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_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 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_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 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 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 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_uv_mslp(initTime=None, fhour=0, day_back=0,model='ECMWF',
gh_lev=500,uv_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='surface',data_source=model,var_name='PRMSL')]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if(initTime == None):
initTime = MICAPS_IO.get_latest_initTime(data_dir[-1])
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
mslp = MICAPS_IO.get_model_grid(data_dir[3], filename=filename)
if mslp 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
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.
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 = (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)
#- 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)
mslp=mslp.where(mask3,drop=True)
uv=xr.merge([u.rename({'data': 'u'}),v.rename({'data': 'v'})])
synoptic_graphics.draw_gh_uv_mslp(
mslp=mslp, 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 compare_gh_uv(anaTime=None,
anamodel='GRAPES_GFS',
fhour=24,
model='ECMWF',
data_source='MICAPS',
gh_lev=500,
uv_lev=850,
area=None,
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
**products_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
if (anaTime == None):
anaTime = MICAPS_IO.get_latest_initTime(data_dir[-1])
initTime = (datetime.strptime('20' + anaTime, '%Y%m%d%H') -
timedelta(hours=fhour)).strftime("%Y%m%d%H")[2:10]
if (anaTime != None):
filename_ana = utl.model_filename(anaTime, 0)
initTime = (datetime.strptime('20' + anaTime, '%Y%m%d%H') -
timedelta(hours=fhour)).strftime("%Y%m%d%H")[2:10]
filename_fcst = utl.model_filename(initTime, fhour)
# retrieve data from micaps server
gh_ana = MICAPS_IO.get_model_grid(data_dir[0], filename=filename_ana)
u_ana = MICAPS_IO.get_model_grid(data_dir[1], filename=filename_ana)
v_ana = MICAPS_IO.get_model_grid(data_dir[2], filename=filename_ana)
psfc_ana = MICAPS_IO.get_model_grid(data_dir[3], filename=filename_ana)
gh_fcst = MICAPS_IO.get_model_grid(data_dir[0], filename=filename_fcst)
u_fcst = MICAPS_IO.get_model_grid(data_dir[1], filename=filename_fcst)
v_fcst = MICAPS_IO.get_model_grid(data_dir[2], filename=filename_fcst)
psfc_fcst = MICAPS_IO.get_model_grid(data_dir[3],
filename=filename_fcst)
if (data_source == 'CIMISS'):
# get filename
if (anaTime != None):
anaTime = utl.model_filename(anaTime, fhour, UTC=True)[0:8]
else:
anaTime = utl.filename_day_back_model(fhour=fhour, UTC=True)[0:8]
initTime = (datetime.strptime('20' + anaTime, '%Y%m%d%H') -
timedelta(hours=fhour)).strftime("%Y%m%d%H")[2:10]
try:
# retrieve data from CIMISS server
gh_ana = CMISS_IO.cimiss_model_by_time(
'20' + anaTime,
valid_time=0,
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')
gh_ana['data'].values = gh_ana['data'].values / 10.
gh_fcst = CMISS_IO.cimiss_model_by_time(
'20' + initTime,
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')
gh_fcst['data'].values = gh_fcst['data'].values / 10.
u_ana = CMISS_IO.cimiss_model_by_time(
'20' + anaTime,
valid_time=0,
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')
u_fcst = CMISS_IO.cimiss_model_by_time(
'20' + initTime,
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')
v_ana = CMISS_IO.cimiss_model_by_time(
'20' + anaTime,
valid_time=0,
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')
v_fcst = CMISS_IO.cimiss_model_by_time(
'20' + initTime,
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')
psfc_ana = CMISS_IO.cimiss_model_by_time(
'20' + anaTime,
valid_time=0,
data_code=utl.CMISS_data_code(data_source=model,
var_name='PRS'),
levattrs={
'long_name': 'sea_surface_pressure',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="PRS",
units='Pa')
psfc_fcst = CMISS_IO.cimiss_model_by_time(
'20' + initTime,
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='PRS'),
levattrs={
'long_name': 'sea_surface_pressure',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="PRS",
units='Pa')
psfc_ana['data'] = psfc_ana['data'] / 100.
psfc_fcst['data'] = psfc_fcst['data'] / 100.
except KeyError:
raise ValueError('Can not find all data needed')
# prepare data
if (all([
gh_ana, u_ana, v_ana, psfc_ana, gh_fcst, u_fcst, v_fcst, psfc_fcst
]) is False):
print('some data is not avaliable')
return
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
gh_ana = utl.cut_xrdata(map_extent, gh_ana)
u_ana = utl.cut_xrdata(map_extent, u_ana)
v_ana = utl.cut_xrdata(map_extent, v_ana)
psfc_ana = utl.cut_xrdata(map_extent, psfc_ana)
gh_fcst = utl.cut_xrdata(map_extent, gh_fcst)
u_fcst = utl.cut_xrdata(map_extent, u_fcst)
v_fcst = utl.cut_xrdata(map_extent, v_fcst)
psfc_fcst = utl.cut_xrdata(map_extent, psfc_fcst)
u_ana = utl.mask_terrian(uv_lev, psfc_ana, u_ana)
v_ana = utl.mask_terrian(uv_lev, psfc_ana, v_ana)
gh_ana = utl.mask_terrian(gh_lev, psfc_ana, gh_ana)
u_fcst = utl.mask_terrian(uv_lev, psfc_fcst, u_fcst)
v_fcst = utl.mask_terrian(uv_lev, psfc_fcst, v_fcst)
gh_fcst = utl.mask_terrian(gh_lev, psfc_fcst, gh_fcst)
uv_ana = xr.merge(
[u_ana.rename({'data': 'u'}),
v_ana.rename({'data': 'v'})])
uv_fcst = xr.merge(
[u_fcst.rename({'data': 'u'}),
v_fcst.rename({'data': 'v'})])
gh_ana.attrs = {'model_name': model}
u_ana.attrs = {'model_name': model}
v_ana.attrs = {'model_name': model}
gh_fcst.attrs = {'model_name': model}
u_fcst.attrs = {'model_name': model}
v_fcst.attrs = {'model_name': model}
vs_ana.draw_compare_gh_uv(gh_ana=gh_ana,
uv_ana=uv_ana,
gh_fcst=gh_fcst,
uv_fcst=uv_fcst,
map_extent=map_extent,
**products_kwargs)
def point_fcst_uv_tmp_according_to_3D_field_vs_sounding(
output_dir=None,
obs_ID='55664',
initTime=None,
fhour=6,
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, 400, 300, 250,
200, 150
]
},
**kwargs):
model = 'GRAPES_GFS'
try:
dir_rqd = [
utl.Cassandra_dir(data_type='high',
data_source='OBS',
var_name='TLOGP',
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='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='')
]
except KeyError:
raise ValueError('Can not find all required directories needed')
if (initTime == None):
initTime = get_latest_initTime(dir_rqd[1][0:-1] + '/850')
filename_obs = (datetime.strptime('20' + initTime, '%Y%m%d%H') +
timedelta(hours=fhour)).strftime('%Y%m%d%H%M%S') + '.000'
obs_pfl_all = MICAPS_IO.get_tlogp(dir_rqd[0][0:-1],
filename=filename_obs,
cache=False)
if (obs_pfl_all is None):
return
obs_pfl_raw = obs_pfl_all[obs_pfl_all.ID == obs_ID]
obs_pfl = obs_pfl_raw.replace(9999.0, np.nan).dropna(how='any')
obs_pfl = obs_pfl[obs_pfl.p >= 200.]
directory = dir_rqd[1][0:-1]
filename = initTime + '.' + str(fhour).zfill(3)
HGT_4D = get_model_3D_grid(directory=directory,
filename=filename,
levels=extra_info['levels_for_interp'],
allExists=False)
directory = dir_rqd[2][0:-1]
U_4D = get_model_3D_grid(directory=directory,
filename=filename,
levels=extra_info['levels_for_interp'],
allExists=False)
directory = dir_rqd[3][0:-1]
V_4D = get_model_3D_grid(directory=directory,
filename=filename,
levels=extra_info['levels_for_interp'],
allExists=False)
directory = dir_rqd[4][0:-1]
TMP_4D = get_model_3D_grid(directory=directory,
filename=filename,
levels=extra_info['levels_for_interp'],
allExists=False)
points = {
'lon': obs_pfl.lon.to_numpy(),
'lat': obs_pfl.lat.to_numpy(),
'altitude': obs_pfl.h.to_numpy() * 10
}
directory = dir_rqd[4][0:-1]
delt_xy = HGT_4D['lon'].values[1] - HGT_4D['lon'].values[0]
mask = (HGT_4D['lon'] < (points['lon'][0] + 2 * delt_xy)) & (
HGT_4D['lon'] > (points['lon'][0] - 2 * delt_xy)
) & (HGT_4D['lat'] <
(points['lat'][0] + 2 * delt_xy)) & (HGT_4D['lat'] >
(points['lat'][0] - 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 = TMP_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 = HGT_4D_sm['forecast_period'].values
coords = np.zeros((HGT_4D_sm.level.size, len(lat_md), len(lon_md), 3))
coords[..., 1] = lat_md.reshape((1, len(lat_md), 1))
coords[..., 2] = lon_md.reshape((1, 1, len(lon_md)))
coords = coords.reshape((alt_md.size, 3))
coords[:, 0] = 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((np.size(points['lon']), 3))
coords2[:, 0] = points['altitude']
coords2[:, 1] = points['lat']
coords2[:, 2] = points['lon']
U_interped = np.squeeze(interpolator_U(coords2))
V_interped = np.squeeze(interpolator_V(coords2))
windsp_interped = (U_interped**2 + V_interped**2)**0.5
winddir10m_interped = mpcalc.wind_direction(U_interped * units('m/s'),
V_interped * units('m/s'))
TMP_interped = np.squeeze(interpolator_TMP(coords2))
fcst_pfl = obs_pfl.copy()
fcst_pfl.wind_angle = np.array(winddir10m_interped)
fcst_pfl.wind_speed = np.array(windsp_interped)
fcst_pfl.t = TMP_interped
fcst_info = xr.DataArray(np.array(U_4D_sm.values),
coords=U_4D_sm.coords,
dims=U_4D_sm.dims,
attrs={
'points': points,
'model': model
})
sta_graphics.draw_sta_skewT_model_VS_obs(fcst_pfl=fcst_pfl,
obs_pfl=obs_pfl,
fcst_info=fcst_info,
output_dir=output_dir)
def point_fcst_tmp_according_to_3D_field_box_line(
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, 925, 850, 700, 500,300,200]}
):
try:
dir_rqd=[utl.Cassandra_dir(data_type='high',data_source='ECMWF_ENSEMBLE',var_name='HGT_RAW',lvl=''),
utl.Cassandra_dir(data_type='high',data_source='ECMWF_ENSEMBLE',var_name='TMP_RAW',lvl='')]
#utl.Cassandra_dir(data_type='surface',data_source=model,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[0][0:-1]+'/850')
#initTime=utl.filename_day_back_model(day_back=day_back,fhour=0)[0:8]
directory=dir_rqd[0][0:-1]
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]
HGT_4D=get_model_3D_grids(directory=directory,filenames=filenames,levels=extra_info['levels_for_interp'], allExists=False)
directory=dir_rqd[0][0:-1]
directory=dir_rqd[1][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[1][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)
TMP_4D_sm=TMP_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)
number_md=np.squeeze(HGT_4D_sm['number'].values)
'''
coords = np.zeros((len(time_md),len(number_md),len(extra_info['levels_for_interp']),len(lat_md),len(lon_md),5))
coords[...,0]=time_md.reshape((len(time_md),1,1,1,1))
coords[...,1]=number_md.reshape((1,len(number_md),1,1,1))
coords[...,3] = lat_md.reshape((1,1,1,len(lat_md),1))
coords[...,4] = lon_md.reshape((1,1,1,1,len(lon_md)))
coords = coords.reshape((alt_md.size,5))
coords[:,2]=alt_md
interpolator_TMP = LinearNDInterpolator(coords,TMP_4D_sm.values.reshape((TMP_4D_sm.values.size)),rescale=True)
coords2 = np.zeros((len(time_md),len(number_md),1,1,1,5))
coords2[...,0]=time_md.reshape((len(time_md),1,1,1,1))
coords2[...,1]=number_md.reshape(1,(len(number_md),1,1,1))
coords2[...,2]=points['altitude'][0]
coords2[...,3] = points['lat'][0]
coords2[...,4] = points['lon'][0]
coords2 = coords2.reshape((time_md.size,5))
TMP_interped=np.squeeze(interpolator_TMP(coords2))
'''
TMP_interped=np.zeros((len(time_md),len(number_md)))
for it in range(0,len(time_md)):
for inum in range(0,len(number_md)):
alt_md=np.squeeze(HGT_4D_sm.values[it,inum,:,:,:]*10).flatten()
coords = np.zeros((len(extra_info['levels_for_interp']),len(lat_md),len(lon_md),3))
coords[...,1] = lat_md.reshape((1,len(lat_md),1))
coords[...,2] = lon_md.reshape((1,1,len(lon_md)))
coords = coords.reshape((alt_md.size,3))
coords[:,0]=alt_md
interpolator_TMP = LinearNDInterpolator(coords,TMP_4D_sm.values[it,inum,:,:,:].reshape((TMP_4D_sm.values[it,inum,:,:,:].size)),rescale=True)
coords2 = np.zeros((1,1,1,3))
coords2[...,0]=points['altitude'][0]
coords2[...,1] = points['lat'][0]
coords2[...,2] = points['lon'][0]
coords2 = coords2.reshape((1,3))
TMP_interped[it,inum]=np.squeeze(interpolator_TMP(coords2))
TMP_interped_xr=coords_info_2D.copy()
TMP_interped_xr['data'].values[:,:,0,0]=TMP_interped
TMP_interped_xr.attrs['model']='ECMWF_ENSEMBLE'
Ensemble_graphics.box_line_temp(TMP=TMP_interped_xr,
points=points,
extra_info=extra_info,output_dir=output_dir)
def ivt(initTime=None,
fhour=0,
frange=None,
model='ECMWF',
region='中国陆地',
show='list',
width=500,
getModels=False,
getInitTime=False,
noshow=False):
"""
分析水汽传输垂直积分场.
Args:
initTime (string, optional): model initial time YYYYmmddHH, like 2020061320.
Defaults to None, = the model latest run time.
fhour (int, optional): model forecast hour. Defaults to 0.
frange (list, optional): model forecast hour range, [start, end, step] or [start, end] which step=6.
if frange is set, fhour is ignored.
model (str, optional): model name. Defaults to 'ECMWF'.
You can use "getModels=True" return all model names.
region (str or list, optional): Predifined region name, like '中国', '中国陆地', '华北', '东北', '华东', '华中', '华南',
'西南', '西北', '新疆', '青藏'. Defaults to '中国陆地'. Or [lonmin, lonmax, latmin, latmax]
show (str, optional): 'list', show all plots in one cell.
'tab', show one plot in each tab page.
'animation', show gif animation.
width (int, optional): Width of the displayed image. Defaults to 500.
noshow (bool, optional): just return the plots.
"""
# get function arguments
kwargs = locals().copy()
# set and check model directory
model_dirs = {
'ECMWF': [
"ECMWF_HR/SPFH/", "ECMWF_HR/UGRD/", "ECMWF_HR/VGRD/",
"ECMWF_HR/PRES/SURFACE/", "ECMWF_HR/PRMSL/"
],
'GRAPES': [
'GRAPES_GFS/SPFH/', 'GRAPES_GFS/UGRD/', 'GRAPES_GFS/VGRD/',
'GRAPES_GFS/PRES/SURFACE/', 'GRAPES_GFS/PRMSL/'
]
}
if getModels: return list(model_dirs.keys())
model_dir = check_model(model, model_dirs)
if model_dir is None: return None
# check initTime
if initTime is None:
initTime = get_latest_initTime(model_dir[0] + '1000')
initTime = check_initTime(initTime)
if getInitTime: return initTime
# check frange
if frange is not None:
return util.draw_multiple_plots(ivt, kwargs)
# prepare data
filename = initTime.strftime("%y%m%d%H") + '.' + str(int(fhour)).zfill(3)
levels = [1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300]
qData = get_model_3D_grid(model_dir[0], filename, levels=levels)
if qData is None: return None
uData = get_model_3D_grid(model_dir[1], filename, levels=levels)
if uData is None: return None
vData = get_model_3D_grid(model_dir[2], filename, levels=levels)
if vData is None: return None
sPres = get_model_grid(model_dir[3], filename)
if sPres is None: return None
mslp = get_model_grid(model_dir[4], filename)
if mslp is None: return None
# get the coordinates
lon = qData.lon.values
lat = qData.lat.values
lev = qData.level.values
time = qData.forecast_reference_time.values
fhour = qData.forecast_period.values[0]
# comform surface and high variables
sPres = hinterp(sPres.data.values.squeeze(), sPres.lon.values,
sPres.lat.values, lon, lat)
mslp = hinterp(mslp.data.values.squeeze(), mslp.lon.values,
mslp.lat.values, lon, lat)
mslp = grid_smooth(mslp, radius=4, method='CRES')
qData = qData.data.values.squeeze()
uData = uData.data.values.squeeze()
vData = vData.data.values.squeeze()
# compute IVT
iquData, iqvData = cal_ivt(qData,
uData,
vData,
lon,
lat,
lev,
surf_pres=sPres)
# draw the figure
plot = draw_ivt(iquData,
iqvData,
lon,
lat,
mslp=mslp,
map_region=get_map_region(region),
title_kwargs={
'name': model.upper(),
'time': time,
'fhour': fhour,
'tzone': 'BJT'
})
if noshow:
return plot, str(int(fhour)).zfill(3)
else:
return plot
def tmp_evo(initTime=None,
tmp_lev=850,
t_gap=6,
t_range=[6, 36],
model='ECMWF',
data_source='MICAPS',
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
**kwargs):
fhours = np.arange(t_range[0], t_range[1], t_gap)
#prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl=tmp_lev),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PSFC')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initTime == None):
initTime = MICAPS_IO.get_latest_initTime(data_dir[0])
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
# retrieve data from micaps server
tmp = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
psfc = MICAPS_IO.get_model_grids(data_dir[1], filenames=filenames)
if (data_source == 'CIMISS'):
# get filename
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:
tmp = CMISS_IO.cimiss_model_by_times(
'20' + filename[0:8],
valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TEM'),
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_level=tmp_lev,
fcst_ele="TEM",
units='K')
tmp['data'].values = tmp['data'].values - 273.15
psfc = CMISS_IO.cimiss_model_by_times(
'20' + filename[0:8],
valid_times=fhours,
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')
# 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, delt_x, delt_y = utl.get_map_extent(cntr_pnt, zoom_ratio,
map_ratio)
tmp = utl.cut_xrdata(map_extent, tmp, delt_x=delt_x, delt_y=delt_y)
tmp = tmp.rolling({'lon': 3, 'lat': 3}).mean()
psfc = utl.cut_xrdata(map_extent, psfc, delt_x=delt_x, delt_y=delt_y)
tmp = utl.mask_terrian(tmp_lev, psfc, tmp)
tmp.attrs['model'] = model
coldwave_graphics.draw_tmp_evo(tmp=tmp,
map_extent=map_extent,
south_China_sea=south_China_sea,
**kwargs)
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 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 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 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 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 gh_uv_mslp(initTime=None,
fhour=0,
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='PRMSL'),
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):
initTime = MICAPS_IO.get_latest_initTime(data_dir[-1])
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)
u = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
v = MICAPS_IO.get_model_grid(data_dir[2], filename=filename)
mslp = MICAPS_IO.get_model_grid(data_dir[3], filename=filename)
psfc = MICAPS_IO.get_model_grid(data_dir[4], filename=filename)
if (data_source == 'CIMISS'):
# get filename
if (initTime != None):
initTime = utl.model_filename(initTime, fhour, UTC=True)[0:8]
else:
initTime = utl.filename_day_back_model(fhour=fhour, UTC=True)[0:8]
try:
# retrieve data from CIMISS server
gh = CMISS_IO.cimiss_model_by_time(
'20' + initTime,
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')
gh['data'].values = gh['data'].values / 10.
u = CMISS_IO.cimiss_model_by_time(
'20' + initTime,
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')
v = CMISS_IO.cimiss_model_by_time(
'20' + initTime,
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 (model == 'ECMWF'):
mslp = CMISS_IO.cimiss_model_by_time(
'20' + initTime,
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' + initTime,
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')
mslp['data'] = mslp['data'] / 100.
psfc = CMISS_IO.cimiss_model_by_time('20' + initTime,
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 = [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)
mslp = utl.cut_xrdata(map_extent, mslp, delt_x=delt_x, delt_y=delt_y)
psfc = utl.cut_xrdata(map_extent, psfc, delt_x=delt_x, delt_y=delt_y)
u = utl.mask_terrian(uv_lev, psfc, u)
v = utl.mask_terrian(uv_lev, psfc, v)
gh = utl.mask_terrian(gh_lev, psfc, gh)
gh.attrs['model'] = model
uv = xr.merge([u.rename({'data': 'u'}), v.rename({'data': 'v'})])
synoptic_graphics.draw_gh_uv_mslp(mslp=mslp,
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 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 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],data_source='MICAPS',
t_gap=3,t_range=[0,48],lw_ratio=[16,9],output_dir=None,**kwargs):
fhours = np.arange(t_range[0], t_range[1], t_gap)
# 读数据
if(data_source == 'MICAPS'):
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)
u_4D=get_model_3D_grids(directory=data_dir[1][0:-1],filenames=filenames,levels=levels, allExists=False)
v_4D=get_model_3D_grids(directory=data_dir[2][0:-1],filenames=filenames,levels=levels, allExists=False)
rh_4D=get_model_3D_grids(directory=data_dir[3][0:-1],filenames=filenames,levels=levels, allExists=False)
Psfc_3D=get_model_grids(directory=data_dir[4][0:-1],filenames=filenames,allExists=False)
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:
rh_4D=CMISS_IO.cimiss_model_3D_grids(init_time_str='20'+filename[0:8],valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'),
fcst_levels=levels, fcst_ele="RHU", units='%',pbar=True)
u_4D=CMISS_IO.cimiss_model_3D_grids(init_time_str='20'+filename[0:8],valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'),
fcst_levels=levels, fcst_ele="WIU", units='m/s',pbar=True)
v_4D=CMISS_IO.cimiss_model_3D_grids(init_time_str='20'+filename[0:8],valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s',pbar=True)
TMP_4D=CMISS_IO.cimiss_model_3D_grids(init_time_str='20'+filename[0:8],valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
fcst_levels=levels, fcst_ele="TEM", units='K',pbar=True)
TMP_4D['data'].values=TMP_4D['data'].values-273.15
Psfc_3D=CMISS_IO.cimiss_model_grids(init_time_str='20'+filename[0:8], valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'),
fcst_level=0, fcst_ele="PRS", units='Pa',pbar=True)
except KeyError:
raise ValueError('Can not find all data needed')
TMP_2D=TMP_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
u_2D=u_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
v_2D=v_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
rh_2D=rh_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
rh_2D.attrs['model']=model
rh_2D.attrs['points']=points
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,lw_ratio=lw_ratio,output_dir=output_dir)
