GFS#
This demonstrates how to download and use GFS data.
[1]:
from herbie import Herbie
from herbie.toolbox import EasyMap, pc, ccrs
from herbie import paint
import matplotlib.pyplot as plt
[2]:
H = Herbie("2021-07-11", model="gfs", product="pgrb2.0p25")
✅ Found ┊ model=gfs ┊ product=pgrb2.0p25 ┊ 2021-Jul-11 00:00 UTC F00 ┊ GRIB2 @ aws ┊ IDX @ aws
[3]:
# Show all available sources
H.SOURCES
[3]:
{'aws': 'https://noaa-gfs-bdp-pds.s3.amazonaws.com/gfs.20210711/00/atmos/gfs.t00z.pgrb2.0p25.f000',
'ftpprd': 'https://ftpprd.ncep.noaa.gov/data/nccf/com/gfs/prod/gfs.20210711/00/atmos/gfs.t00z.pgrb2.0p25.f000',
'nomads': 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/gfs/prod/gfs.20210711/00/atmos/gfs.t00z.pgrb2.0p25.f000',
'google': 'https://storage.googleapis.com/global-forecast-system/gfs.20210711/00/atmos/gfs.t00z.pgrb2.0p25.f000',
'azure': 'https://noaagfs.blob.core.windows.net/gfs/gfs.20210711/00/atmos/gfs.t00z.pgrb2.0p25.f000',
'ncar_rda': 'https://data.rda.ucar.edu/d084001/2021/20210711/gfs.0p25.2021071100.f000.grib2'}
[4]:
# Show all available products
H.PRODUCTS
[4]:
{'pgrb2.0p25': 'common fields, 0.25 degree resolution',
'pgrb2.0p50': 'common fields, 0.50 degree resolution',
'pgrb2.1p00': 'common fields, 1.00 degree resolution',
'pgrb2b.0p25': 'uncommon fields, 0.25 degree resolution',
'pgrb2b.0p50': 'uncommon fields, 0.50 degree resolution',
'pgrb2b.1p00': 'uncommon fields, 1.00 degree resolution',
'pgrb2full.0p50': 'combined grids of 0.50 resolution',
'sfluxgrb': 'surface flux fields, T1534 Semi-Lagrangian grid',
'goesimpgrb2.0p25': ', 0.50 degree resolution'}
[5]:
ds = H.xarray(":TMP:2 m above")
👨🏻🏭 Created directory: [/home/blaylock/data/gfs/20210711]
[6]:
ax = EasyMap(crs=ds.herbie.crs, figsize=[10, 8]).ax
p = ax.pcolormesh(
ds.longitude,
ds.latitude,
ds.t2m - 273.15,
transform=pc,
**paint.NWSTemperature.kwargs2,
)
plt.colorbar(
p, ax=ax, orientation="horizontal", pad=0.05, **paint.NWSTemperature.cbar_kwargs2
)
ax.set_title(ds.t2m.GRIB_name, loc="right")
ax.set_title(f"{ds.model.upper()}: {H.product_description}", loc="left")
[6]:
Text(0.0, 1.0, 'GFS: common fields, 0.25 degree resolution')
Let’s change it up and plot on a Geostationary projection.
[7]:
ax = EasyMap(crs=ccrs.Geostationary(central_longitude=-100), figsize=[10, 10]).ax
p = ax.pcolormesh(
ds.longitude,
ds.latitude,
ds.t2m - 273.15,
transform=pc,
**paint.NWSTemperature.kwargs2,
)
plt.colorbar(
p,
ax=ax,
orientation="horizontal",
pad=0.05,
shrink=0.8,
**paint.NWSTemperature.cbar_kwargs2,
)
ax.set_title(ds.t2m.GRIB_name, loc="right")
ax.set_title(f"{ds.model.upper()}: {H.product_description}", loc="left")
[7]:
Text(0.0, 1.0, 'GFS: common fields, 0.25 degree resolution')
[8]:
ds2 = H.xarray("RH:2 m above")
ax = EasyMap(crs=ccrs.Robinson(), figsize=[8, 8]).ax
p = ax.pcolormesh(
ds2.longitude,
ds2.latitude,
ds2.r2,
transform=pc,
**paint.NWSRelativeHumidity.kwargs,
)
plt.colorbar(
p,
ax=ax,
orientation="horizontal",
pad=0.05,
shrink=0.8,
**paint.NWSRelativeHumidity.cbar_kwargs,
)
[8]:
<matplotlib.colorbar.Colorbar at 0x7fb369862a20>
GFS from NCEI#
Depending on the date, Herbie might find old GFS data at NCEI or RDA for dates older than January 1, 2021.
[9]:
# Look for file in all available sources
H = Herbie("2020-06-01", model="gfs")
✅ Found ┊ model=gfs ┊ product=0.5-degree ┊ 2020-Jun-01 00:00 UTC F00 ┊ GRIB2 @ ncei_analysis ┊ IDX @ ncei_analysis
[10]:
H.SOURCES, H.PRODUCTS
[10]:
({'ncei_analysis': 'https://www.ncei.noaa.gov/data/global-forecast-system/access/grid-004-0.5-degree/analysis/202006/20200601/gfs_4_20200601_0000_000.grb2',
'ncei_forecast': 'https://www.ncei.noaa.gov/data/global-forecast-system/access/grid-004-0.5-degree/forecast/202006/20200601/gfs_4_20200601_0000_000.grb2',
'ncar_rda': 'https://data.rda.ucar.edu/d084001/2020/20200601/gfs.0p25.2020060100.f000.grib2'},
{'0.5-degree': '0.5 degree grid', '1.0-degree': '1.0 degree grid'})
[11]:
H.inventory("GRD:80 m above")
[11]:
| grib_message | start_byte | end_byte | range | reference_time | valid_time | variable | level | forecast_time | search_this | |
|---|---|---|---|---|---|---|---|---|---|---|
| 465 | 466 | 80208994 | 80496003.0 | 80208994-80496003 | 2020-06-01 | 2020-06-01 | UGRD | 80 m above ground | anl | :UGRD:80 m above ground:anl |
| 466 | 467 | 80496004 | 80780823.0 | 80496004-80780823 | 2020-06-01 | 2020-06-01 | VGRD | 80 m above ground | anl | :VGRD:80 m above ground:anl |
GFS GraphCast#
This demonstrates how to download and plot data from the experimental GFS GraphCast AI model.
[22]:
H = Herbie("2024-05-10 12:00", model="graphcast", fxx=6)
H.SOURCES, H.PRODUCTS
✅ Found ┊ model=graphcast ┊ product=pgrb2.0p25 ┊ 2024-May-10 12:00 UTC F06 ┊ GRIB2 @ aws ┊ IDX @ aws
[22]:
({'aws': 'https://noaa-nws-graphcastgfs-pds.s3.amazonaws.com/graphcastgfs.20240510/12/forecasts_13_levels/graphcastgfs.t12z.pgrb2.0p25.f006'},
{'pgrb2.0p25': 'common fields, 0.25 degree resolution'})
The current version of the GraphCast has a limited set of parameters on 13 different levels.
[23]:
H.inventory()
[23]:
| grib_message | start_byte | end_byte | range | reference_time | valid_time | variable | level | forecast_time | search_this | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3244678.0 | 0-3244678 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VGRD | 50 mb | 6 hour fcst | :VGRD:50 mb:6 hour fcst |
| 1 | 2 | 3244679 | 6489357.0 | 3244679-6489357 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VGRD | 100 mb | 6 hour fcst | :VGRD:100 mb:6 hour fcst |
| 2 | 3 | 6489358 | 9734036.0 | 6489358-9734036 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VGRD | 150 mb | 6 hour fcst | :VGRD:150 mb:6 hour fcst |
| 3 | 4 | 9734037 | 12978715.0 | 9734037-12978715 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VGRD | 200 mb | 6 hour fcst | :VGRD:200 mb:6 hour fcst |
| 4 | 5 | 12978716 | 16223394.0 | 12978716-16223394 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VGRD | 250 mb | 6 hour fcst | :VGRD:250 mb:6 hour fcst |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 79 | 80 | 256329689 | 259574367.0 | 256329689-259574367 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VVEL | 600 mb | 6 hour fcst | :VVEL:600 mb:6 hour fcst |
| 80 | 81 | 259574368 | 262819046.0 | 259574368-262819046 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VVEL | 700 mb | 6 hour fcst | :VVEL:700 mb:6 hour fcst |
| 81 | 82 | 262819047 | 266063725.0 | 262819047-266063725 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VVEL | 850 mb | 6 hour fcst | :VVEL:850 mb:6 hour fcst |
| 82 | 83 | 266063726 | 269308404.0 | 266063726-269308404 | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VVEL | 925 mb | 6 hour fcst | :VVEL:925 mb:6 hour fcst |
| 83 | 84 | 269308405 | NaN | 269308405- | 2024-05-10 12:00:00 | 2024-05-10 18:00:00 | VVEL | 1000 mb | 6 hour fcst | :VVEL:1000 mb:6 hour fcst |
84 rows × 10 columns
Plot 2m temperature
[24]:
ds = H.xarray("TMP:2 m")
👨🏻🏭 Created directory: [/home/blaylock/data/graphcast/20240510]
[25]:
ax = EasyMap(crs=ds.herbie.crs, figsize=[10, 8]).ax
p = ax.pcolormesh(
ds.longitude,
ds.latitude,
ds.t2m - 273.15,
transform=pc,
**paint.NWSTemperature.kwargs2,
)
plt.colorbar(
p, ax=ax, orientation="horizontal", pad=0.05, **paint.NWSTemperature.cbar_kwargs2
)
ax.set_title(ds.t2m.GRIB_name, loc="right")
ax.set_title(f"{ds.model.upper()}: {H.product_description}", loc="left")
[25]:
Text(0.0, 1.0, 'GRAPHCAST: common fields, 0.25 degree resolution')
GFS wave data#
This demonstrates getting data from the GFS wave output.
[26]:
H = Herbie("2021-07-11", model="gfs_wave")
✅ Found ┊ model=gfs_wave ┊ product=arctic.9km ┊ 2021-Jul-11 00:00 UTC F00 ┊ GRIB2 @ aws ┊ IDX @ aws
[27]:
H.inventory()
[27]:
| grib_message | start_byte | end_byte | range | reference_time | valid_time | variable | level | forecast_time | search_this | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 430315.0 | 0-430315 | 2021-07-11 | 2021-07-11 | WIND | surface | anl | :WIND:surface:anl |
| 1 | 2 | 430316 | 1049179.0 | 430316-1049179 | 2021-07-11 | 2021-07-11 | WDIR | surface | anl | :WDIR:surface:anl |
| 2 | 3 | 1049180 | 1480558.0 | 1049180-1480558 | 2021-07-11 | 2021-07-11 | UGRD | surface | anl | :UGRD:surface:anl |
| 3 | 4 | 1480559 | 1908976.0 | 1480559-1908976 | 2021-07-11 | 2021-07-11 | VGRD | surface | anl | :VGRD:surface:anl |
| 4 | 5 | 1908977 | 2174765.0 | 1908977-2174765 | 2021-07-11 | 2021-07-11 | HTSGW | surface | anl | :HTSGW:surface:anl |
| 5 | 6 | 2174766 | 2497195.0 | 2174766-2497195 | 2021-07-11 | 2021-07-11 | PERPW | surface | anl | :PERPW:surface:anl |
| 6 | 7 | 2497196 | 3016884.0 | 2497196-3016884 | 2021-07-11 | 2021-07-11 | DIRPW | surface | anl | :DIRPW:surface:anl |
| 7 | 8 | 3016885 | 3260188.0 | 3016885-3260188 | 2021-07-11 | 2021-07-11 | WVHGT | surface | anl | :WVHGT:surface:anl |
| 8 | 9 | 3260189 | 3529929.0 | 3260189-3529929 | 2021-07-11 | 2021-07-11 | SWELL | 1 in sequence | anl | :SWELL:1 in sequence:anl |
| 9 | 10 | 3529930 | 3764422.0 | 3529930-3764422 | 2021-07-11 | 2021-07-11 | SWELL | 2 in sequence | anl | :SWELL:2 in sequence:anl |
| 10 | 11 | 3764423 | 3968458.0 | 3764423-3968458 | 2021-07-11 | 2021-07-11 | SWELL | 3 in sequence | anl | :SWELL:3 in sequence:anl |
| 11 | 12 | 3968459 | 4247105.0 | 3968459-4247105 | 2021-07-11 | 2021-07-11 | WVPER | surface | anl | :WVPER:surface:anl |
| 12 | 13 | 4247106 | 4558848.0 | 4247106-4558848 | 2021-07-11 | 2021-07-11 | SWPER | 1 in sequence | anl | :SWPER:1 in sequence:anl |
| 13 | 14 | 4558849 | 4873389.0 | 4558849-4873389 | 2021-07-11 | 2021-07-11 | SWPER | 2 in sequence | anl | :SWPER:2 in sequence:anl |
| 14 | 15 | 4873390 | 5158236.0 | 4873390-5158236 | 2021-07-11 | 2021-07-11 | SWPER | 3 in sequence | anl | :SWPER:3 in sequence:anl |
| 15 | 16 | 5158237 | 5558668.0 | 5158237-5558668 | 2021-07-11 | 2021-07-11 | WVDIR | surface | anl | :WVDIR:surface:anl |
| 16 | 17 | 5558669 | 6033914.0 | 5558669-6033914 | 2021-07-11 | 2021-07-11 | SWDIR | 1 in sequence | anl | :SWDIR:1 in sequence:anl |
| 17 | 18 | 6033915 | 6470115.0 | 6033915-6470115 | 2021-07-11 | 2021-07-11 | SWDIR | 2 in sequence | anl | :SWDIR:2 in sequence:anl |
| 18 | 19 | 6470116 | NaN | 6470116- | 2021-07-11 | 2021-07-11 | SWDIR | 3 in sequence | anl | :SWDIR:3 in sequence:anl |
[28]:
ds = H.xarray("SWELL:1 in sequence", remove_grib=False)
ds
👨🏻🏭 Created directory: [/home/blaylock/data/gfs_wave/20210711]
ECCODES ERROR : Polar stereographic Geoiterator: Only supported for spherical earth.
ECCODES ERROR : Geoiterator factory: Error instantiating iterator polar_stereographic (Problem with calculation of geographic attributes)
ECCODES ERROR : latitudes: Unable to create iterator
---------------------------------------------------------------------------
GeocalculusError Traceback (most recent call last)
Cell In[28], line 1
----> 1 ds = H.xarray("SWELL:1 in sequence", remove_grib=False)
2 ds
File ~/GITHUB/Herbie/herbie/core.py:1137, in Herbie.xarray(self, search, searchString, backend_kwargs, remove_grib, **download_kwargs)
1133 backend_kwargs.setdefault("errors", "raise")
1135 # Use cfgrib.open_datasets, just in case there are multiple "hypercubes"
1136 # for what we requested.
-> 1137 Hxr = cfgrib.open_datasets(
1138 local_file,
1139 backend_kwargs=backend_kwargs,
1140 )
1142 # Get CF grid projection information with pygrib and pyproj because
1143 # this is something cfgrib doesn't do (https://github.com/ecmwf/cfgrib/issues/251)
1144 # NOTE: Assumes the projection is the same for all variables
1145 with pygrib.open(str(local_file)) as grb:
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/xarray_store.py:105, in open_datasets(path, backend_kwargs, **kwargs)
103 backend_kwargs = backend_kwargs.copy()
104 backend_kwargs["squeeze"] = False
--> 105 datasets = open_variable_datasets(path, backend_kwargs=backend_kwargs, **kwargs)
107 type_of_level_datasets = {} # type: T.Dict[str, T.List[xr.Dataset]]
108 for ds in datasets:
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/xarray_store.py:93, in open_variable_datasets(path, backend_kwargs, **kwargs)
91 bk["filter_by_keys"] = backend_kwargs.get("filter_by_keys", {}).copy()
92 bk["filter_by_keys"]["paramId"] = param_id
---> 93 datasets.extend(raw_open_datasets(path, bk, **kwargs))
94 return datasets
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/xarray_store.py:66, in raw_open_datasets(path, backend_kwargs, **kwargs)
64 datasets = []
65 try:
---> 66 datasets.append(open_dataset(path, backend_kwargs=backend_kwargs, **kwargs))
67 except DatasetBuildError as ex:
68 fbks.extend(ex.args[2])
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/xarray_store.py:39, in open_dataset(path, **kwargs)
37 raise ValueError("only engine=='cfgrib' is supported")
38 kwargs["engine"] = "cfgrib"
---> 39 return xr.open_dataset(path, **kwargs)
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/xarray/backends/api.py:573, in open_dataset(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)
561 decoders = _resolve_decoders_kwargs(
562 decode_cf,
563 open_backend_dataset_parameters=backend.open_dataset_parameters,
(...)
569 decode_coords=decode_coords,
570 )
572 overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None)
--> 573 backend_ds = backend.open_dataset(
574 filename_or_obj,
575 drop_variables=drop_variables,
576 **decoders,
577 **kwargs,
578 )
579 ds = _dataset_from_backend_dataset(
580 backend_ds,
581 filename_or_obj,
(...)
591 **kwargs,
592 )
593 return ds
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/xarray_plugin.py:109, in CfGribBackend.open_dataset(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, lock, indexpath, filter_by_keys, read_keys, encode_cf, squeeze, time_dims, errors, extra_coords, cache_geo_coords)
87 def open_dataset(
88 self,
89 filename_or_obj: T.Union[str, abc.MappingFieldset[T.Any, abc.Field]],
(...)
107 cache_geo_coords: bool = True,
108 ) -> xr.Dataset:
--> 109 store = CfGribDataStore(
110 filename_or_obj,
111 indexpath=indexpath,
112 filter_by_keys=filter_by_keys,
113 read_keys=read_keys,
114 encode_cf=encode_cf,
115 squeeze=squeeze,
116 time_dims=time_dims,
117 lock=lock,
118 errors=errors,
119 extra_coords=extra_coords,
120 cache_geo_coords=cache_geo_coords,
121 )
122 with xr.core.utils.close_on_error(store):
123 vars, attrs = store.load() # type: ignore
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/xarray_plugin.py:40, in CfGribDataStore.__init__(self, filename, lock, **backend_kwargs)
38 else:
39 opener = dataset.open_fieldset
---> 40 self.ds = opener(filename, **backend_kwargs)
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/dataset.py:800, in open_file(path, errors, indexpath, filter_by_keys, read_keys, time_dims, extra_coords, **kwargs)
797 index_keys = compute_index_keys(time_dims, extra_coords)
798 index = open_fileindex(stream, indexpath, index_keys, filter_by_keys=filter_by_keys)
--> 800 return open_from_index(index, read_keys, time_dims, extra_coords, errors=errors, **kwargs)
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/dataset.py:743, in open_from_index(index, read_keys, time_dims, extra_coords, **kwargs)
736 def open_from_index(
737 index: abc.Index[T.Any, abc.Field],
738 read_keys: T.Sequence[str] = (),
(...)
741 **kwargs: T.Any,
742 ) -> Dataset:
--> 743 dimensions, variables, attributes, encoding = build_dataset_components(
744 index, read_keys=read_keys, time_dims=time_dims, extra_coords=extra_coords, **kwargs
745 )
746 return Dataset(dimensions, variables, attributes, encoding)
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/dataset.py:673, in build_dataset_components(index, errors, encode_cf, squeeze, log, read_keys, time_dims, extra_coords, cache_geo_coords)
671 var_index = index.subindex(paramId=param_id)
672 try:
--> 673 dims, data_var, coord_vars = build_variable_components(
674 var_index,
675 encode_cf,
676 filter_by_keys,
677 errors=errors,
678 squeeze=squeeze,
679 read_keys=read_keys,
680 time_dims=time_dims,
681 extra_coords=extra_coords,
682 cache_geo_coords=cache_geo_coords,
683 )
684 except DatasetBuildError as ex:
685 # NOTE: When a variable has more than one value for an attribute we need to raise all
686 # the values in the file, not just the ones associated with that variable. See #54.
687 key = ex.args[1]
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/dataset.py:543, in build_variable_components(index, encode_cf, filter_by_keys, log, errors, squeeze, read_keys, time_dims, extra_coords, cache_geo_coords)
541 geo_coords = GEOCACHE[cache_key]
542 else:
--> 543 geo_coords = build_geography_coordinates(index.first(), encode_cf, errors)
544 GEOCACHE[cache_key] = geo_coords
545 else:
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/dataset.py:414, in build_geography_coordinates(first, encode_cf, errors, log)
410 geo_shape = (first["Ny"], first["Nx"])
411 try:
412 geo_coord_vars["latitude"] = Variable(
413 dimensions=("y", "x"),
--> 414 data=np.array(first["latitudes"]).reshape(geo_shape),
415 attributes=COORD_ATTRS["latitude"],
416 )
417 geo_coord_vars["longitude"] = Variable(
418 dimensions=("y", "x"),
419 data=np.array(first["longitudes"]).reshape(geo_shape),
420 attributes=COORD_ATTRS["longitude"],
421 )
422 except KeyError: # pragma: no cover
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/messages.py:246, in ComputedKeysAdapter.__getitem__(self, item)
244 return getter(self)
245 else:
--> 246 return self.context[item]
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/messages.py:168, in Message.__getitem__(self, item)
166 raise ValueError("key type not supported %r" % key_type_text)
167 key_type = KEY_TYPES[key_type_text]
--> 168 return self.message_get(key, key_type=key_type)
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/cfgrib/messages.py:131, in Message.message_get(self, item, key_type, default)
129 try:
130 if eccodes.codes_get_size(self.codes_id, item) > 1:
--> 131 values = eccodes.codes_get_array(self.codes_id, item, key_type)
132 else:
133 values = [eccodes.codes_get(self.codes_id, item, key_type)]
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/gribapi/gribapi.py:2028, in grib_get_array(msgid, key, ktype)
2026 result = grib_get_long_array(msgid, key)
2027 elif ktype is float or ktype is np.float64:
-> 2028 result = grib_get_double_array(msgid, key)
2029 elif ktype is np.float32:
2030 result = grib_get_float_array(msgid, key)
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/gribapi/gribapi.py:1196, in grib_get_double_array(msgid, key)
1194 vals_p = ffi.cast("double *", arr.ctypes.data)
1195 err = lib.grib_get_double_array(h, key.encode(ENC), vals_p, length_p)
-> 1196 GRIB_CHECK(err)
1197 return arr
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/gribapi/gribapi.py:226, in GRIB_CHECK(errid)
218 """
219 Utility function checking the ecCodes error code and raising
220 an error if that was set.
(...)
223 @exception CodesInternalError
224 """
225 if errid:
--> 226 errors.raise_grib_error(errid)
File ~/miniconda3/envs/herbie-dev/lib/python3.12/site-packages/gribapi/errors.py:381, in raise_grib_error(errid)
377 def raise_grib_error(errid):
378 """
379 Raise the GribInternalError corresponding to ``errid``.
380 """
--> 381 raise ERROR_MAP[errid](errid)
GeocalculusError: Problem with calculation of geographic attributes