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 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 Crosssection_Wind_Temp_RH( initTime=None, fhour=24, levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,200], day_back=0,model='ECMWF',data_source='MICAPS', output_dir=None, st_point = [43.5, 111.5], ed_point = [33, 125.0], map_extent=[70,140,15,55], lw_ratio=[16,9], h_pos=[0.125, 0.665, 0.25, 0.2],**kwargs ): if(data_source == 'MICAPS'): try: data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl=''), utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=''), utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=''), utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=''), utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',lvl='500'), utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')] except KeyError: raise ValueError('Can not find all directories needed') if(initTime != None): filename = utl.model_filename(initTime, fhour) else: filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour) rh=get_model_3D_grid(directory=data_dir[0][0:-1],filename=filename,levels=levels, allExists=False) u=get_model_3D_grid(directory=data_dir[1][0:-1],filename=filename,levels=levels, allExists=False) v=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False) t=get_model_3D_grid(directory=data_dir[3][0:-1],filename=filename,levels=levels, allExists=False) gh=get_model_grid(data_dir[4], filename=filename) psfc=get_model_grid(data_dir[5], filename=filename) if(data_source == 'CIMISS'): if(initTime != None): filename = utl.model_filename(initTime, fhour,UTC=True) else: filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour,UTC=True) try: rh=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'), fcst_levels=levels, fcst_ele="RHU", units='%') u=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'), fcst_levels=levels, fcst_ele="WIU", units='m/s') v=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'), fcst_levels=levels, fcst_ele="WIV", units='m/s') t=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'), fcst_levels=levels, fcst_ele="TEM", units='K') t['data'].values=t['data'].values-273.15 gh=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='GPH'), fcst_level=500, fcst_ele="GPH", units='gpm') gh['data'].values=gh['data'].values/10. psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'), fcst_level=0, fcst_ele="PRS", units='Pa') psfc['data']=psfc['data']/100. except KeyError: raise ValueError('Can not find all data needed') rh = rh.metpy.parse_cf().squeeze() u = u.metpy.parse_cf().squeeze() v = v.metpy.parse_cf().squeeze() t = t.metpy.parse_cf().squeeze() psfc=psfc.metpy.parse_cf().squeeze() #if(psfc['lon'].values[0] != t['lon'].values[0]): mask1 = ( (psfc['lon']>=t['lon'].values.min())& (psfc['lon']<=t['lon'].values.max())& (psfc['lat']>=t['lat'].values.min())& (psfc['lat']<=t['lat'].values.max()) ) t2,psfc_bdcst=xr.broadcast(t['data'],psfc['data'].where(mask1, drop=True)) mask2=(psfc_bdcst > -10000) psfc_bdcst=psfc_bdcst.where(mask2, drop=True) #else: # psfc_bdcst=psfc['data'].copy resolution=u['lon'][1]-u['lon'][0] x,y=np.meshgrid(u['lon'], u['lat']) dx,dy=mpcalc.lat_lon_grid_deltas(u['lon'],u['lat']) cross = cross_section(rh, st_point, ed_point) cross_rh=cross.set_coords(('lat', 'lon')) cross = cross_section(u, st_point, ed_point) cross_u=cross.set_coords(('lat', 'lon')) cross = cross_section(v, st_point, ed_point) cross_v=cross.set_coords(('lat', 'lon')) cross_psfc = cross_section(psfc_bdcst, st_point, ed_point) cross_u['data'].attrs['units']=units.meter/units.second cross_v['data'].attrs['units']=units.meter/units.second cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(cross_u['data'],cross_v['data']) cross = cross_section(t, st_point, ed_point) cross_Temp=cross.set_coords(('lat', 'lon')) cross_Td = mpcalc.dewpoint_rh(cross_Temp['data'].values*units.celsius, cross_rh['data'].values* units.percent) rh,pressure = xr.broadcast(cross_rh['data'],cross_Temp['level']) cross_terrain=pressure-cross_psfc crossection_graphics.draw_Crosssection_Wind_Temp_RH( cross_rh=cross_rh, cross_Temp=cross_Temp, cross_u=cross_u, cross_v=cross_v,cross_terrain=cross_terrain,gh=gh, h_pos=h_pos,st_point=st_point,ed_point=ed_point,lw_ratio=lw_ratio, levels=levels,map_extent=map_extent,model=model, output_dir=output_dir)
def 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 Crosssection_Wind_Theta_e_Qv( initTime=None, fhour=24, levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,200], day_back=0,model='GRAPES_GFS',data_source='MICAPS', lw_ratio=[16,9], output_dir=None, st_point = [20, 120.0], ed_point = [50, 130.0], map_extent=[70,140,15,55], h_pos=[0.125, 0.665, 0.25, 0.2],**kwargs ): if(data_source == 'MICAPS'): try: data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl=''), utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=''), utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=''), utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=''), utl.Cassandra_dir(data_type='high',data_source=model,var_name='HGT',lvl='500'), utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')] except KeyError: raise ValueError('Can not find all directories needed') # get filename if(initTime != None): filename = utl.model_filename(initTime, fhour) else: filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour) # retrieve data from micaps server rh=get_model_3D_grid(directory=data_dir[0][0:-1],filename=filename,levels=levels, allExists=False) u=get_model_3D_grid(directory=data_dir[1][0:-1],filename=filename,levels=levels, allExists=False) v=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False) v2=get_model_3D_grid(directory=data_dir[2][0:-1],filename=filename,levels=levels, allExists=False) t=get_model_3D_grid(directory=data_dir[3][0:-1],filename=filename,levels=levels, allExists=False) gh=get_model_grid(data_dir[4], filename=filename) psfc=get_model_grid(data_dir[5], filename=filename) if(data_source == 'CIMISS'): # get filename if(initTime != None): filename = utl.model_filename(initTime, fhour,UTC=True) else: filename=utl.filename_day_back_model(day_back=day_back,fhour=fhour,UTC=True) try: rh=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'), fcst_levels=levels, fcst_ele="RHU", units='%') u=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'), fcst_levels=levels, fcst_ele="WIU", units='m/s') v=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'), fcst_levels=levels, fcst_ele="WIV", units='m/s') v2=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'), fcst_levels=levels, fcst_ele="WIV", units='m/s') t=CMISS_IO.cimiss_model_3D_grid(init_time_str='20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'), fcst_levels=levels, fcst_ele="TEM", units='K') t['data'].values=t['data'].values-273.15 gh=CMISS_IO.cimiss_model_by_time('20'+filename[0:8],valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='GPH'), fcst_level=500, fcst_ele="GPH", units='gpm') gh['data'].values=gh['data'].values/10. psfc=CMISS_IO.cimiss_model_by_time('20'+filename[0:8], valid_time=fhour, data_code=utl.CMISS_data_code(data_source=model,var_name='PRS'), fcst_level=0, fcst_ele="PRS", units='Pa') psfc['data']=psfc['data']/100. except KeyError: raise ValueError('Can not find all data needed') rh = rh.metpy.parse_cf().squeeze() u = u.metpy.parse_cf().squeeze() v = v.metpy.parse_cf().squeeze() v2 = v2.metpy.parse_cf().squeeze() psfc=psfc.metpy.parse_cf().squeeze() t = t.metpy.parse_cf().squeeze() resolution=u['lon'][1]-u['lon'][0] x,y=np.meshgrid(u['lon'], u['lat']) # +form 3D psfc mask1 = ( (psfc['lon']>=t['lon'].values.min())& (psfc['lon']<=t['lon'].values.max())& (psfc['lat']>=t['lat'].values.min())& (psfc['lat']<=t['lat'].values.max()) ) t2,psfc_bdcst=xr.broadcast(t['data'],psfc['data'].where(mask1, drop=True)) mask2=(psfc_bdcst > -10000) psfc_bdcst=psfc_bdcst.where(mask2, drop=True) # -form 3D psfc dx,dy=mpcalc.lat_lon_grid_deltas(u['lon'],u['lat']) #rh=rh.rename(dict(lat='latitude',lon='longitude')) cross = cross_section(rh, st_point, ed_point) cross_rh=cross.set_coords(('lat', 'lon')) cross = cross_section(u, st_point, ed_point) cross_u=cross.set_coords(('lat', 'lon')) cross = cross_section(v, st_point, ed_point) cross_v=cross.set_coords(('lat', 'lon')) cross_psfc = cross_section(psfc_bdcst, st_point, ed_point) cross_u['data'].attrs['units']=units.meter/units.second cross_v['data'].attrs['units']=units.meter/units.second cross_u['t_wind'], cross_v['n_wind'] = mpcalc.cross_section_components(cross_u['data'],cross_v['data']) cross = cross_section(t, st_point, ed_point) cross_t=cross.set_coords(('lat', 'lon')) cross_Td = mpcalc.dewpoint_rh(cross_t['data'].values*units.celsius, cross_rh['data'].values* units.percent) rh,pressure = xr.broadcast(cross_rh['data'],cross_t['level']) pressure.attrs['units']='hPa' Qv = mpcalc.specific_humidity_from_dewpoint(cross_Td, pressure) cross_Qv = xr.DataArray(np.array(Qv)*1000., coords=cross_rh['data'].coords, dims=cross_rh['data'].dims, attrs={'units': units('g/kg')}) Theta_e=mpcalc.equivalent_potential_temperature(pressure, cross_t['data'].values*units.celsius, cross_Td) cross_terrain=pressure-cross_psfc cross_Theta_e = xr.DataArray(np.array(Theta_e), coords=cross_rh['data'].coords, dims=cross_rh['data'].dims, attrs={'units': Theta_e.units}) crossection_graphics.draw_Crosssection_Wind_Theta_e_Qv( cross_Qv=cross_Qv, cross_Theta_e=cross_Theta_e, cross_u=cross_u, cross_v=cross_v,cross_terrain=cross_terrain,gh=gh, h_pos=h_pos,st_point=st_point,ed_point=ed_point, levels=levels,map_extent=map_extent,lw_ratio=lw_ratio, output_dir=output_dir)