SMAP_L4_SM_gph_2018
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/SMAP_L4_SM_gph_2018.yaml")
ds=cat.netcdf.read()Metadata
| title | SMAP_L4_SM_gph_2018 |
| location | /shared/land/SMAP/SPL4SMGP.005/daily |
| tags | SMAP,soil_moisture |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/SMAP_L4_SM_gph_2018.yaml |
| last updated | 2022-03-09 |
Dataset Contents
<xarray.Dataset>
Dimensions: (time: 365, x: 3856, y: 1624)
Coordinates:
* x (x) float64 -1.736e+07 ... 1.736e+07
* y (y) float64 7.31e+06 ... -7.31e+06
* time (time) datetime64[ns] 2018-01-01T01:28...
Data variables:
EASE2_global_projection (time) |S1 b'' b'' b'' ... b'' b'' b''
cell_column (time, y, x) float64 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
cell_lat (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
cell_lon (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
cell_row (time, y, x) float64 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
baseflow_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
heat_flux_ground (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
heat_flux_latent (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
heat_flux_sensible (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
height_lowatmmodlay (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
land_evapotranspiration_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
land_fraction_saturated (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
land_fraction_snow_covered (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
land_fraction_unsaturated (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
land_fraction_wilting (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
leaf_area_index (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
net_downward_longwave_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
net_downward_shortwave_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
overland_runoff_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
precipitation_total_surface_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
radiation_longwave_absorbed_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
radiation_shortwave_downward_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
sm_profile (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
sm_profile_pctl (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
sm_profile_wetness (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
sm_rootzone (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
sm_rootzone_pctl (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
sm_rootzone_wetness (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
sm_surface (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
sm_surface_wetness (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
snow_depth (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
snow_mass (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
snow_melt_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
snowfall_surface_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
soil_temp_layer1 (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
soil_temp_layer2 (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
soil_temp_layer3 (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
soil_temp_layer4 (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
soil_temp_layer5 (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
soil_temp_layer6 (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
soil_water_infiltration_flux (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
specific_humidity_lowatmmodlay (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
surface_pressure (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
surface_temp (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
temp_lowatmmodlay (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
vegetation_greenness_fraction (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
windspeed_lowatmmodlay (time, y, x) float32 dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>xarray.Dataset
- time: 365
- x: 3856
- y: 1624
- x(x)float64-1.736e+07 -1.735e+07 ... 1.736e+07
- long_name :
- X coordinate of cell center in EASE 2.0 global projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- units :
- m
array([-17363027.29248 , -17354019.236816, -17345011.181152, ..., 17345011.181152, 17354019.236816, 17363027.29248 ]) - y(y)float647.31e+06 7.301e+06 ... -7.31e+06
- long_name :
- Y coordinate of cell center in EASE 2.0 global projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- units :
- m
array([ 7310037.171387, 7301029.115723, 7292021.060059, ..., -7292021.060059, -7301029.115723, -7310037.171387]) - time(time)datetime64[ns]2018-01-01T01:28:55.816000 ... 2...
- long_name :
- Time
- delta_t :
- 0000-00-00 03:00:00
array(['2018-01-01T01:28:55.816000000', '2018-01-02T01:28:55.816000000', '2018-01-03T01:28:55.816000000', ..., '2018-12-29T01:28:55.816000000', '2018-12-30T01:28:55.816000000', '2018-12-31T01:28:55.816000000'], dtype='datetime64[ns]')
- EASE2_global_projection(time)|S1b'' b'' b'' b'' ... b'' b'' b'' b''
- grid_mapping_name :
- lambert_cylindrical_equal_area
- standard_parallel :
- 30.0
- false_easting :
- 0.0
- false_northing :
- 0.0
- longitude_of_central_meridian :
- 0.0
array([b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], dtype='|S1') - cell_column(time, y, x)float64dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
- long_name :
- The column index of each cell in the cylindrical 9 km Earth-fixed EASE-Grid 2.0. Type is Unsigned32.
- valid_max :
- 3855
- fmissing_value :
- 4294967294
- DIMENSION_LABELS :
- ['yGwj.\x7f', 'xHwj.\x7f']
- units :
- dimensionless
- grid_mapping :
- EASE2_global_projection
- coordinates :
- /cell_lat /cell_lon
- valid_min :
- 0
Array Chunk Bytes 18.29 GB 50.10 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float64 numpy.ndarray - cell_lat(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
- long_name :
- The geodetic latitude of the center of each cell in the cylindrical 9 km Earth-fixed EASE-Grid 2.0. Zero latitude represents the Equator. Positive latitudes represent locations North of the Equator. Negative latitudes represent locations South of the Equator.
- valid_max :
- 90.0
- fmissing_value :
- -9999.0
- DIMENSION_LABELS :
- ['yHwj.\x7f', 'xGwj.\x7f']
- units :
- degrees
- grid_mapping :
- EASE2_global_projection
- valid_min :
- -90.0
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - cell_lon(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
- long_name :
- The longitude of the center of each cell in the cylindrical 9 km Earth-fixed EASE-Grid 2.0. Zero longitude represents the Prime Meridian. Positive longitudes represent locations to the East of the Prime Meridian. Negative longitudes represent locations to the West of the Prime Meridian.
- valid_max :
- 179.999
- fmissing_value :
- -9999.0
- DIMENSION_LABELS :
- ['yGwj.\x7f', 'xHwj.\x7f']
- units :
- degrees
- grid_mapping :
- EASE2_global_projection
- valid_min :
- -180.0
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - cell_row(time, y, x)float64dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
- long_name :
- The row index of each cell in the cylindrical 9 km Earth-fixed EASE-Grid 2.0. Type is Unsigned32.
- valid_max :
- 1623
- fmissing_value :
- 4294967294
- DIMENSION_LABELS :
- ['yGwj.\x7f', 'xHwj.\x7f']
- units :
- dimensionless
- grid_mapping :
- EASE2_global_projection
- coordinates :
- /cell_lat /cell_lon
- valid_min :
- 0
Array Chunk Bytes 18.29 GB 50.10 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float64 numpy.ndarray - baseflow_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - heat_flux_ground(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - heat_flux_latent(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - heat_flux_sensible(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - height_lowatmmodlay(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - land_evapotranspiration_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - land_fraction_saturated(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - land_fraction_snow_covered(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - land_fraction_unsaturated(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - land_fraction_wilting(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - leaf_area_index(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - net_downward_longwave_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - net_downward_shortwave_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - overland_runoff_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - precipitation_total_surface_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - radiation_longwave_absorbed_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - radiation_shortwave_downward_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - sm_profile(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - sm_profile_pctl(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - sm_profile_wetness(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - sm_rootzone(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - sm_rootzone_pctl(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - sm_rootzone_wetness(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - sm_surface(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - sm_surface_wetness(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - snow_depth(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - snow_mass(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - snow_melt_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - snowfall_surface_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - soil_temp_layer1(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - soil_temp_layer2(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - soil_temp_layer3(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - soil_temp_layer4(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - soil_temp_layer5(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - soil_temp_layer6(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - soil_water_infiltration_flux(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - specific_humidity_lowatmmodlay(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - surface_pressure(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - surface_temp(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - temp_lowatmmodlay(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - vegetation_greenness_fraction(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray - windspeed_lowatmmodlay(time, y, x)float32dask.array<chunksize=(1, 1624, 3856), meta=np.ndarray>
Array Chunk Bytes 9.14 GB 25.05 MB Shape (365, 1624, 3856) (1, 1624, 3856) Count 1460 Tasks 365 Chunks Type float32 numpy.ndarray