Python 2.7 使用xarray读取grib2文件
我需要用xarray打开一个grib2文件。为此，我在xarray中使用python 2.7和pynio作为引擎：Python 2.7 使用xarray读取grib2文件,python-2.7,python-xarray,Python 2.7,Python Xarray,我需要用xarray打开一个grib2文件。为此，我在xarray中使用python 2.7和pynio作为引擎： grbs = xr.open_dataset('hrrr.t06z.wrfsubhf02.grib2'], engine = 'pynio') 输出： <xarray.Dataset> Dimensions: (forecast_time0: 4, lv_HTGL0: 2, lv_HTGL1: 2, xgrid_0: 1799,
grbs = xr.open_dataset('hrrr.t06z.wrfsubhf02.grib2'], engine = 'pynio')
<xarray.Dataset>
Dimensions: (forecast_time0: 4, lv_HTGL0: 2, lv_HTGL1: 2, xgrid_0: 1799, ygrid_0: 1059)
Coordinates:
* forecast_time0 (forecast_time0) timedelta64[ns] 5 days 15:00:00 ...
* lv_HTGL1 (lv_HTGL1) float32 1000.0 4000.0
* lv_HTGL0 (lv_HTGL0) float32 10.0 80.0
gridlat_0 (ygrid_0, xgrid_0) float32 21.1381 21.1451 ...
gridlon_0 (ygrid_0, xgrid_0) float32 -122.72 -122.693 ...
Dimensions without coordinates: xgrid_0, ygrid_0
Data variables:
ULWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 446.3 ...
WIND_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 5.31 ...
SBT124_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 288.7 ...
VDDSF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
VIS_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 8.4e+03 ...
DSWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CICEP_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
DLWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 429.7 ...
USWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
ULWRF_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 291.0 ...
HGT_P0_L3_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.4e+03 ...
VGRD_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 -4.92 ...
gridrot_0 (ygrid_0, xgrid_0) float32 -0.274008 ...
VIL_P0_L10_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0048 ...
CSNOW_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
SBT123_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 251.5 ...
GUST_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 6.469 ...
SBT114_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 289.4 ...
DPT_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 295.8 ...
UGRD_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 -2.02 ...
RETOP_P0_L3_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 -999.0 ...
REFD_P0_L103_GLC0 (forecast_time0, lv_HTGL1, ygrid_0, xgrid_0) float64 -10.0 ...
TMP_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 297.0 ...
UGRD_P0_L103_GLC0 (forecast_time0, lv_HTGL0, ygrid_0, xgrid_0) float64 -1.998 ...
HGT_P0_L215_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 461.2 ...
UPHL_P0_2L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
SBT113_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 253.9 ...
VBDSF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
PRATE_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CFRZR_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CPOFP_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 -50.0 ...
CRAIN_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
REFC_P0_L10_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.0 ...
DSWRF_P8_L1_GLC0_avg15min (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
VBDSF_P8_L1_GLC0_avg15min (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
PRES_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.014e+05 ...
SPFH_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.01705 ...
VGRD_P0_L103_GLC0 (forecast_time0, lv_HTGL0, ygrid_0, xgrid_0) float64 -4.939 ...
HGT_P0_L2_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 869.5 ...
0}).sel(**{'ygrid_0': slice(22,24), 'xgrid_0': slice(-115,-110)})
<xarray.DataArray 'DSWRF_P8_L1_GLC0_avg15min' (ygrid_0: 2, xgrid_0: 5)>
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
Coordinates:
forecast_time0 timedelta64[ns] 5 days 15:00:00
gridlat_0 (ygrid_0, xgrid_0) float32 22.4459 22.4396 22.4334 ...
gridlon_0 (ygrid_0, xgrid_0) float32 -75.2096 -75.1823 -75.155 ...
Dimensions without coordinates: ygrid_0, xgrid_0
Attributes:
production_status: Operational products
center: US National Weather Servi...
level: [ 0.]
type_of_statistical_processing: Average
long_name: Downward short-wave radia...
parameter_template_discipline_category_number: [8 0 4 7]
initial_time: 09/06/2017 (06:00)
grid_type: Lambert Conformal can be ...
units: W m-2
statistical_process_duration: 15 minutes (ending at for...
level_type: Ground or water surface
parameter_discipline_and_category: Meteorological products, ...
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-0c24d2bdb040> in <module>()
7 #data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(gridlon_0=-75.2096, gridlat_0=22.4396, method='nearest') # ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
8 #data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(lat=gridlat_0, lon = gridlon_0) # ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
----> 9 data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(gridlon_0=-75.2096, gridlat_0=22.4396, method='nearest')
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/dataarray.pyc in sel(self, method, tolerance, drop, **indexers)
690 """
691 pos_indexers, new_indexes = indexing.remap_label_indexers(
--> 692 self, indexers, method=method, tolerance=tolerance
693 )
694 result = self.isel(drop=drop, **pos_indexers)
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/indexing.pyc in remap_label_indexers(data_obj, indexers, method, tolerance)
275 new_indexes = {}
276
--> 277 dim_indexers = get_dim_indexers(data_obj, indexers)
278 for dim, label in iteritems(dim_indexers):
279 try:
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/indexing.pyc in get_dim_indexers(data_obj, indexers)
243 if invalid:
244 raise ValueError("dimensions or multi-index levels %r do not exist"
--> 245 % invalid)
246
247 level_indexers = defaultdict(dict)
ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
<xarray.Dataset>
Dimensions: (forecast_time0: 4, lv_HTGL0: 2, lv_HTGL1: 2, xgrid_0: 1799, ygrid_0: 1059)
Coordinates:
* forecast_time0 (forecast_time0) timedelta64[ns] 5 days 15:00:00 ...
* lv_HTGL1 (lv_HTGL1) float32 1000.0 4000.0
* lv_HTGL0 (lv_HTGL0) float32 10.0 80.0
gridlat_0 (ygrid_0, xgrid_0) float32 21.1381 21.1451 ...
gridlon_0 (ygrid_0, xgrid_0) float32 -122.72 -122.693 ...
Dimensions without coordinates: xgrid_0, ygrid_0
Data variables:
ULWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 446.3 ...
WIND_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 5.31 ...
SBT124_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 288.7 ...
VDDSF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
VIS_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 8.4e+03 ...
DSWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CICEP_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
DLWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 429.7 ...
USWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
ULWRF_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 291.0 ...
HGT_P0_L3_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.4e+03 ...
VGRD_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 -4.92 ...
gridrot_0 (ygrid_0, xgrid_0) float32 -0.274008 ...
VIL_P0_L10_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0048 ...
CSNOW_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
SBT123_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 251.5 ...
GUST_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 6.469 ...
SBT114_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 289.4 ...
DPT_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 295.8 ...
UGRD_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 -2.02 ...
RETOP_P0_L3_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 -999.0 ...
REFD_P0_L103_GLC0 (forecast_time0, lv_HTGL1, ygrid_0, xgrid_0) float64 -10.0 ...
TMP_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 297.0 ...
UGRD_P0_L103_GLC0 (forecast_time0, lv_HTGL0, ygrid_0, xgrid_0) float64 -1.998 ...
HGT_P0_L215_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 461.2 ...
UPHL_P0_2L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
SBT113_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 253.9 ...
VBDSF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
PRATE_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CFRZR_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CPOFP_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 -50.0 ...
CRAIN_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
REFC_P0_L10_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.0 ...
DSWRF_P8_L1_GLC0_avg15min (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
VBDSF_P8_L1_GLC0_avg15min (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
PRES_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.014e+05 ...
SPFH_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.01705 ...
VGRD_P0_L103_GLC0 (forecast_time0, lv_HTGL0, ygrid_0, xgrid_0) float64 -4.939 ...
HGT_P0_L2_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 869.5 ...
0}).sel(**{'ygrid_0': slice(22,24), 'xgrid_0': slice(-115,-110)})
<xarray.DataArray 'DSWRF_P8_L1_GLC0_avg15min' (ygrid_0: 2, xgrid_0: 5)>
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
Coordinates:
forecast_time0 timedelta64[ns] 5 days 15:00:00
gridlat_0 (ygrid_0, xgrid_0) float32 22.4459 22.4396 22.4334 ...
gridlon_0 (ygrid_0, xgrid_0) float32 -75.2096 -75.1823 -75.155 ...
Dimensions without coordinates: ygrid_0, xgrid_0
Attributes:
production_status: Operational products
center: US National Weather Servi...
level: [ 0.]
type_of_statistical_processing: Average
long_name: Downward short-wave radia...
parameter_template_discipline_category_number: [8 0 4 7]
initial_time: 09/06/2017 (06:00)
grid_type: Lambert Conformal can be ...
units: W m-2
statistical_process_duration: 15 minutes (ending at for...
level_type: Ground or water surface
parameter_discipline_and_category: Meteorological products, ...
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-0c24d2bdb040> in <module>()
7 #data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(gridlon_0=-75.2096, gridlat_0=22.4396, method='nearest') # ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
8 #data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(lat=gridlat_0, lon = gridlon_0) # ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
----> 9 data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(gridlon_0=-75.2096, gridlat_0=22.4396, method='nearest')
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/dataarray.pyc in sel(self, method, tolerance, drop, **indexers)
690 """
691 pos_indexers, new_indexes = indexing.remap_label_indexers(
--> 692 self, indexers, method=method, tolerance=tolerance
693 )
694 result = self.isel(drop=drop, **pos_indexers)
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/indexing.pyc in remap_label_indexers(data_obj, indexers, method, tolerance)
275 new_indexes = {}
276
--> 277 dim_indexers = get_dim_indexers(data_obj, indexers)
278 for dim, label in iteritems(dim_indexers):
279 try:
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/indexing.pyc in get_dim_indexers(data_obj, indexers)
243 if invalid:
244 raise ValueError("dimensions or multi-index levels %r do not exist"
--> 245 % invalid)
246
247 level_indexers = defaultdict(dict)
ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
<xarray.Dataset>
Dimensions: (forecast_time0: 4, lv_HTGL0: 2, lv_HTGL1: 2, xgrid_0: 1799, ygrid_0: 1059)
Coordinates:
* forecast_time0 (forecast_time0) timedelta64[ns] 5 days 15:00:00 ...
* lv_HTGL1 (lv_HTGL1) float32 1000.0 4000.0
* lv_HTGL0 (lv_HTGL0) float32 10.0 80.0
gridlat_0 (ygrid_0, xgrid_0) float32 21.1381 21.1451 ...
gridlon_0 (ygrid_0, xgrid_0) float32 -122.72 -122.693 ...
Dimensions without coordinates: xgrid_0, ygrid_0
Data variables:
ULWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 446.3 ...
WIND_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 5.31 ...
SBT124_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 288.7 ...
VDDSF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
VIS_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 8.4e+03 ...
DSWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CICEP_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
DLWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 429.7 ...
USWRF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
ULWRF_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 291.0 ...
HGT_P0_L3_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.4e+03 ...
VGRD_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 -4.92 ...
gridrot_0 (ygrid_0, xgrid_0) float32 -0.274008 ...
VIL_P0_L10_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0048 ...
CSNOW_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
SBT123_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 251.5 ...
GUST_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 6.469 ...
SBT114_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 289.4 ...
DPT_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 295.8 ...
UGRD_P8_L103_GLC0_avg5min (forecast_time0, ygrid_0, xgrid_0) float64 -2.02 ...
RETOP_P0_L3_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 -999.0 ...
REFD_P0_L103_GLC0 (forecast_time0, lv_HTGL1, ygrid_0, xgrid_0) float64 -10.0 ...
TMP_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 297.0 ...
UGRD_P0_L103_GLC0 (forecast_time0, lv_HTGL0, ygrid_0, xgrid_0) float64 -1.998 ...
HGT_P0_L215_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 461.2 ...
UPHL_P0_2L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
SBT113_P0_L8_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 253.9 ...
VBDSF_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
PRATE_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CFRZR_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
CPOFP_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 -50.0 ...
CRAIN_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
REFC_P0_L10_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.0 ...
DSWRF_P8_L1_GLC0_avg15min (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
VBDSF_P8_L1_GLC0_avg15min (forecast_time0, ygrid_0, xgrid_0) float64 0.0 ...
PRES_P0_L1_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 1.014e+05 ...
SPFH_P0_L103_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 0.01705 ...
VGRD_P0_L103_GLC0 (forecast_time0, lv_HTGL0, ygrid_0, xgrid_0) float64 -4.939 ...
HGT_P0_L2_GLC0 (forecast_time0, ygrid_0, xgrid_0) float64 869.5 ...
0}).sel(**{'ygrid_0': slice(22,24), 'xgrid_0': slice(-115,-110)})
<xarray.DataArray 'DSWRF_P8_L1_GLC0_avg15min' (ygrid_0: 2, xgrid_0: 5)>
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
Coordinates:
forecast_time0 timedelta64[ns] 5 days 15:00:00
gridlat_0 (ygrid_0, xgrid_0) float32 22.4459 22.4396 22.4334 ...
gridlon_0 (ygrid_0, xgrid_0) float32 -75.2096 -75.1823 -75.155 ...
Dimensions without coordinates: ygrid_0, xgrid_0
Attributes:
production_status: Operational products
center: US National Weather Servi...
level: [ 0.]
type_of_statistical_processing: Average
long_name: Downward short-wave radia...
parameter_template_discipline_category_number: [8 0 4 7]
initial_time: 09/06/2017 (06:00)
grid_type: Lambert Conformal can be ...
units: W m-2
statistical_process_duration: 15 minutes (ending at for...
level_type: Ground or water surface
parameter_discipline_and_category: Meteorological products, ...
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-37-0c24d2bdb040> in <module>()
7 #data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(gridlon_0=-75.2096, gridlat_0=22.4396, method='nearest') # ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
8 #data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(lat=gridlat_0, lon = gridlon_0) # ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
----> 9 data = grbs['DSWRF_P8_L1_GLC0_avg15min'].sel(gridlon_0=-75.2096, gridlat_0=22.4396, method='nearest')
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/dataarray.pyc in sel(self, method, tolerance, drop, **indexers)
690 """
691 pos_indexers, new_indexes = indexing.remap_label_indexers(
--> 692 self, indexers, method=method, tolerance=tolerance
693 )
694 result = self.isel(drop=drop, **pos_indexers)
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/indexing.pyc in remap_label_indexers(data_obj, indexers, method, tolerance)
275 new_indexes = {}
276
--> 277 dim_indexers = get_dim_indexers(data_obj, indexers)
278 for dim, label in iteritems(dim_indexers):
279 try:
/Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/indexing.pyc in get_dim_indexers(data_obj, indexers)
243 if invalid:
244 raise ValueError("dimensions or multi-index levels %r do not exist"
--> 245 % invalid)
246
247 level_indexers = defaultdict(dict)
ValueError: dimensions or multi-index levels ['gridlat_0', 'gridlon_0'] do not exist
---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在（）
7#data=grbs['DSWRF_P8_L1_GLC0_avg15min'].sel（gridlon_0=-75.2096，gridlat_0=22.4396，method='nearest'）#值错误：维度或多索引级别['gridlat_0'，gridlon u 0']不存在
8#data=grbs['DSWRF_P8_L1_GLC0_avg15min'].sel（lat=gridlat_0，lon=gridlon_0）#value错误：维度或多索引级别['gridlat_0'，gridlon_0']不存在
---->9数据=grbs['DSWRF_P8_L1_GLC0_avg15min'].sel（网格0=-75.2096，网格0=22.4396，方法为最近）
/sel中的Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/dataarray.pyc（self、method、tolerance、drop、**索引器）
690 """
691位置索引器，新索引=索引。重新映射标签索引器(
-->692自身，索引器，方法=方法，公差=公差
693 )
694结果=自身isel（下降=下降，**位置索引器）
/重新映射标签索引器中的Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/index.pyc（数据对象、索引器、方法、公差）
275个新的_索引={}
276
-->277尺寸索引器=获取尺寸索引器（数据对象，索引器）
278对于尺寸，在iteritems中标记（尺寸索引器）：
279试试：
/get_dim_索引器（数据对象，索引器）中的Users/maurice/anaconda3/envs/py27/lib/python2.7/site-packages/xarray/core/index.pyc
243如果无效：
244 raise VALUERROR（“维度或多索引级别%r不存在”
-->245%无效）
246
247级别索引器=默认dict（dict）
ValueError:维度或多索引级别['gridlat_0'，'gridlon_0']不存在
注意：这些文件可以在格式为hrrr.YYYMMDD/hrrr.tHHz.wrfsubhf**.grib2）的链接中找到。您的lat/lon坐标变量是二维的。像这样的最近邻查找在xarray中目前是不可能的。xarray开发人员正在积极讨论如何将其包括在包中，但没有具体的内容尚未具体化。此github问题非常相关：