhour in fhours]
HGT_4D=get_model_3D_grids(directory=directory,filenames=filenames,levels=extra_info['levels_for_interp'], allExists=False)
directory=dir_rqd[1][0:-1]
U_4D=get_model_3D_grids(directory=directory,filenames=filenames,levels=extra_info['levels_for_interp'], allExists=False)
directory=dir_rqd[2][0:-1]
V_4D=get_model_3D_grids(directory=directory,filenames=filenames,levels=extra_info['levels_for_interp'], allExists=False)
directory=dir_rqd[3][0:-1]
TMP_4D=get_model_3D_grids(directory=directory,filenames=filenames,levels=extra_info['levels_for_interp'], allExists=False)
rn=utl.get_model_points_gy(dir_rqd[4], filenames, points,allExists=False)
directory=dir_rqd[3][0:-1]
coords_info_2D=utl.get_model_points_gy(directory+str(extra_info['levels_for_interp'][0])+'/',
points=points,filenames=filenames,allExists=False)
delt_xy=HGT_4D['lon'].values[1]-HGT_4D['lon'].values[0]
mask = (HGT_4D['lon']<(points['lon']+2*delt_xy))&(HGT_4D['lon']>(points['lon']-2*delt_xy))&(HGT_4D['lat']<(points['lat']+2*delt_xy))&(HGT_4D['lat']>(points['lat']-2*delt_xy))
HGT_4D_sm=HGT_4D['data'].where(mask,drop=True)
U_4D_sm=U_4D['data'].where(mask,drop=True)
V_4D_sm=V_4D['data'].where(mask,drop=True)
TMP_4D_sm=U_4D['data'].where(mask,drop=True)
lon_md=np.squeeze(HGT_4D_sm['lon'].values)
lat_md=np.squeeze(HGT_4D_sm['lat'].values)
alt_md=np.squeeze(HGT_4D_sm.values*10).flatten()
time_md=np.squeeze(HGT_4D_sm['forecast_period'].values)
coords = np.zeros((len(time_md),len(extra_info['levels_for_interp']),len(lat_md),len(lon_md),4))
coords[...,0]=time_md.reshape((len(time_md),1,1,1))
coords[...,2] = lat_md.reshape((1,1,len(lat_md),1))
coords[...,3] = lon_md.reshape((1,1,1,len(lon_md)))
coords = coords.reshape((alt_md.size,4))
coords[:,1]=alt_md
interpolator_U = LinearNDInterpolator(coords,U_4D_sm.values.reshape((U_4D_sm.values.size)),rescale=True)
interpolator_V = LinearNDInterpolator(coords,V_4D_sm.values.reshape((V_4D_sm.values.size)),rescale=True)
interpolator_TMP = LinearNDInterpolator(coords,TMP_4D_sm.values.reshape((TMP_4D_sm.values.size)),rescale=True)
coords2 = np.zeros((len(time_md),1,1,1,4))
coords2[...,0]=time_md.reshape((len(time_md),1,1,1))
coords2[...,1]=points['altitude'][0]
coords2[...,2] = points['lat'][0]
coords2[...,3] = points['lon'][0]
coords2 = coords2.reshape((time_md.size,4))
U_interped=np.squeeze(interpolator_U(coords2))
V_interped=np.squeeze(interpolator_V(coords2))
TMP_interped=np.squeeze(interpolator_TMP(coords2))
U_interped_xr=coords_info_2D.copy()
U_interped_xr['data'].values=U_interped.reshape(U_interped.size,1,1)
V_interped_xr=coords_info_2D.copy()
V_interped_xr['data'].values=V_interped.reshape(V_interped.size,1,1)
TMP_interped_xr=coords_info_2D.copy()
TMP_interped_xr['data'].values=TMP_interped.reshape(TMP_interped.size,1,1)
sta_graphics.draw_point_fcst(t2m=TMP_interped_xr,u10m=U_interped_xr,v10m=V_interped_xr,rn=rn,
model=model,
output_dir=output_dir,
points=points,
extra_info=extra_info
)
def wind_rh_according_to_4D_data(
initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
sta_fcs={
'lon': [101.82, 101.32, 101.84, 102.23, 102.2681],
'lat': [28.35, 27.91, 28.32, 27.82, 27.8492],
'altitude': [3600, 3034.62, 3240, 1669, 1941.5],
'name': ['健美乡', '项脚乡', '\n锦屏镇', '\n马道镇', 'S9005 ']
},
draw_zd=True,
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500],
map_ratio=19 / 9,
zoom_ratio=1,
south_China_sea=False,
area='全国',
city=False,
output_dir=None,
bkgd_type='satellite',
data_source='MICAPS'):
# micaps data directory
if (area != '全国'):
south_China_sea = False
# prepare data
if (area != '全国'):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
cntr_pnt = np.append(np.mean(sta_fcs['lon']), np.mean(sta_fcs['lat']))
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
bkgd_level = utl.cal_background_zoom_ratio(zoom_ratio)
# micaps data directory
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl=''),
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='surface',
data_source=model,
var_name='u10m'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='v10m'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='Td2m'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='T2m')
]
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)
initTime = filename[0:8]
# retrieve data from micaps server
gh = MICAPS_IO.get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels)
if (gh is None):
return
gh['data'].values = gh['data'].values * 10
rh = MICAPS_IO.get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
u = MICAPS_IO.get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
v = MICAPS_IO.get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
u10m = MICAPS_IO.get_model_grid(directory=data_dir[4],
filename=filename)
if u10m is None:
return
v10m = MICAPS_IO.get_model_grid(directory=data_dir[5],
filename=filename)
if v10m is None:
return
td2m = MICAPS_IO.get_model_grid(directory=data_dir[6],
filename=filename)
if td2m is None:
return
t2m = MICAPS_IO.get_model_grid(directory=data_dir[7],
filename=filename)
if t2m is None:
return
if (draw_zd == True):
validtime = (datetime.strptime('20' + initTime, '%Y%m%d%H') +
timedelta(hours=fhour)).strftime("%Y%m%d%H")
directory_obs = utl.Cassandra_dir(data_type='surface',
data_source='OBS',
var_name='PLOT_ALL')
try:
zd_sta = MICAPS_IO.get_station_data(filename=validtime +
'0000.000',
directory=directory_obs,
dropna=True,
cache=False)
obs_valid = True
except:
zd_sta = MICAPS_IO.get_station_data(directory=directory_obs,
dropna=True,
cache=False)
obs_valid = False
zd_lon = zd_sta['lon'].values
zd_lat = zd_sta['lat'].values
zd_alt = zd_sta['Alt'].values
zd_u, zd_v = mpcalc.wind_components(
zd_sta['Wind_speed_2m_avg'].values * units('m/s'),
zd_sta['Wind_angle_2m_avg'].values * units.deg)
idx_zd = np.where((zd_lon > map_extent[0])
& (zd_lon < map_extent[1])
& (zd_lat > map_extent[2])
& (zd_lat < map_extent[3]))
zd_sm_lon = zd_lon[idx_zd[0]]
zd_sm_lat = zd_lat[idx_zd[0]]
zd_sm_alt = zd_alt[idx_zd[0]]
zd_sm_u = zd_u[idx_zd[0]]
zd_sm_v = zd_v[idx_zd[0]]
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:
# retrieve data from CMISS server
gh = CIMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='GPH'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_levels=levels,
fcst_ele="GPH",
units='gpm')
if gh is None:
return
rh = CIMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='RHU'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_levels=levels,
fcst_ele="RHU",
units='%')
if rh is None:
return
u = CIMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIU'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_levels=levels,
fcst_ele="WIU",
units='m/s')
if u is None:
return
v = CIMISS_IO.cimiss_model_3D_grid(
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIV'),
init_time_str='20' + filename[0:8],
valid_time=fhour,
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_levels=levels,
fcst_ele="WIV",
units='m/s')
if v is None:
return
if (model == 'ECMWF'):
td2m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='DPT'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="DPT",
units='K')
if td2m is None:
return
t2m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TEF2'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TEF2",
units='K')
if t2m is None:
return
v10m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIV10'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="WIV10",
units='m/s')
if v10m is None:
return
u10m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIU10'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="WIU10",
units='m/s')
if u10m is None:
return
if (model == 'GRAPES_GFS'):
rh2m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='RHF2'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=2,
fcst_ele="RHF2",
units='%')
if rh2m is None:
return
v10m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIV10'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=10,
fcst_ele="WIV10",
units='m/s')
if v10m is None:
return
u10m = CIMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIU10'),
levattrs={
'long_name': 'height_above_ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=10,
fcst_ele="WIU10",
units='m/s')
if u10m is None:
return
except KeyError:
raise ValueError('Can not find all data needed')
if (draw_zd == True):
if (initTime == None):
initTime1 = CIMISS_IO.cimiss_get_obs_latest_time(
data_code="SURF_CHN_MUL_HOR")
initTime = (datetime.strptime('20' + initTime1, '%Y%m%d%H') -
timedelta(days=day_back)).strftime("%Y%m%d%H")[2:]
validtime = (datetime.strptime('20' + initTime, '%Y%m%d%H') +
timedelta(hours=fhour)).strftime("%Y%m%d%H")
data_code = utl.CMISS_data_code(data_source='OBS',
var_name='PLOT_sfc')
zd_sta = CIMISS_IO.cimiss_obs_by_time(
times=validtime + '0000',
data_code=data_code,
sta_levels="011,012,013,014",
elements=
"Station_Id_C,Station_Id_d,lat,lon,Alti,TEM,WIN_D_Avg_2mi,WIN_S_Avg_2mi,RHU"
)
obs_valid = True
if (zd_sta is None):
CIMISS_IO.cimiss_get_obs_latest_time(data_code=data_code,
latestTime=6)
zd_sta = CIMISS_IO.cimiss_obs_by_time(directory=directory_obs,
dropna=True,
cache=False)
obs_valid = False
zd_lon = zd_sta['lon'].values
zd_lat = zd_sta['lat'].values
zd_alt = zd_sta['Alti'].values
zd_u, zd_v = mpcalc.wind_components(
zd_sta['WIN_S_Avg_2mi'].values * units('m/s'),
zd_sta['WIN_D_Avg_2mi'].values * units.deg)
idx_zd = np.where((zd_lon > map_extent[0])
& (zd_lon < map_extent[1])
& (zd_lat > map_extent[2])
& (zd_lat < map_extent[3])
& (zd_sta['WIN_S_Avg_2mi'].values < 1000))
zd_sm_lon = zd_lon[idx_zd[0]]
zd_sm_lat = zd_lat[idx_zd[0]]
zd_sm_alt = zd_alt[idx_zd[0]]
zd_sm_u = zd_u[idx_zd[0]]
zd_sm_v = zd_v[idx_zd[0]]
#maskout area
delt_xy = rh['lon'].values[1] - rh['lon'].values[0]
#+ to solve the problem of labels on all the contours
mask1 = (rh['lon'] > map_extent[0] - delt_xy) & (
rh['lon'] < map_extent[1] + delt_xy) & (
rh['lat'] > map_extent[2] - delt_xy) & (rh['lat'] <
map_extent[3] + delt_xy)
mask2 = (u10m['lon'] > map_extent[0] - delt_xy) & (
u10m['lon'] < map_extent[1] + delt_xy) & (
u10m['lat'] > map_extent[2] - delt_xy) & (u10m['lat'] <
map_extent[3] + delt_xy)
#- to solve the problem of labels on all the contours
rh = rh.where(mask1, drop=True)
u = u.where(mask1, drop=True)
v = v.where(mask1, drop=True)
gh = gh.where(mask1, drop=True)
u10m = u10m.where(mask2, drop=True)
v10m = v10m.where(mask2, drop=True)
#prepare interpolator
Ex1 = np.squeeze(u['data'].values).flatten()
Ey1 = np.squeeze(v['data'].values).flatten()
Ez1 = np.squeeze(rh['data'].values).flatten()
z = (np.squeeze(gh['data'].values)).flatten()
coords = np.zeros((np.size(levels), u['lat'].size, u['lon'].size, 3))
coords[..., 1] = u['lat'].values.reshape((1, u['lat'].size, 1))
coords[..., 2] = u['lon'].values.reshape((1, 1, u['lon'].size))
coords = coords.reshape((Ex1.size, 3))
coords[:, 0] = z
interpolator_U = LinearNDInterpolator(coords, Ex1, rescale=True)
interpolator_V = LinearNDInterpolator(coords, Ey1, rescale=True)
interpolator_RH = LinearNDInterpolator(coords, Ez1, rescale=True)
#process sta_fcs 10m wind
coords2 = np.zeros((np.size(sta_fcs['lon']), 3))
coords2[:, 0] = sta_fcs['altitude']
coords2[:, 1] = sta_fcs['lat']
coords2[:, 2] = sta_fcs['lon']
u_sta = interpolator_U(coords2)
v_sta = interpolator_V(coords2)
RH_sta = interpolator_RH(coords2)
wsp_sta = (u_sta**2 + v_sta**2)**0.5
u10m_2D = u10m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))
v10m_2D = v10m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))
if (model == 'GRAPES_GFS' and data_source == 'CIMISS'):
rh2m_2D = rh2m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))['data'].values
else:
td2m_2D = td2m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))
t2m_2D = t2m.interp(lon=('points', sta_fcs['lon']),
lat=('points', sta_fcs['lat']))
if (data_source == 'MICAPS'):
rh2m_2D = mpcalc.relative_humidity_from_dewpoint(
t2m_2D['data'].values * units('degC'),
td2m_2D['data'].values * units('degC')) * 100
else:
rh2m_2D = mpcalc.relative_humidity_from_dewpoint(
t2m_2D['data'].values * units('kelvin'),
td2m_2D['data'].values * units('kelvin')) * 100
wsp10m_2D = (u10m_2D['data'].values**2 + v10m_2D['data'].values**2)**0.5
winddir10m = mpcalc.wind_direction(u10m_2D['data'].values * units('m/s'),
v10m_2D['data'].values * units('m/s'))
if (np.isnan(wsp_sta).any()):
if (wsp_sta.size == 1):
wsp_sta[np.isnan(wsp_sta)] = np.squeeze(
wsp10m_2D[np.isnan(wsp_sta)])
RH_sta[np.isnan(RH_sta)] = np.squeeze(
np.array(rh2m_2D)[np.isnan(RH_sta)])
else:
wsp_sta[np.isnan(wsp_sta)] = np.squeeze(wsp10m_2D)[np.isnan(
wsp_sta)]
RH_sta[np.isnan(RH_sta)] = np.squeeze(
np.array(rh2m_2D))[np.isnan(RH_sta)]
u_sta, v_sta = mpcalc.wind_components(wsp_sta * units('m/s'), winddir10m)
#process zd_sta 10m wind
zd_fcst_obs = None
if (draw_zd is True):
coords3 = np.zeros((np.size(zd_sm_alt), 3))
coords3[:, 0] = zd_sm_alt
coords3[:, 1] = zd_sm_lat
coords3[:, 2] = zd_sm_lon
u_sm_sta = interpolator_U(coords3)
v_sm_sta = interpolator_V(coords3)
wsp_sm_sta = (u_sm_sta**2 + v_sm_sta**2)**0.5
u10m_sm = u10m.interp(lon=('points', zd_sm_lon),
lat=('points', zd_sm_lat))
v10m_sm = v10m.interp(lon=('points', zd_sm_lon),
lat=('points', zd_sm_lat))
wsp10m_sta = np.squeeze(
(u10m_sm['data'].values**2 + v10m_sm['data'].values**2)**0.5)
winddir10m_sm = mpcalc.wind_direction(
u10m_sm['data'].values * units('m/s'),
v10m_sm['data'].values * units('m/s'))
if (np.isnan(wsp_sm_sta).any()):
wsp_sm_sta[np.isnan(wsp_sm_sta)] = wsp10m_sta[np.isnan(wsp_sm_sta)]
u_sm_sta, v_sm_sta = mpcalc.wind_components(wsp_sm_sta * units('m/s'),
winddir10m_sm)
zd_fcst_obs = {
'lon': zd_sm_lon,
'lat': zd_sm_lat,
'altitude': zd_sm_alt,
'U': np.squeeze(np.array(u_sm_sta)),
'V': np.squeeze(np.array(v_sm_sta)),
'obs_valid': obs_valid,
'U_obs': np.squeeze(np.array(zd_sm_u)),
'V_obs': np.squeeze(np.array(zd_sm_v))
}
#prepare for graphics
sta_fcs_fcst = {
'lon': sta_fcs['lon'],
'lat': sta_fcs['lat'],
'altitude': sta_fcs['altitude'],
'name': sta_fcs['name'],
'RH': np.array(RH_sta),
'U': np.squeeze(np.array(u_sta)),
'V': np.squeeze(np.array(v_sta))
}
fcst_info = gh.coords
local_scale_graphics.draw_wind_rh_according_to_4D_data(
sta_fcs_fcst=sta_fcs_fcst,
zd_fcst_obs=zd_fcst_obs,
fcst_info=fcst_info,
map_extent=map_extent,
draw_zd=draw_zd,
bkgd_type=bkgd_type,
bkgd_level=bkgd_level,
output_dir=None)
def low_level_wind(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
wind_level='100m',
data_source='MICAPS',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
# micaps data directory
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='u' + wind_level),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='v' + wind_level)
]
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
u10m = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
if u10m is None:
return
v10m = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if v10m is None:
return
init_time = v10m.coords['forecast_reference_time'].values
# prepare data
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
if (area != '全国'):
south_China_sea = False
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:
# retrieve data from CMISS server
u10m = 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' +
wind_level[0:-1]),
fcst_level=0,
fcst_ele="WIU" + wind_level[0:-1],
units='m*s-1')
if u10m is None:
return
v10m = 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' +
wind_level[0:-1]),
fcst_level=0,
fcst_ele="WIV" + wind_level[0:-1],
units='m*s-1')
if v10m is None:
return
except KeyError:
raise ValueError('Can not find all data needed')
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
mask1 = (v10m['lon'] > map_extent[0] - delt_x) & (
v10m['lon'] < map_extent[1] + delt_x) & (
v10m['lat'] > map_extent[2] - delt_y) & (v10m['lat'] <
map_extent[3] + delt_y)
u10m = u10m.where(mask1, drop=True)
v10m = v10m.where(mask1, drop=True)
uv = xr.merge([u10m.rename({'data': 'u'}), v10m.rename({'data': 'v'})])
uv.attrs['model'] = model
uv.attrs['level'] = wind_level
elements_graphics.draw_low_level_wind(uv=uv,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def Time_Crossection_rh_uv_theta_e(
initTime=None,
model='ECMWF',
points={
'lon': [116.3833],
'lat': [39.9]
},
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 200],
t_gap=3,
t_range=[0, 48],
output_dir=None):
fhours = np.arange(t_range[0], t_range[1], t_gap)
# 读数据
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
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='RH',
lvl='')
]
except KeyError:
raise ValueError('Can not find all directories needed')
if (initTime == None):
initTime = get_latest_initTime(data_dir[0][0:-1] + "850")
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
TMP_4D = get_model_3D_grids(directory=data_dir[0][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
TMP_2D = TMP_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
u_4D = get_model_3D_grids(directory=data_dir[1][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
u_2D = u_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
v_4D = get_model_3D_grids(directory=data_dir[2][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
v_2D = v_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
rh_4D = get_model_3D_grids(directory=data_dir[3][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
rh_2D = rh_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
rh_2D.attrs['model'] = model
rh_2D.attrs['points'] = points
Td_2D = mpcalc.dewpoint_rh(TMP_2D['data'].values * units.celsius,
rh_2D['data'].values * units.percent)
rh, pressure = xr.broadcast(rh_2D['data'], rh_2D['level'])
Theta_e = mpcalc.equivalent_potential_temperature(
pressure, TMP_2D['data'].values * units.celsius, Td_2D)
theta_e_2D = xr.DataArray(np.array(Theta_e),
coords=rh_2D['data'].coords,
dims=rh_2D['data'].dims,
attrs={'units': Theta_e.units})
crossection_graphics.draw_Time_Crossection_rh_uv_theta_e(
rh_2D=rh_2D,
u_2D=u_2D,
v_2D=v_2D,
theta_e_2D=theta_e_2D,
t_range=t_range,
output_dir=output_dir)
def gh_rain(initTime=None,
fhour=24,
day_back=0,
model='ECMWF',
gh_lev=500,
atime=6,
data_source='MICAPS',
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
Global=False,
**kwargs):
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl=str(gh_lev)),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % atime),
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)
if (atime > 3):
filename_gh = utl.model_filename(initTime,
int(fhour - atime / 2))
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
if (atime > 3):
filename_gh = utl.filename_day_back_model(day_back=day_back,
fhour=int(fhour -
atime / 2))
# retrieve data from micaps server
gh = MICAPS_IO.get_model_grid(data_dir[0], filename=filename_gh)
if gh is None:
return
rain = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if rain is None:
return
psfc = MICAPS_IO.get_model_grid(data_dir[2], filename=filename)
if (data_source == 'CIMISS'):
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour, UTC=True)
if (atime > 3):
filename_gh = utl.model_filename(initTime,
fhour=int(fhour - atime / 2),
UTC=True)
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)
if (atime > 3):
filename_gh = utl.filename_day_back_model(day_back=day_back,
fhour=int(fhour -
atime / 2),
UTC=True)
try:
# retrieve data from CIMISS server
gh = CMISS_IO.cimiss_model_by_time(
'20' + filename_gh[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=gh_lev,
fcst_ele="GPH",
units='gpm')
if gh is None:
return
gh['data'].values = gh['data'].values / 10.
TPE1 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TPE'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE1 is None:
return
TPE2 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour - atime,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TPE'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE2 is None:
return
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')
rain = TPE1.copy(deep=True)
rain['data'].values = TPE1['data'].values - TPE2['data'].values
# set map extent
if (area != None):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
gh = utl.cut_xrdata(map_extent, gh, delt_x=delt_x, delt_y=delt_y)
rain = utl.cut_xrdata(map_extent, rain, delt_x=delt_x, delt_y=delt_y)
gh = utl.mask_terrian(gh_lev, psfc, gh)
gh.attrs['model'] = model
gh.attrs['lev'] = gh_lev
rain.attrs['atime'] = atime
# draw
QPF_graphics.draw_gh_rain(rain=rain,
gh=gh,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def gh_uv_wvfl(initTime=None, fhour=6, day_back=0,model='GRAPES_GFS',
gh_lev=500,uv_lev=850,wvfl_lev=850,
map_ratio=19/9,zoom_ratio=20,cntr_pnt=[102,34],
south_China_sea=True,area = '全国',city=False,output_dir=None,data_source='MICAPS',
Global=False):
if(area != '全国'):
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=gh_lev),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=uv_lev),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=uv_lev),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='WVFL',lvl=wvfl_lev)
]
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
wvfl = MICAPS_IO.get_model_grid(data_dir[3], filename=filename)
if wvfl is None:
return
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:
# retrieve data from CMISS server
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=gh_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=uv_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=uv_lev, fcst_ele="WIV", units='m/s')
if v is None:
return
wvfl=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='MOFU'),
levattrs={'long_name':'pressure_level', 'units':'hPa', '_CoordinateAxisType':'-'},
fcst_level=wvfl_lev, fcst_ele="MOFU", units='10^-1*g/cm*hPa*s')
if wvfl is None:
return
except KeyError:
raise ValueError('Can not find all data needed')
# prepare data
if(area != None):
cntr_pnt,zoom_ratio=utl.get_map_area(area_name=area)
map_extent=[0,0,0,0]
map_extent[0]=cntr_pnt[0]-zoom_ratio*1*map_ratio
map_extent[1]=cntr_pnt[0]+zoom_ratio*1*map_ratio
map_extent[2]=cntr_pnt[1]-zoom_ratio*1
map_extent[3]=cntr_pnt[1]+zoom_ratio*1
delt_x=(map_extent[1]-map_extent[0])*0.2
delt_y=(map_extent[3]-map_extent[2])*0.1
#+ to solve the problem of labels on all the contours
mask1 = (gh['lon'] > map_extent[0]-delt_x) & (gh['lon'] < map_extent[1]+delt_x) & (gh['lat'] > map_extent[2]-delt_y) & (gh['lat'] < map_extent[3]+delt_y)
mask2 = (u['lon'] > map_extent[0]-delt_x) & (u['lon'] < map_extent[1]+delt_x) & (u['lat'] > map_extent[2]-delt_y) & (u['lat'] < map_extent[3]+delt_y)
mask3 = (wvfl['lon'] > map_extent[0]-delt_x) & (u['lon'] < map_extent[1]+delt_x) & (u['lat'] > map_extent[2]-delt_y) & (u['lat'] < map_extent[3]+delt_y)
#- to solve the problem of labels on all the contours
gh=gh.where(mask1,drop=True)
gh.attrs['model']=model
u=u.where(mask2,drop=True)
v=v.where(mask2,drop=True)
uv=xr.merge([u.rename({'data': 'u'}),v.rename({'data': 'v'})])
wvfl=v.where(wvfl,drop=True)
moisture_graphics.draw_gh_uv_wvfl(
wvfl=wvfl, gh=gh, uv=uv,
map_extent=map_extent, regrid_shape=20,
city=city,south_China_sea=south_China_sea,
output_dir=output_dir,Global=Global)
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)
