\n",
+ "
<xarray.Dataset>\n",
+ "Dimensions: (x: 14, y: 24, time: 768)\n",
+ "Coordinates:\n",
+ " * x (x) float64 -9.9 -9.6 -9.3 -9.0 ... -6.9 -6.6 -6.3 -6.0\n",
+ " * y (y) float64 36.0 36.3 36.6 36.9 ... 42.0 42.3 42.6 42.9\n",
+ " * time (time) datetime64[ns] 2013-01-01 ... 2013-02-01T23:00:00\n",
+ " lon (x) float64 dask.array<chunksize=(14,), meta=np.ndarray>\n",
+ " lat (y) float64 dask.array<chunksize=(24,), meta=np.ndarray>\n",
+ "Data variables: (12/13)\n",
+ " height (y, x) float32 dask.array<chunksize=(24, 14), meta=np.ndarray>\n",
+ " wnd100m (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " wnd_azimuth (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " roughness (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " influx_toa (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " influx_direct (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " ... ...\n",
+ " albedo (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " solar_altitude (time, y, x) float64 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " solar_azimuth (time, y, x) float64 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " temperature (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " soil temperature (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ " runoff (time, y, x) float32 dask.array<chunksize=(100, 24, 14), meta=np.ndarray>\n",
+ "Attributes:\n",
+ " module: era5\n",
+ " prepared_features: ['height', 'runoff', 'temperature', 'wind', 'influx']\n",
+ " chunksize_time: 100\n",
+ " dx: 0.3\n",
+ " dy: 0.3\n",
+ " Conventions: CF-1.6\n",
+ " history: 2022-03-04 14:08:37 GMT by grib_to_netcdf-2.24.2: /op...
x
(x)
float64
-9.9 -9.6 -9.3 ... -6.6 -6.3 -6.0
array([-9.9, -9.6, -9.3, -9. , -8.7, -8.4, -8.1, -7.8, -7.5, -7.2, -6.9, -6.6,\n",
+ " -6.3, -6. ])
y
(y)
float64
36.0 36.3 36.6 ... 42.3 42.6 42.9
array([36. , 36.3, 36.6, 36.9, 37.2, 37.5, 37.8, 38.1, 38.4, 38.7, 39. , 39.3,\n",
+ " 39.6, 39.9, 40.2, 40.5, 40.8, 41.1, 41.4, 41.7, 42. , 42.3, 42.6, 42.9])
time
(time)
datetime64[ns]
2013-01-01 ... 2013-02-01T23:00:00
array(['2013-01-01T00:00:00.000000000', '2013-01-01T01:00:00.000000000',\n",
+ " '2013-01-01T02:00:00.000000000', ..., '2013-02-01T21:00:00.000000000',\n",
+ " '2013-02-01T22:00:00.000000000', '2013-02-01T23:00:00.000000000'],\n",
+ " dtype='datetime64[ns]')
lon
(x)
float64
dask.array<chunksize=(14,), meta=np.ndarray>
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 112 B | \n",
+ " 112 B | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (14,) | \n",
+ " (14,) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 1 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float64 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
lat
(y)
float64
dask.array<chunksize=(24,), meta=np.ndarray>
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 192 B | \n",
+ " 192 B | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (24,) | \n",
+ " (24,) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 1 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float64 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
height
(y, x)
float32
dask.array<chunksize=(24, 14), meta=np.ndarray>
- module :
- era5
- feature :
- height
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 1.31 kiB | \n",
+ " 1.31 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (24, 14) | \n",
+ " (24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 1 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
wnd100m
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- m s**-1
- long_name :
- 100 metre wind speed
- module :
- era5
- feature :
- wind
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
wnd_azimuth
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- m s**-1
- long_name :
- 100 metre U wind component
- module :
- era5
- feature :
- wind
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
roughness
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- m
- long_name :
- Forecast surface roughness
- module :
- era5
- feature :
- wind
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
influx_toa
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- W m**-2
- module :
- era5
- feature :
- influx
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
influx_direct
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- W m**-2
- module :
- era5
- feature :
- influx
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
influx_diffuse
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- W m**-2
- module :
- era5
- feature :
- influx
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
albedo
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- (0 - 1)
- long_name :
- Albedo
- module :
- era5
- feature :
- influx
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
solar_altitude
(time, y, x)
float64
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- time shift :
- -1 days +23:30:00
- units :
- rad
- module :
- era5
- feature :
- influx
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 1.97 MiB | \n",
+ " 262.50 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float64 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
solar_azimuth
(time, y, x)
float64
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- time shift :
- -1 days +23:30:00
- units :
- rad
- module :
- era5
- feature :
- influx
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 1.97 MiB | \n",
+ " 262.50 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float64 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
temperature
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- K
- long_name :
- 2 metre temperature
- module :
- era5
- feature :
- temperature
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
soil temperature
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- K
- long_name :
- Soil temperature level 4
- module :
- era5
- feature :
- temperature
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
runoff
(time, y, x)
float32
dask.array<chunksize=(100, 24, 14), meta=np.ndarray>
- units :
- m
- long_name :
- Runoff
- module :
- era5
- feature :
- runoff
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " Array | \n",
+ " Chunk | \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Bytes | \n",
+ " 0.98 MiB | \n",
+ " 131.25 kiB | \n",
+ " \n",
+ " \n",
+ " \n",
+ " Shape | \n",
+ " (768, 24, 14) | \n",
+ " (100, 24, 14) | \n",
+ " \n",
+ " \n",
+ " Dask graph | \n",
+ " 8 chunks in 2 graph layers | \n",
+ " \n",
+ " \n",
+ " Data type | \n",
+ " float32 numpy.ndarray | \n",
+ " \n",
+ " \n",
+ " \n",
+ " | \n",
+ " \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
PandasIndex
PandasIndex(Index([-9.9, -9.6, -9.3, -9.0, -8.7, -8.4, -8.1, -7.8, -7.5, -7.2, -6.9, -6.6,\n",
+ " -6.3, -6.0],\n",
+ " dtype='float64', name='x'))
PandasIndex
PandasIndex(Index([36.0, 36.3, 36.6, 36.9, 37.2, 37.5, 37.8, 38.1, 38.4, 38.7, 39.0, 39.3,\n",
+ " 39.6, 39.9, 40.2, 40.5, 40.8, 41.1, 41.4, 41.7, 42.0, 42.3, 42.6, 42.9],\n",
+ " dtype='float64', name='y'))
PandasIndex
PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 01:00:00',\n",
+ " '2013-01-01 02:00:00', '2013-01-01 03:00:00',\n",
+ " '2013-01-01 04:00:00', '2013-01-01 05:00:00',\n",
+ " '2013-01-01 06:00:00', '2013-01-01 07:00:00',\n",
+ " '2013-01-01 08:00:00', '2013-01-01 09:00:00',\n",
+ " ...\n",
+ " '2013-02-01 14:00:00', '2013-02-01 15:00:00',\n",
+ " '2013-02-01 16:00:00', '2013-02-01 17:00:00',\n",
+ " '2013-02-01 18:00:00', '2013-02-01 19:00:00',\n",
+ " '2013-02-01 20:00:00', '2013-02-01 21:00:00',\n",
+ " '2013-02-01 22:00:00', '2013-02-01 23:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=768, freq=None))
- module :
- era5
- prepared_features :
- ['height', 'runoff', 'temperature', 'wind', 'influx']
- chunksize_time :
- 100
- dx :
- 0.3
- dy :
- 0.3
- Conventions :
- CF-1.6
- history :
- 2022-03-04 14:08:37 GMT by grib_to_netcdf-2.24.2: /opt/ecmwf/mars-client/bin/grib_to_netcdf -S param -o /cache/data5/adaptor.mars.internal-1646402916.940792-4467-14-36e42b7d-86c5-45a9-804b-239a490afe28.nc /cache/tmp/36e42b7d-86c5-45a9-804b-239a490afe28-adaptor.mars.internal-1646402913.817388-4467-5-tmp.grib
"
+ ],
+ "text/plain": [
+ "