def TMP850_extreme_uv(initTime=None, fhour=6, day_back=0,model='ECMWF',
uv_lev=850,tmp_lev=850,
map_ratio=13/9,zoom_ratio=20,cntr_pnt=[102,34],
south_China_sea=True,area =None,city=False,output_dir=None,data_source='MICAPS',
Global=False,**kwargs):
#prepare data
if(data_source =='MICAPS'):
try:
data_dir = [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='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):
filename = utl.model_filename(initTime, fhour)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour)
# retrieve data from micaps server
u = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
if u is None:
return
v = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if v is None:
return
tmp = MICAPS_IO.get_model_grid(data_dir[2], filename=filename)
if tmp 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:
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
tmp=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
levattrs={'long_name':'pressure_level', 'units':'hPa', '_CoordinateAxisType':'-'},
fcst_level=tmp_lev, fcst_ele="TEM", units='K')
if tmp is None:
return
tmp['data'].values=tmp['data'].values-273.15
psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'),
fcst_level=0, fcst_ele="PRS", units='Pa')
psfc['data']=psfc['data']/100.
except KeyError:
raise ValueError('Can not find all data needed')
# set map extent
tmp_extr=utl.get_var_extr(tmp+273.15,Var_name='t850')
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['data'].values=gaussian_filter(tmp['data'].values,5)
u=utl.mask_terrian(uv_lev,psfc,u)
v=utl.mask_terrian(uv_lev,psfc,v)
tmp=utl.mask_terrian(tmp_lev,psfc,tmp)
tmp_extr=utl.mask_terrian(tmp_lev,psfc,tmp_extr)
#to solve the problem of labels on all the contours
tmp=utl.cut_xrdata(map_extent,tmp,delt_x=delt_x,delt_y=delt_y)
u=utl.cut_xrdata(map_extent,u,delt_x=delt_x,delt_y=delt_y)
v=utl.cut_xrdata(map_extent,v,delt_x=delt_x,delt_y=delt_y)
tmp_extr=utl.cut_xrdata(map_extent,tmp_extr,delt_x=delt_x,delt_y=delt_y)
tmp.attrs['model']=model
uv=xr.merge([u.rename({'data': 'u'}),v.rename({'data': 'v'})])
#draw
thermal_graphics.draw_gh_uv_tmp_extr(
tmp=tmp, tmp_extr=tmp_extr, uv=uv,
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 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 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_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 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 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 isentropic_uv(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
data_source='MICAPS',
isentlev=310,
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
levels=[
1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300,
250, 200, 100
],
Global=False,
south_China_sea=True,
area='全国',
city=False,
output_dir=None):
# 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 CMISS server
rh = CMISS_IO.cimiss_model_3D_grid(
init_time_str='20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='RHU'),
fcst_levels=levels,
fcst_ele="RHU",
units='%')
if rh is None:
return
u = CMISS_IO.cimiss_model_3D_grid(
init_time_str='20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIU'),
fcst_levels=levels,
fcst_ele="WIU",
units='m/s')
if u is None:
return
v = CMISS_IO.cimiss_model_3D_grid(
init_time_str='20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='WIV'),
fcst_levels=levels,
fcst_ele="WIV",
units='m/s')
if v is None:
return
t = CMISS_IO.cimiss_model_3D_grid(
init_time_str='20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TEM'),
fcst_levels=levels,
fcst_ele="TEM",
units='K')
if t is None:
return
t['data'].values = t['data'].values - 273.15
except KeyError:
raise ValueError('Can not find all data needed')
lats = np.squeeze(rh['lat'].values)
lons = np.squeeze(rh['lon'].values)
pres = np.array(levels) * 100 * units('Pa')
tmp = t['data'].values.squeeze() * units('degC')
uwnd = u['data'].values.squeeze() * units.meter / units.second
vwnd = v['data'].values.squeeze() * units.meter / units.second
relh = rh['data'].values.squeeze() * units.meter / units.percent
isentlev = isentlev * units.kelvin
isent_anal = mpcalc.isentropic_interpolation(isentlev,
pres,
tmp,
relh,
uwnd,
vwnd,
axis=0)
isentprs, isentrh, isentu, isentv = isent_anal
# 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
idx_x1 = np.where((lons > map_extent[0] - delt_x)
& (lons < map_extent[1] + delt_x))
idx_y1 = np.where((lats > map_extent[2] - delt_y)
& (lats < map_extent[3] + delt_y))
mask1 = ((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) &
(t['level'] == t['level'].values[0]))
isentrh1 = t.where(mask1, drop=True)
isentrh1['data'].values = [[
np.array(isentrh)[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)]
]]
isentrh1.attrs['model'] = model
isentrh1['level'].values = [np.array(isentlev)]
isentu1 = isentrh1.copy()
isentu1['data'].values = [[
np.array(isentu)[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)]
]]
isentv1 = isentrh1.copy()
isentv1['data'].values = [[
np.array(isentv)[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)]
]]
isentuv1 = xr.merge([
isentu1.rename({'data': 'isentu'}),
isentv1.rename({'data': 'isentv'})
])
isentprs1 = isentrh1.copy()
isentprs1['data'].values = [[
np.array(isentprs)[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)]
]]
isentropic_graphics.draw_isentropic_uv(isentrh=isentrh1,
isentuv=isentuv1,
isentprs=isentprs1,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def mslp_rain_snow(initial_time=None,
fhour=24,
day_back=0,
model='ECMWF',
atime=6,
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='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 (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
if (atime > 3):
filename_mslp = utl.model_filename(initial_time, fhour / 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=fhour / 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)
snow = get_model_grid(data_dir[2], filename=filename)
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
idx_x1 = np.where((mslp.coords['lon'].values > map_extent[0] - delt_x)
& (mslp.coords['lon'].values < map_extent[1] + delt_x))
idx_y1 = np.where((mslp.coords['lat'].values > map_extent[2] - delt_y)
& (mslp.coords['lat'].values < map_extent[3] + delt_y))
idx_x2 = np.where((rain.coords['lon'].values > map_extent[0] - delt_x)
& (rain.coords['lon'].values < map_extent[1] + delt_x))
idx_y2 = np.where((rain.coords['lat'].values > map_extent[2] - delt_y)
& (rain.coords['lat'].values < map_extent[3] + delt_y))
#- to solve the problem of labels on all the contours
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)
mslp = {
'lon':
mslp.coords['lon'].values[idx_x1],
'lat':
mslp.coords['lat'].values[idx_y1],
'data':
mslp['data'].values[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'model':
model,
'fhour':
fhour,
'init_time':
init_time
}
rain = {
'lon':
rn.coords['lon'].values[idx_x2],
'lat':
rn.coords['lat'].values[idx_y2],
'data':
rn.values[0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)]
}
snow = {
'lon':
snw.coords['lon'].values[idx_x2],
'lat':
snw.coords['lat'].values[idx_y2],
'data':
snw.values[0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)]
}
sleet = {
'lon':
sleet.coords['lon'].values[idx_x2],
'lat':
sleet.coords['lat'].values[idx_y2],
'data':
sleet.values[0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)]
}
QPF_graphics.draw_mslp_rain_snow(rain=rain,
snow=snow,
sleet=sleet,
mslp=mslp,
atime=atime,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def gh_uv_spfh(initTime=None, fhour=6, day_back=0,model='ECMWF',
gh_lev=500,uv_lev=850,spfh_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
if(data_source=='MICAPS'):
# 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=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='SPFH',lvl=spfh_lev),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if(initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour)
# retrieve data from micaps server
gh = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
if gh is None:
return
u = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if u is None:
return
v = MICAPS_IO.get_model_grid(data_dir[2], filename=filename)
if v is None:
return
spfh = MICAPS_IO.get_model_grid(data_dir[3], filename=filename)
if spfh 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'),
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'),
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'),
fcst_level=uv_lev, fcst_ele="WIV", units='m/s')
if v is None:
return
spfh=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='SHU'),
fcst_level=spfh_lev, fcst_ele="SHU", units='kg.kg-1')
if spfh is None:
return
spfh['data'].values=spfh['data'].values*1000
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=[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 = (spfh['lon'] > map_extent[0]-delt_x) & (spfh['lon'] < map_extent[1]+delt_x) & (spfh['lat'] > map_extent[2]-delt_y) & (spfh['lat'] < map_extent[3]+delt_y)
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)
spfh=utl.cut_xrdata(map_extent, spfh, 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)
spfh=utl.mask_terrian(spfh_lev,psfc,spfh)
#- 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)
spfh=spfh.where(mask3,drop=True)
uv=xr.merge([u.rename({'data': 'u'}),v.rename({'data': 'v'})])
moisture_graphics.draw_gh_uv_spfh(
spfh=spfh, 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 isentropic_uv(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
isentlev=310,
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
levels=[
1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300,
250, 200, 100
],
Global=False,
south_China_sea=True,
area='全国',
city=False,
output_dir=None):
# 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='')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
# retrieve data from micaps server
rh = get_model_3D_grid(directory=data_dir[0][0:-1],
filename=filename,
levels=levels,
allExists=False)
if rh is None:
return
u = get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if u is None:
return
v = get_model_3D_grid(directory=data_dir[2][0:-1],
filename=filename,
levels=levels,
allExists=False)
if v is None:
return
t = get_model_3D_grid(directory=data_dir[3][0:-1],
filename=filename,
levels=levels,
allExists=False)
if t is None:
return
lats = np.squeeze(rh['lat'].values)
lons = np.squeeze(rh['lon'].values)
pres = np.array(levels) * 100 * units('Pa')
tmp = t['data'].values.squeeze() * units('degC')
uwnd = u['data'].values.squeeze() * units.meter / units.second
vwnd = v['data'].values.squeeze() * units.meter / units.second
relh = rh['data'].values.squeeze() * units.meter / units.percent
isentlev = isentlev * units.kelvin
isent_anal = mpcalc.isentropic_interpolation(isentlev,
pres,
tmp,
relh,
uwnd,
vwnd,
axis=0)
isentprs, isentrh, isentu, isentv = isent_anal
# 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((lons > map_extent[0] - delt_x)
& (lons < map_extent[1] + delt_x))
idx_y1 = np.where((lats > map_extent[2] - delt_y)
& (lats < map_extent[3] + delt_y))
#- to solve the problem of labels on all the contours
init_time = u.coords['forecast_reference_time'].values
isentrh = {
'lon':
lons[idx_x1],
'lat':
lats[idx_y1],
'data':
np.array(isentrh)[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'lev':
str(isentlev),
'model':
model,
'fhour':
fhour,
'init_time':
init_time
}
isentuv = {
'lon':
lons[idx_x1],
'lat':
lats[idx_y1],
'isentu':
np.array(isentu)[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'isentv':
np.array(isentv)[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'lev':
str(isentlev)
}
isentprs = {
'lon':
lons[idx_x1],
'lat':
lats[idx_y1],
'data':
np.array(isentprs)[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'lev':
str(isentlev)
}
isentropic_graphics.draw_isentropic_uv(isentrh=isentrh,
isentuv=isentuv,
isentprs=isentprs,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def Miller_Composite_Chart(initial_time=None,
fhour=24,
day_back=0,
model='GRAPES_GFS',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
Global=False,
south_China_sea=True,
area='全国',
city=False,
output_dir=None):
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='RH',
lvl='700'),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl='300'),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl='300'),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl='500'),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl='500'),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl='850'),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl='850'),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl='700'),
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='BLI'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='Td2m'),
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 (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
filename2 = utl.model_filename(initial_time, fhour - 12)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
filename2 = utl.filename_day_back_model(day_back=day_back,
fhour=fhour - 12)
# retrieve data from micaps server
rh_700 = get_model_grid(directory=data_dir[0], filename=filename)
if rh_700 is None:
return
u_300 = get_model_grid(directory=data_dir[1], filename=filename)
if u_300 is None:
return
v_300 = get_model_grid(directory=data_dir[2], filename=filename)
if v_300 is None:
return
u_500 = get_model_grid(directory=data_dir[3], filename=filename)
if u_500 is None:
return
v_500 = get_model_grid(directory=data_dir[4], filename=filename)
if v_500 is None:
return
u_850 = get_model_grid(directory=data_dir[5], filename=filename)
if u_850 is None:
return
v_850 = get_model_grid(directory=data_dir[6], filename=filename)
if v_850 is None:
return
t_700 = get_model_grid(directory=data_dir[7], filename=filename)
if t_700 is None:
return
hgt_500 = get_model_grid(directory=data_dir[8], filename=filename)
if hgt_500 is None:
return
hgt_500_2 = get_model_grid(directory=data_dir[8], filename=filename2)
if hgt_500_2 is None:
return
BLI = get_model_grid(directory=data_dir[9], filename=filename)
if BLI is None:
return
Td2m = get_model_grid(directory=data_dir[10], filename=filename)
if Td2m is None:
return
PRMSL = get_model_grid(directory=data_dir[11], filename=filename)
if PRMSL is None:
return
PRMSL2 = get_model_grid(directory=data_dir[11], filename=filename2)
if PRMSL2 is None:
return
lats = np.squeeze(rh_700['lat'].values)
lons = np.squeeze(rh_700['lon'].values)
x, y = np.meshgrid(rh_700['lon'], rh_700['lat'])
tmp_700 = t_700['data'].values.squeeze() * units('degC')
u_300 = (u_300['data'].values.squeeze() * units.meter /
units.second).to('kt')
v_300 = (v_300['data'].values.squeeze() * units.meter /
units.second).to('kt')
u_500 = (u_500['data'].values.squeeze() * units.meter /
units.second).to('kt')
v_500 = (v_500['data'].values.squeeze() * units.meter /
units.second).to('kt')
u_850 = (u_850['data'].values.squeeze() * units.meter /
units.second).to('kt')
v_850 = (v_850['data'].values.squeeze() * units.meter /
units.second).to('kt')
hgt_500 = (hgt_500['data'].values.squeeze()) * 10 / 9.8 * units.meter
rh_700 = rh_700['data'].values.squeeze()
lifted_index = BLI['data'].values.squeeze() * units.kelvin
Td_sfc = Td2m['data'].values.squeeze() * units('degC')
dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)
avor_500 = mpcalc.absolute_vorticity(u_500, v_500, dx, dy,
y * units.degree)
pmsl = PRMSL['data'].values.squeeze() * units('hPa')
hgt_500_2 = (hgt_500_2['data'].values.squeeze()) * 10 / 9.8 * units.meter
pmsl2 = PRMSL2['data'].values.squeeze() * units('hPa')
# 500 hPa CVA
vort_adv_500 = mpcalc.advection(
avor_500, [u_500.to('m/s'), v_500.to('m/s')],
(dx, dy), dim_order='yx') * 1e9
vort_adv_500_smooth = gaussian_filter(vort_adv_500, 4)
wspd_300 = gaussian_filter(mpcalc.wind_speed(u_300, v_300), 5)
wspd_500 = gaussian_filter(mpcalc.wind_speed(u_500, v_500), 5)
wspd_850 = gaussian_filter(mpcalc.wind_speed(u_850, v_850), 5)
Td_dep_700 = tmp_700 - mpcalc.dewpoint_rh(tmp_700, rh_700 / 100.)
pmsl_change = pmsl - pmsl2
hgt_500_change = hgt_500 - hgt_500_2
mask_500 = ma.masked_less_equal(wspd_500, 0.66 * np.max(wspd_500)).mask
u_500[mask_500] = np.nan
v_500[mask_500] = np.nan
# 300 hPa
mask_300 = ma.masked_less_equal(wspd_300, 0.66 * np.max(wspd_300)).mask
u_300[mask_300] = np.nan
v_300[mask_300] = np.nan
# 850 hPa
mask_850 = ma.masked_less_equal(wspd_850, 0.66 * np.max(wspd_850)).mask
u_850[mask_850] = np.nan
v_850[mask_850] = np.nan
# 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((lons > map_extent[0] - delt_x)
& (lons < map_extent[1] + delt_x))
idx_y1 = np.where((lats > map_extent[2] - delt_y)
& (lats < map_extent[3] + delt_y))
fcst_info = {
'lon': lons,
'lat': lats,
'fhour': fhour,
'model': model,
'init_time': t_700.coords['forecast_reference_time'].values
}
synthetical_graphics.draw_Miller_Composite_Chart(
fcst_info=fcst_info,
u_300=u_300,
v_300=v_300,
u_500=u_500,
v_500=v_500,
u_850=u_850,
v_850=v_850,
pmsl_change=pmsl_change,
hgt_500_change=hgt_500_change,
Td_dep_700=Td_dep_700,
Td_sfc=Td_sfc,
pmsl=pmsl,
lifted_index=lifted_index,
vort_adv_500_smooth=vort_adv_500_smooth,
map_extent=map_extent,
add_china=True,
city=False,
south_China_sea=True,
output_dir=None,
Global=False)
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 wind_temp_rn_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, 400, 300, 250, 200,
150
],
map_ratio=19 / 9,
zoom_ratio=1,
south_China_sea=False,
area=None,
city=False,
output_dir=None,
bkgd_type='satellite',
data_source='MICAPS',
**kwargs):
# micaps data directory
if (area != None):
south_China_sea = False
# prepare data
if (area != None):
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='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='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
TMP = MICAPS_IO.get_model_3D_grid(directory=data_dir[1][0:-1],
filename=filename,
levels=levels,
allExists=False)
if TMP 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]]
#maskout area
delt_xy = TMP['lon'].values[1] - TMP['lon'].values[0]
#+ to solve the problem of labels on all the contours
mask1 = (TMP['lon'] > map_extent[0] - delt_xy) & (
TMP['lon'] < map_extent[1] + delt_xy) & (
TMP['lat'] > map_extent[2] - delt_xy) & (TMP['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
TMP = TMP.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(TMP['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_TMP = 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)
TMP_sta = interpolator_TMP(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']))
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']))
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)])
TMP_sta[np.isnan(TMP_sta)] = np.squeeze(
np.array(t2m_2D)[np.isnan(TMP_sta)])
else:
wsp_sta[np.isnan(wsp_sta)] = np.squeeze(wsp10m_2D)[np.isnan(
wsp_sta)]
TMP_sta[np.isnan(TMP_sta)] = np.squeeze(
np.array(t2m_2D))[np.isnan(TMP_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)]
for ista in range(0, len(wsp10m_sta)):
if (wsp10m_sta[ista] > wsp_sm_sta[ista]):
wsp_sm_sta[ista] = wsp10m_sta[ista]
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'],
'TMP': np.array(TMP_sta),
'U': np.squeeze(np.array(u_sta)),
'V': np.squeeze(np.array(v_sta))
}
fcst_info = gh.coords
local_scale_graphics.draw_wind_temp_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=output_dir)
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 gh_rain(initial_time=None,
fhour=24,
day_back=0,
model='ECMWF',
gh_lev='500',
atime=6,
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='surface',
data_source=model,
var_name='RAIN' + '%02d' % atime)
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
if (atime > 3):
filename_gh = utl.model_filename(initial_time, fhour / 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=fhour / 2.)
# retrieve data from micaps server
gh = get_model_grid(data_dir[0], filename=filename_gh)
if gh is None:
return
rain = get_model_grid(data_dir[1], 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((rain.coords['lon'].values > map_extent[0] - delt_x)
& (rain.coords['lon'].values < map_extent[1] + delt_x))
idx_y2 = np.where((rain.coords['lat'].values > map_extent[2] - delt_y)
& (rain.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
}
rain = {
'lon':
rain.coords['lon'].values[idx_x2],
'lat':
rain.coords['lat'].values[idx_y2],
'data':
copy.deepcopy(rain['data'].values[0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)])
}
QPF_graphics.draw_gh_rain(rain=rain,
gh=gh,
atime=atime,
map_extent=map_extent,
regrid_shape=20,
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 T2m_mslp_uv10m(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
data_source='MICAPS',
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
# 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='u10m'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='v10m'),
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)
# retrieve data from micaps server
mslp = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
if mslp is None:
return
u10m = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if u10m is None:
return
v10m = MICAPS_IO.get_model_grid(data_dir[2], filename=filename)
if v10m is None:
return
t2m = MICAPS_IO.get_model_grid(data_dir[3], filename=filename)
if t2m 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
t2m = CMISS_IO.cimiss_model_by_time('20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(
data_source=model,
var_name='TEF2'),
fcst_level=0,
fcst_ele="TEF2",
units='K')
if t2m is None:
return
t2m['data'].values = t2m['data'].values - 273.15
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='WIU10'),
fcst_level=0,
fcst_ele="WIU10",
units='m/s')
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='WIV10'),
fcst_level=0,
fcst_ele="WIV10",
units='m/s')
if v10m 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')
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
if (area != '全国'):
south_China_sea = False
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 = (t2m['lon'] > map_extent[0] - delt_x) & (
t2m['lon'] < map_extent[1] + delt_x) & (
t2m['lat'] > map_extent[2] - delt_y) & (t2m['lat'] <
map_extent[3] + delt_y)
mask2 = (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)
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)
t2m = t2m.where(mask1, drop=True)
t2m.attrs['model'] = model
u10m = u10m.where(mask2, drop=True)
v10m = v10m.where(mask2, drop=True)
uv10m = xr.merge(
[u10m.rename({'data': 'u10m'}),
v10m.rename({'data': 'v10m'})])
mslp = mslp.where(mask3, drop=True)
mslp.attrs['model'] = model
# draw
elements_graphics.draw_T2m_mslp_uv10m(t2m=t2m,
mslp=mslp,
uv10m=uv10m,
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 T2m_all_type(initTime=None,
fhour=24,
day_back=0,
model='中央气象台中短期指导',
Var_plot='Tmn_2m',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
data_source='MICAPS',
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
# prepare data
if (data_source == 'MICAPS'):
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')
if (initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
T_2m = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
if T_2m 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
T_2m = CMISS_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': 'Mean_sea_level',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TEF2",
units='K')
if T_2m is None:
return
T_2m['data'].values = T_2m['data'].values - 273.15
except KeyError:
raise ValueError('Can not find all data needed')
# 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 = (T_2m['lon'] > map_extent[0] - delt_x) & (
T_2m['lon'] < map_extent[1] + delt_x) & (
T_2m['lat'] > map_extent[2] - delt_y) & (T_2m['lat'] <
map_extent[3] + delt_y)
T_2m = T_2m.where(mask1, drop=True)
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.attrs['model'] = model
T_2m.attrs['title'] = titles[Var_plot]
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 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 mslp_gust10m(initTime=None,
fhour=6,
day_back=0,
model='ECMWF',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
data_source='MICAPS',
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='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 = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
if mslp is None:
return
gust = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if gust 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:
gust = CMISS_IO.cimiss_model_by_time('20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(
data_source=model,
var_name='GUST10T6'),
fcst_level=0,
fcst_ele="GUST10T6",
units='m/s')
if gust 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'),
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'),
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)
if (area != '全国'):
south_China_sea = False
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 = (gust['lon'] > map_extent[0] - delt_x) & (
gust['lon'] < map_extent[1] + delt_x) & (
gust['lat'] > map_extent[2] - delt_y) & (gust['lat'] <
map_extent[3] + delt_y)
mask2 = (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)
gust = gust.where(mask1, drop=True)
mslp = mslp.where(mask2, drop=True)
mslp.attrs['model'] = model
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',
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 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 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',
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 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 gh_uv_tadv(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):
#prepare data
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='TMP',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(initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour)
# retrieve data from micaps server
gh = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
if gh is None:
return
u = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if u is None:
return
v = MICAPS_IO.get_model_grid(data_dir[2], filename=filename)
if v is None:
return
tmp = MICAPS_IO.get_model_grid(data_dir[3], filename=filename)
if tmp 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
tmp=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
levattrs={'long_name':'pressure_level', 'units':'hPa', '_CoordinateAxisType':'-'},
fcst_level=uv_lev, fcst_ele="TEM", units='K')
if tmp is None:
return
tmp['data'].values=tmp['data'].values-273.15
psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'),
fcst_level=0, fcst_ele="PRS", units='Pa')
psfc['data']=psfc['data']/100.
except KeyError:
raise ValueError('Can not find all data needed')
# 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=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)
tmp=utl.cut_xrdata(map_extent, tmp, delt_x=delt_x, delt_y=delt_y)
tadv_xr=tmp.copy(deep=True)
tadv=-1*mbd.advection(tmp['data'].values.squeeze(),u['data'].values.squeeze(),v['data'].values.squeeze(),tmp['lat'].values,tmp['lon'].values)
tadv_xr['data'].values=tadv[np.newaxis,np.newaxis,:,:]
gh=utl.mask_terrian(gh_lev,psfc,gh)
u=utl.mask_terrian(uv_lev,psfc,u)
tadv_xr=utl.mask_terrian(uv_lev,psfc,tadv_xr)
gh.attrs['model']=model
uv=xr.merge([u.rename({'data': 'u'}),v.rename({'data': 'v'})])
#draw
thermal_graphics.draw_gh_uv_tadv(
tadv=tadv_xr, 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)
