How to work with ERA5-Land on Earth Data Hub: storm Daniel flood, Greece 2023¶

Earth Data Hub (EDH) offers an innovative and super-efficient way to access earth related data. This notebook will provide you guidance on how to access and use the https://data.earthdatahub.destine.eu/era5/reanalysis-era5-land-no-antartica-v0.zarr dataset on Earth Data Hub.

Goal of this tutorial¶

The first goal of this tutorial is to compute the total precipitation observed during the Storm Daniel event, from 6 to 7 September 2023, in Greece, and compare it with the average 1993-2022 precipitation in the same area.

The second goal of this tutorial is to compare the 2023 cumulative precipitation on a specific interland location (39.25 N, 21.9 E) with the cumulative 1993-2022 precipitation in the same location.

What you will learn:¶

  • how to access the dataset
  • select and reduce the data
  • plot the results

To access datasets on Earth Data Hub you need to instruct your tools (xarray, Zarr, etc.) to use EDH personal access token when downloading the data.

To obtain a personal access token you first need to register to the Destination Earth platform. Then you can go to Earth Data Hub account settings where you can find your default personal access token or create others. After retrieving your personal access token, please cut and paste it below: ⤵

In [1]:
PAT = "your_personal_access_token"

#e.g. PAT="edh_pat_44bbb7e9192a4c6bb47ddf07d07564eee5d17de8dfc48f7118f88e3bc4a4157f8fe2403f5aa0a2d53441b6922ea9a33a"

Working with EDH data¶

Datasets on Earth Data Hub are often very large and remotely hosted. Typical use imply a selection of the data followed by one or more reduction steps to be performed in a local or distributed Dask environment.

The structure of a workflow that uses EDH data tipically looks like this:

  • data access
  • data selection
  • (optional) data reduction
  • data download
  • further operations and visualization

Xarray and Dask work together following a lazy principle. This means that when you access and manipulate a Zarr store the data is in not immediately downloaded and loaded in memory. Instead, Dask constructs a task graph that represents the operations to be performed. A smart user will first reduce the amount of data that needs to be downloaded and explicitly call compute() on it. Once the compute() operation is complete the data is loaded into memory and available for subsequent fast processing.

1. Data access¶

To access the data, only the dataset metadata must be downloaded. Xarray does this automatically when you access a Zarr dataset:

In [2]:
import xarray as xr

ds = xr.open_dataset(
    f"https://edh:{PAT}@data.earthdatahub.destine.eu/era5/reanalysis-era5-land-no-antartica-v0.zarr",
    chunks={},
    engine="zarr",
).astype("float32")
ds
Out[2]:
<xarray.Dataset> Size: 695TB
Dimensions:              (valid_time: 655992, latitude: 1472, longitude: 3600)
Coordinates:
    depthBelowLandLayer  float32 4B ...
  * latitude             (latitude) float64 12kB 90.0 89.9 89.8 ... -57.0 -57.1
  * longitude            (longitude) float64 29kB 0.0 0.1 0.2 ... 359.8 359.9
    number               int64 8B ...
    surface              float64 8B ...
  * valid_time           (valid_time) datetime64[ns] 5MB 1950-01-01 ... 2024-...
Data variables: (12/50)
    asn                  (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    d2m                  (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    e                    (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    es                   (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    evabs                (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    evaow                (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    ...                   ...
    swvl4                (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    t2m                  (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    tp                   (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    tsn                  (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    u10                  (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
    v10                  (valid_time, latitude, longitude) float32 14TB dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_edition:            1
    GRIB_subCentre:          0
    history:                 2024-10-29T11:44 GRIB to CDM+CF via cfgrib-0.9.1...
    institution:             European Centre for Medium-Range Weather Forecasts
xarray.Dataset
    • valid_time: 655992
    • latitude: 1472
    • longitude: 3600
    • depthBelowLandLayer
      ()
      float32
      ...
      long_name :
      soil depth
      positive :
      down
      standard_name :
      depth
      units :
      m
      [1 values with dtype=float32]
    • latitude
      (latitude)
      float64
      90.0 89.9 89.8 ... -57.0 -57.1
      long_name :
      latitude
      standard_name :
      latitude
      stored_direction :
      decreasing
      units :
      degrees_north
      array([ 90. ,  89.9,  89.8, ..., -56.9, -57. , -57.1])
    • longitude
      (longitude)
      float64
      0.0 0.1 0.2 ... 359.7 359.8 359.9
      long_name :
      longitude
      standard_name :
      longitude
      units :
      degrees_east
      array([0.000e+00, 1.000e-01, 2.000e-01, ..., 3.597e+02, 3.598e+02, 3.599e+02])
    • number
      ()
      int64
      ...
      long_name :
      ensemble member numerical id
      standard_name :
      realization
      units :
      1
      [1 values with dtype=int64]
    • surface
      ()
      float64
      ...
      long_name :
      original GRIB coordinate for key: level(surface)
      units :
      1
      [1 values with dtype=float64]
    • valid_time
      (valid_time)
      datetime64[ns]
      1950-01-01 ... 2024-10-31T23:00:00
      array(['1950-01-01T00:00:00.000000000', '1950-01-01T01:00:00.000000000',
       '1950-01-01T02:00:00.000000000', ..., '2024-10-31T21:00:00.000000000',
       '2024-10-31T22:00:00.000000000', '2024-10-31T23:00:00.000000000'],
      dtype='datetime64[ns]')
    • asn
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      asn
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Snow albedo
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      32
      GRIB_shortName :
      asn
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      (0 - 1)
      last_restart_dim_updated :
      654528
      long_name :
      Snow albedo
      standard_name :
      unknown
      units :
      (0 - 1)
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • d2m
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      d2m
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      2 metre dewpoint temperature
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      168
      GRIB_shortName :
      2d
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      2 metre dewpoint temperature
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • e
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      lwe_thickness_of_water_evaporation_amount
      GRIB_cfVarName :
      e
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Evaporation
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      182
      GRIB_shortName :
      e
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Evaporation
      standard_name :
      lwe_thickness_of_water_evaporation_amount
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • es
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      es
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Snow evaporation
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      44
      GRIB_shortName :
      es
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Snow evaporation
      standard_name :
      unknown
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • evabs
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      evabs
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/longitude
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Evaporation from bare soil
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228101
      GRIB_shortName :
      evabs
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Evaporation from bare soil
      standard_name :
      unknown
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • evaow
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      evaow
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/longitude
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Evaporation from open water surfaces excluding oceans
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228102
      GRIB_shortName :
      evaow
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Evaporation from open water surfaces excluding oceans
      standard_name :
      unknown
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • evatc
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      evatc
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/longitude
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Evaporation from the top of canopy
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228100
      GRIB_shortName :
      evatc
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Evaporation from the top of canopy
      standard_name :
      unknown
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • evavt
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      evavt
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/longitude
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Evaporation from vegetation transpiration
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228103
      GRIB_shortName :
      evavt
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Evaporation from vegetation transpiration
      standard_name :
      unknown
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • fal
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      fal
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Forecast albedo
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      243
      GRIB_shortName :
      fal
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      (0 - 1)
      last_restart_dim_updated :
      654528
      long_name :
      Forecast albedo
      standard_name :
      unknown
      units :
      (0 - 1)
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • lai_hv
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      lai_hv
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Leaf area index, high vegetation
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      67
      GRIB_shortName :
      lai_hv
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m**2 m**-2
      last_restart_dim_updated :
      654528
      long_name :
      Leaf area index, high vegetation
      standard_name :
      unknown
      units :
      m**2 m**-2
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • lai_lv
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      lai_lv
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Leaf area index, low vegetation
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      66
      GRIB_shortName :
      lai_lv
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m**2 m**-2
      last_restart_dim_updated :
      654528
      long_name :
      Leaf area index, low vegetation
      standard_name :
      unknown
      units :
      m**2 m**-2
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • lblt
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      lblt
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Lake bottom temperature
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228010
      GRIB_shortName :
      lblt
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Lake bottom temperature
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • licd
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      licd
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Lake ice total depth
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228014
      GRIB_shortName :
      licd
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m
      last_restart_dim_updated :
      654528
      long_name :
      Lake ice total depth
      standard_name :
      unknown
      units :
      m
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • lict
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      lict
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Lake ice surface temperature
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228013
      GRIB_shortName :
      lict
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Lake ice surface temperature
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • lmld
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      lmld
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Lake mix-layer depth
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228009
      GRIB_shortName :
      lmld
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m
      last_restart_dim_updated :
      654528
      long_name :
      Lake mix-layer depth
      standard_name :
      unknown
      units :
      m
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • lmlt
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      lmlt
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Lake mix-layer temperature
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228008
      GRIB_shortName :
      lmlt
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Lake mix-layer temperature
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • lshf
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      lshf
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Lake shape factor
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228012
      GRIB_shortName :
      lshf
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      dimensionless
      last_restart_dim_updated :
      654528
      long_name :
      Lake shape factor
      standard_name :
      unknown
      units :
      dimensionless
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • ltlt
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      ltlt
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Lake total layer temperature
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228011
      GRIB_shortName :
      ltlt
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Lake total layer temperature
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • pev
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      pev
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Potential evaporation
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228251
      GRIB_shortName :
      pev
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m
      last_restart_dim_updated :
      654528
      long_name :
      Potential evaporation
      standard_name :
      unknown
      units :
      m
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • ro
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      ro
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Runoff
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      205
      GRIB_shortName :
      ro
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m
      last_restart_dim_updated :
      654528
      long_name :
      Runoff
      standard_name :
      unknown
      units :
      m
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • rsn
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      rsn
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Snow density
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      33
      GRIB_shortName :
      rsn
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      kg m**-3
      last_restart_dim_updated :
      654528
      long_name :
      Snow density
      standard_name :
      unknown
      units :
      kg m**-3
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • sd
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      lwe_thickness_of_surface_snow_amount
      GRIB_cfVarName :
      sd
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Snow depth
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      141
      GRIB_shortName :
      sd
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Snow depth
      standard_name :
      lwe_thickness_of_surface_snow_amount
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • sde
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      lwe_thickness_of_surface_snow_amount
      GRIB_cfVarName :
      sde
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/longitude
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Snow depth
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      3066
      GRIB_shortName :
      sde
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m
      last_restart_dim_updated :
      654528
      long_name :
      Snow depth
      standard_name :
      lwe_thickness_of_surface_snow_amount
      units :
      m
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • sf
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      lwe_thickness_of_snowfall_amount
      GRIB_cfVarName :
      sf
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Snowfall
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      144
      GRIB_shortName :
      sf
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Snowfall
      standard_name :
      lwe_thickness_of_snowfall_amount
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • skt
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      skt
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Skin temperature
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      235
      GRIB_shortName :
      skt
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Skin temperature
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • slhf
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      surface_upward_latent_heat_flux
      GRIB_cfVarName :
      slhf
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Surface latent heat flux
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      147
      GRIB_shortName :
      slhf
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      J m**-2
      last_restart_dim_updated :
      654528
      long_name :
      Surface latent heat flux
      standard_name :
      surface_upward_latent_heat_flux
      units :
      J m**-2
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • smlt
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      smlt
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Snowmelt
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      45
      GRIB_shortName :
      smlt
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Snowmelt
      standard_name :
      unknown
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • snowc
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      snowc
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/longitude
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Snow cover
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      260038
      GRIB_shortName :
      snowc
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      %
      last_restart_dim_updated :
      654528
      long_name :
      Snow cover
      standard_name :
      unknown
      units :
      %
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • sp
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      surface_air_pressure
      GRIB_cfVarName :
      sp
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Surface pressure
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      134
      GRIB_shortName :
      sp
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      Pa
      last_restart_dim_updated :
      654528
      long_name :
      Surface pressure
      standard_name :
      surface_air_pressure
      units :
      Pa
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • src
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      src
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Skin reservoir content
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      198
      GRIB_shortName :
      src
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m of water equivalent
      last_restart_dim_updated :
      654528
      long_name :
      Skin reservoir content
      standard_name :
      unknown
      units :
      m of water equivalent
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • sro
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      sro
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Surface runoff
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      8
      GRIB_shortName :
      sro
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m
      last_restart_dim_updated :
      654528
      long_name :
      Surface runoff
      standard_name :
      unknown
      units :
      m
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • sshf
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      surface_upward_sensible_heat_flux
      GRIB_cfVarName :
      sshf
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Surface sensible heat flux
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      146
      GRIB_shortName :
      sshf
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      J m**-2
      last_restart_dim_updated :
      654528
      long_name :
      Surface sensible heat flux
      standard_name :
      surface_upward_sensible_heat_flux
      units :
      J m**-2
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • ssr
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      surface_net_downward_shortwave_flux
      GRIB_cfVarName :
      ssr
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Surface net short-wave (solar) radiation
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      176
      GRIB_shortName :
      ssr
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      J m**-2
      last_restart_dim_updated :
      654528
      long_name :
      Surface net short-wave (solar) radiation
      standard_name :
      surface_net_downward_shortwave_flux
      units :
      J m**-2
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • ssrd
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      surface_downwelling_shortwave_flux_in_air
      GRIB_cfVarName :
      ssrd
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Surface short-wave (solar) radiation downwards
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      169
      GRIB_shortName :
      ssrd
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      J m**-2
      last_restart_dim_updated :
      654528
      long_name :
      Surface short-wave (solar) radiation downwards
      standard_name :
      surface_downwelling_shortwave_flux_in_air
      units :
      J m**-2
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • ssro
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      ssro
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Sub-surface runoff
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      9
      GRIB_shortName :
      ssro
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m
      last_restart_dim_updated :
      654528
      long_name :
      Sub-surface runoff
      standard_name :
      unknown
      units :
      m
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • stl1
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      surface_temperature
      GRIB_cfVarName :
      stl1
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Soil temperature level 1
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      139
      GRIB_shortName :
      stl1
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      depthBelowLandLayer
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Soil temperature level 1
      standard_name :
      surface_temperature
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • stl2
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      stl2
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Soil temperature level 2
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      170
      GRIB_shortName :
      stl2
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      depthBelowLandLayer
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Soil temperature level 2
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • stl3
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      stl3
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Soil temperature level 3
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      183
      GRIB_shortName :
      stl3
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      depthBelowLandLayer
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Soil temperature level 3
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • stl4
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      stl4
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Soil temperature level 4
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      236
      GRIB_shortName :
      stl4
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      depthBelowLandLayer
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Soil temperature level 4
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • str
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      surface_net_upward_longwave_flux
      GRIB_cfVarName :
      str
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Surface net long-wave (thermal) radiation
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      177
      GRIB_shortName :
      str
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      J m**-2
      last_restart_dim_updated :
      654528
      long_name :
      Surface net long-wave (thermal) radiation
      standard_name :
      surface_net_upward_longwave_flux
      units :
      J m**-2
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • strd
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      strd
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Surface long-wave (thermal) radiation downwards
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      175
      GRIB_shortName :
      strd
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      J m**-2
      last_restart_dim_updated :
      654528
      long_name :
      Surface long-wave (thermal) radiation downwards
      standard_name :
      unknown
      units :
      J m**-2
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • swvl1
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      swvl1
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Volumetric soil water layer 1
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      39
      GRIB_shortName :
      swvl1
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      depthBelowLandLayer
      GRIB_units :
      m**3 m**-3
      last_restart_dim_updated :
      654528
      long_name :
      Volumetric soil water layer 1
      standard_name :
      unknown
      units :
      m**3 m**-3
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • swvl2
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      swvl2
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Volumetric soil water layer 2
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      40
      GRIB_shortName :
      swvl2
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      depthBelowLandLayer
      GRIB_units :
      m**3 m**-3
      last_restart_dim_updated :
      654528
      long_name :
      Volumetric soil water layer 2
      standard_name :
      unknown
      units :
      m**3 m**-3
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • swvl3
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      swvl3
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Volumetric soil water layer 3
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      41
      GRIB_shortName :
      swvl3
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      depthBelowLandLayer
      GRIB_units :
      m**3 m**-3
      last_restart_dim_updated :
      654528
      long_name :
      Volumetric soil water layer 3
      standard_name :
      unknown
      units :
      m**3 m**-3
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • swvl4
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      swvl4
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Volumetric soil water layer 4
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      42
      GRIB_shortName :
      swvl4
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      depthBelowLandLayer
      GRIB_units :
      m**3 m**-3
      last_restart_dim_updated :
      654528
      long_name :
      Volumetric soil water layer 4
      standard_name :
      unknown
      units :
      m**3 m**-3
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • t2m
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      t2m
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      2 metre temperature
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      167
      GRIB_shortName :
      2t
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      2 metre temperature
      standard_name :
      unknown
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • tp
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      tp
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Total precipitation
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      228
      GRIB_shortName :
      tp
      GRIB_stepType :
      accum
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m
      last_restart_dim_updated :
      654528
      long_name :
      Total precipitation
      standard_name :
      unknown
      units :
      m
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • tsn
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      temperature_in_surface_snow
      GRIB_cfVarName :
      tsn
      GRIB_dataType :
      an
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      Temperature of snow layer
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      238
      GRIB_shortName :
      tsn
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      K
      last_restart_dim_updated :
      654528
      long_name :
      Temperature of snow layer
      standard_name :
      temperature_in_surface_snow
      units :
      K
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • u10
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      u10
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      10 metre U wind component
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      165
      GRIB_shortName :
      10u
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m s**-1
      last_restart_dim_updated :
      654528
      long_name :
      10 metre U wind component
      standard_name :
      unknown
      units :
      m s**-1
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • v10
      (valid_time, latitude, longitude)
      float32
      dask.array<chunksize=(2880, 64, 64), meta=np.ndarray>
      GRIB_NV :
      0
      GRIB_Nx :
      3600
      GRIB_Ny :
      1472
      GRIB_cfName :
      unknown
      GRIB_cfVarName :
      v10
      GRIB_dataType :
      fc
      GRIB_gridDefinitionDescription :
      Latitude/Longitude Grid
      GRIB_gridType :
      regular_ll
      GRIB_iDirectionIncrementInDegrees :
      0.1
      GRIB_iScansNegatively :
      0
      GRIB_jDirectionIncrementInDegrees :
      0.1
      GRIB_jPointsAreConsecutive :
      0
      GRIB_jScansPositively :
      0
      GRIB_latitudeOfFirstGridPointInDegrees :
      90.0
      GRIB_latitudeOfLastGridPointInDegrees :
      -57.1
      GRIB_longitudeOfFirstGridPointInDegrees :
      0.0
      GRIB_longitudeOfLastGridPointInDegrees :
      359.9
      GRIB_missingValue :
      3.4028234663852886e+38
      GRIB_name :
      10 metre V wind component
      GRIB_numberOfPoints :
      5299200
      GRIB_paramId :
      166
      GRIB_shortName :
      10v
      GRIB_stepType :
      instant
      GRIB_stepUnits :
      1
      GRIB_totalNumber :
      0
      GRIB_typeOfLevel :
      surface
      GRIB_units :
      m s**-1
      last_restart_dim_updated :
      654528
      long_name :
      10 metre V wind component
      standard_name :
      unknown
      units :
      m s**-1
      Array Chunk
      Bytes 12.65 TiB 45.00 MiB
      Shape (655992, 1472, 3600) (2880, 64, 64)
      Dask graph 298908 chunks in 2 graph layers
      Data type float32 numpy.ndarray
      		3600 1472 655992
    • latitude
      PandasIndex
      PandasIndex(Index([               90.0,                89.9,   89.80000000000001,
         89.70000000000002,   89.60000000000002,   89.50000000000003,
         89.40000000000003,   89.30000000000004,   89.20000000000005,
         89.10000000000005,
       ...
        -56.19999999999969,  -56.29999999999969,  -56.39999999999969,
       -56.499999999999694, -56.599999999999696,   -56.6999999999997,
         -56.7999999999997,   -56.8999999999997,   -56.9999999999997,
                     -57.1],
      dtype='float64', name='latitude', length=1472))
    • longitude
      PandasIndex
      PandasIndex(Index([                0.0, 0.09999999999999999, 0.19999999999999998,
                       0.3, 0.39999999999999997, 0.49999999999999994,
                       0.6,                 0.7,  0.7999999999999999,
        0.8999999999999999,
       ...
        359.00000000001313,  359.10000000001315,   359.2000000000132,
         359.3000000000132,   359.4000000000132,  359.50000000001324,
        359.60000000001327,   359.7000000000133,   359.8000000000133,
                     359.9],
      dtype='float64', name='longitude', length=3600))
    • valid_time
      PandasIndex
      PandasIndex(DatetimeIndex(['1950-01-01 00:00:00', '1950-01-01 01:00:00',
               '1950-01-01 02:00:00', '1950-01-01 03:00:00',
               '1950-01-01 04:00:00', '1950-01-01 05:00:00',
               '1950-01-01 06:00:00', '1950-01-01 07:00:00',
               '1950-01-01 08:00:00', '1950-01-01 09:00:00',
               ...
               '2024-10-31 14:00:00', '2024-10-31 15:00:00',
               '2024-10-31 16:00:00', '2024-10-31 17:00:00',
               '2024-10-31 18:00:00', '2024-10-31 19:00:00',
               '2024-10-31 20:00:00', '2024-10-31 21:00:00',
               '2024-10-31 22:00:00', '2024-10-31 23:00:00'],
              dtype='datetime64[ns]', name='valid_time', length=655992, freq=None))
  • Conventions :
    CF-1.7
    GRIB_centre :
    ecmf
    GRIB_centreDescription :
    European Centre for Medium-Range Weather Forecasts
    GRIB_edition :
    1
    GRIB_subCentre :
    0
    history :
    2024-10-29T11:44 GRIB to CDM+CF via cfgrib-0.9.14.1/ecCodes-2.38.0 with {"source": ".xarray-ecmwf-cache/6bddab5961062f8a43d6014732137376.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}
    institution :
    European Centre for Medium-Range Weather Forecasts

âš  At this point, no data has been downloaded yet, nor loaded in memory.

Storm Daniel precipitation (6-7 September 2023) vs average September precipitation (1993-2022)¶

2. Data selection¶

First, we perform a geographical selection corresponding to the Greece area:

In [3]:
xr.set_options(keep_attrs=True)

tp = ds.tp * 1000 # convert to [mm]
tp.attrs["units"] = "mm"
tp_greece = tp.sel(**{"latitude": slice(41, 34), "longitude": slice(19, 28)})
tp_greece
Out[3]:
<xarray.DataArray 'tp' (valid_time: 655992, latitude: 70, longitude: 90)> Size: 17GB
dask.array<getitem, shape=(655992, 70, 90), dtype=float32, chunksize=(2880, 49, 64), chunktype=numpy.ndarray>
Coordinates:
    depthBelowLandLayer  float32 4B ...
  * latitude             (latitude) float64 560B 40.9 40.8 40.7 ... 34.1 34.0
  * longitude            (longitude) float64 720B 19.0 19.1 19.2 ... 27.8 27.9
    number               int64 8B ...
    surface              float64 8B ...
  * valid_time           (valid_time) datetime64[ns] 5MB 1950-01-01 ... 2024-...
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  3600
    GRIB_Ny:                                  1472
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           tp
    GRIB_dataType:                            fc
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               m
    last_restart_dim_updated:                 654528
    long_name:                                Total precipitation
    standard_name:                            unknown
    units:                                    mm
xarray.DataArray
'tp'
  • valid_time: 655992
  • latitude: 70
  • longitude: 90
  • dask.array<chunksize=(2880, 21, 2), meta=np.ndarray>
    Array Chunk
    Bytes 15.40 GiB 34.45 MiB
    Shape (655992, 70, 90) (2880, 49, 64)
    Dask graph 1368 chunks in 4 graph layers
    Data type float32 numpy.ndarray
    		90 70 655992
    • depthBelowLandLayer
      ()
      float32
      ...
      long_name :
      soil depth
      positive :
      down
      standard_name :
      depth
      units :
      m
      [1 values with dtype=float32]
    • latitude
      (latitude)
      float64
      40.9 40.8 40.7 ... 34.2 34.1 34.0
      long_name :
      latitude
      standard_name :
      latitude
      stored_direction :
      decreasing
      units :
      degrees_north
      array([40.9, 40.8, 40.7, 40.6, 40.5, 40.4, 40.3, 40.2, 40.1, 40. , 39.9, 39.8,
       39.7, 39.6, 39.5, 39.4, 39.3, 39.2, 39.1, 39. , 38.9, 38.8, 38.7, 38.6,
       38.5, 38.4, 38.3, 38.2, 38.1, 38. , 37.9, 37.8, 37.7, 37.6, 37.5, 37.4,
       37.3, 37.2, 37.1, 37. , 36.9, 36.8, 36.7, 36.6, 36.5, 36.4, 36.3, 36.2,
       36.1, 36. , 35.9, 35.8, 35.7, 35.6, 35.5, 35.4, 35.3, 35.2, 35.1, 35. ,
       34.9, 34.8, 34.7, 34.6, 34.5, 34.4, 34.3, 34.2, 34.1, 34. ])
    • longitude
      (longitude)
      float64
      19.0 19.1 19.2 ... 27.7 27.8 27.9
      long_name :
      longitude
      standard_name :
      longitude
      units :
      degrees_east
      array([19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8, 19.9, 20. , 20.1,
       20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9, 21. , 21.1, 21.2, 21.3,
       21.4, 21.5, 21.6, 21.7, 21.8, 21.9, 22. , 22.1, 22.2, 22.3, 22.4, 22.5,
       22.6, 22.7, 22.8, 22.9, 23. , 23.1, 23.2, 23.3, 23.4, 23.5, 23.6, 23.7,
       23.8, 23.9, 24. , 24.1, 24.2, 24.3, 24.4, 24.5, 24.6, 24.7, 24.8, 24.9,
       25. , 25.1, 25.2, 25.3, 25.4, 25.5, 25.6, 25.7, 25.8, 25.9, 26. , 26.1,
       26.2, 26.3, 26.4, 26.5, 26.6, 26.7, 26.8, 26.9, 27. , 27.1, 27.2, 27.3,
       27.4, 27.5, 27.6, 27.7, 27.8, 27.9])
    • number
      ()
      int64
      ...
      long_name :
      ensemble member numerical id
      standard_name :
      realization
      units :
      1
      [1 values with dtype=int64]
    • surface
      ()
      float64
      ...
      long_name :
      original GRIB coordinate for key: level(surface)
      units :
      1
      [1 values with dtype=float64]
    • valid_time
      (valid_time)
      datetime64[ns]
      1950-01-01 ... 2024-10-31T23:00:00
      array(['1950-01-01T00:00:00.000000000', '1950-01-01T01:00:00.000000000',
       '1950-01-01T02:00:00.000000000', ..., '2024-10-31T21:00:00.000000000',
       '2024-10-31T22:00:00.000000000', '2024-10-31T23:00:00.000000000'],
      dtype='datetime64[ns]')
    • latitude
      PandasIndex
      PandasIndex(Index([ 40.90000000000115,  40.80000000000115,  40.70000000000115,
       40.600000000001145, 40.500000000001144,  40.40000000000114,
        40.30000000000114,  40.20000000000114,  40.10000000000114,
        40.00000000000114, 39.900000000001135, 39.800000000001134,
        39.70000000000113,  39.60000000000113,  39.50000000000113,
        39.40000000000113,  39.30000000000113, 39.200000000001125,
       39.100000000001124,  39.00000000000112,  38.90000000000112,
        38.80000000000112,  38.70000000000112,  38.60000000000112,
       38.500000000001116, 38.400000000001114,  38.30000000000111,
        38.20000000000111,  38.10000000000111,  38.00000000000111,
        37.90000000000111, 37.800000000001106, 37.700000000001104,
         37.6000000000011,   37.5000000000011,   37.4000000000011,
         37.3000000000011,   37.2000000000011, 37.100000000001096,
       37.000000000001094,  36.90000000000109,  36.80000000000109,
        36.70000000000109,  36.60000000000109,  36.50000000000109,
       36.400000000001086, 36.300000000001084,  36.20000000000108,
        36.10000000000108,  36.00000000000108,  35.90000000000108,
        35.80000000000108, 35.700000000001076, 35.600000000001074,
        35.50000000000107,  35.40000000000107,  35.30000000000107,
        35.20000000000107,  35.10000000000107, 35.000000000001066,
       34.900000000001064,  34.80000000000106,  34.70000000000106,
        34.60000000000106,  34.50000000000106,  34.40000000000106,
       34.300000000001056, 34.200000000001054,  34.10000000000105,
        34.00000000000105],
      dtype='float64', name='latitude'))
    • longitude
      PandasIndex
      PandasIndex(Index([              19.0,               19.1, 19.200000000000003,
       19.300000000000004, 19.400000000000006, 19.500000000000007,
        19.60000000000001,  19.70000000000001,  19.80000000000001,
       19.900000000000013, 20.000000000000014, 20.100000000000016,
       20.200000000000017,  20.30000000000002,  20.40000000000002,
        20.50000000000002, 20.600000000000023, 20.700000000000024,
       20.800000000000026, 20.900000000000027,  21.00000000000003,
        21.10000000000003,  21.20000000000003, 21.300000000000033,
       21.400000000000034, 21.500000000000036, 21.600000000000037,
        21.70000000000004,  21.80000000000004,  21.90000000000004,
       22.000000000000043, 22.100000000000044, 22.200000000000045,
       22.300000000000047,  22.40000000000005,  22.50000000000005,
        22.60000000000005, 22.700000000000053, 22.800000000000054,
       22.900000000000055, 23.000000000000057,  23.10000000000006,
        23.20000000000006,  23.30000000000006, 23.400000000000063,
       23.500000000000064, 23.600000000000065, 23.700000000000067,
       23.800000000000068,  23.90000000000007,  24.00000000000007,
       24.100000000000072, 24.200000000000074, 24.300000000000075,
       24.400000000000077, 24.500000000000078,  24.60000000000008,
        24.70000000000008, 24.800000000000082, 24.900000000000084,
       25.000000000000085, 25.100000000000087, 25.200000000000088,
        25.30000000000009,  25.40000000000009, 25.500000000000092,
       25.600000000000094, 25.700000000000095, 25.800000000000097,
       25.900000000000098,   26.0000000000001,   26.1000000000001,
       26.200000000000102, 26.300000000000104, 26.400000000000105,
       26.500000000000107, 26.600000000000108,  26.70000000000011,
        26.80000000000011, 26.900000000000112, 27.000000000000114,
       27.100000000000115, 27.200000000000117, 27.300000000000118,
        27.40000000000012,  27.50000000000012, 27.600000000000122,
       27.700000000000124, 27.800000000000125, 27.900000000000126],
      dtype='float64', name='longitude'))
    • valid_time
      PandasIndex
      PandasIndex(DatetimeIndex(['1950-01-01 00:00:00', '1950-01-01 01:00:00',
               '1950-01-01 02:00:00', '1950-01-01 03:00:00',
               '1950-01-01 04:00:00', '1950-01-01 05:00:00',
               '1950-01-01 06:00:00', '1950-01-01 07:00:00',
               '1950-01-01 08:00:00', '1950-01-01 09:00:00',
               ...
               '2024-10-31 14:00:00', '2024-10-31 15:00:00',
               '2024-10-31 16:00:00', '2024-10-31 17:00:00',
               '2024-10-31 18:00:00', '2024-10-31 19:00:00',
               '2024-10-31 20:00:00', '2024-10-31 21:00:00',
               '2024-10-31 22:00:00', '2024-10-31 23:00:00'],
              dtype='datetime64[ns]', name='valid_time', length=655992, freq=None))
  • GRIB_NV :
    0
    GRIB_Nx :
    3600
    GRIB_Ny :
    1472
    GRIB_cfName :
    unknown
    GRIB_cfVarName :
    tp
    GRIB_dataType :
    fc
    GRIB_gridDefinitionDescription :
    Latitude/Longitude Grid
    GRIB_gridType :
    regular_ll
    GRIB_iDirectionIncrementInDegrees :
    0.1
    GRIB_iScansNegatively :
    0
    GRIB_jDirectionIncrementInDegrees :
    0.1
    GRIB_jPointsAreConsecutive :
    0
    GRIB_jScansPositively :
    0
    GRIB_latitudeOfFirstGridPointInDegrees :
    90.0
    GRIB_latitudeOfLastGridPointInDegrees :
    -57.1
    GRIB_longitudeOfFirstGridPointInDegrees :
    0.0
    GRIB_longitudeOfLastGridPointInDegrees :
    359.9
    GRIB_missingValue :
    3.4028234663852886e+38
    GRIB_name :
    Total precipitation
    GRIB_numberOfPoints :
    5299200
    GRIB_paramId :
    228
    GRIB_shortName :
    tp
    GRIB_stepType :
    accum
    GRIB_stepUnits :
    1
    GRIB_totalNumber :
    0
    GRIB_typeOfLevel :
    surface
    GRIB_units :
    m
    last_restart_dim_updated :
    654528
    long_name :
    Total precipitation
    standard_name :
    unknown
    units :
    mm

Then, we select only the data belonging to the month of September for the 1993-2023 time period (30 years). We will use this to extract the Storm Daniel event and the 1993-2022 Semptember mean.

In [4]:
tp_greece_september = tp_greece[tp_greece.valid_time.dt.month.isin([9])]
tp_greece_september_1993_2023 = tp_greece_september.sel(valid_time=slice('1993', '2023'))
tp_greece_september_1993_2023
Out[4]:
<xarray.DataArray 'tp' (valid_time: 22320, latitude: 70, longitude: 90)> Size: 562MB
dask.array<getitem, shape=(22320, 70, 90), dtype=float32, chunksize=(2877, 49, 64), chunktype=numpy.ndarray>
Coordinates:
    depthBelowLandLayer  float32 4B ...
  * latitude             (latitude) float64 560B 40.9 40.8 40.7 ... 34.1 34.0
  * longitude            (longitude) float64 720B 19.0 19.1 19.2 ... 27.8 27.9
    number               int64 8B ...
    surface              float64 8B ...
  * valid_time           (valid_time) datetime64[ns] 179kB 1993-09-01 ... 202...
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  3600
    GRIB_Ny:                                  1472
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           tp
    GRIB_dataType:                            fc
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               m
    last_restart_dim_updated:                 654528
    long_name:                                Total precipitation
    standard_name:                            unknown
    units:                                    mm
xarray.DataArray
'tp'
  • valid_time: 22320
  • latitude: 70
  • longitude: 90
  • dask.array<chunksize=(687, 21, 2), meta=np.ndarray>
    Array Chunk
    Bytes 536.41 MiB 34.42 MiB
    Shape (22320, 70, 90) (2877, 49, 64)
    Dask graph 54 chunks in 6 graph layers
    Data type float32 numpy.ndarray
    		90 70 22320
    • depthBelowLandLayer
      ()
      float32
      ...
      long_name :
      soil depth
      positive :
      down
      standard_name :
      depth
      units :
      m
      [1 values with dtype=float32]
    • latitude
      (latitude)
      float64
      40.9 40.8 40.7 ... 34.2 34.1 34.0
      long_name :
      latitude
      standard_name :
      latitude
      stored_direction :
      decreasing
      units :
      degrees_north
      array([40.9, 40.8, 40.7, 40.6, 40.5, 40.4, 40.3, 40.2, 40.1, 40. , 39.9, 39.8,
       39.7, 39.6, 39.5, 39.4, 39.3, 39.2, 39.1, 39. , 38.9, 38.8, 38.7, 38.6,
       38.5, 38.4, 38.3, 38.2, 38.1, 38. , 37.9, 37.8, 37.7, 37.6, 37.5, 37.4,
       37.3, 37.2, 37.1, 37. , 36.9, 36.8, 36.7, 36.6, 36.5, 36.4, 36.3, 36.2,
       36.1, 36. , 35.9, 35.8, 35.7, 35.6, 35.5, 35.4, 35.3, 35.2, 35.1, 35. ,
       34.9, 34.8, 34.7, 34.6, 34.5, 34.4, 34.3, 34.2, 34.1, 34. ])
    • longitude
      (longitude)
      float64
      19.0 19.1 19.2 ... 27.7 27.8 27.9
      long_name :
      longitude
      standard_name :
      longitude
      units :
      degrees_east
      array([19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8, 19.9, 20. , 20.1,
       20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9, 21. , 21.1, 21.2, 21.3,
       21.4, 21.5, 21.6, 21.7, 21.8, 21.9, 22. , 22.1, 22.2, 22.3, 22.4, 22.5,
       22.6, 22.7, 22.8, 22.9, 23. , 23.1, 23.2, 23.3, 23.4, 23.5, 23.6, 23.7,
       23.8, 23.9, 24. , 24.1, 24.2, 24.3, 24.4, 24.5, 24.6, 24.7, 24.8, 24.9,
       25. , 25.1, 25.2, 25.3, 25.4, 25.5, 25.6, 25.7, 25.8, 25.9, 26. , 26.1,
       26.2, 26.3, 26.4, 26.5, 26.6, 26.7, 26.8, 26.9, 27. , 27.1, 27.2, 27.3,
       27.4, 27.5, 27.6, 27.7, 27.8, 27.9])
    • number
      ()
      int64
      ...
      long_name :
      ensemble member numerical id
      standard_name :
      realization
      units :
      1
      [1 values with dtype=int64]
    • surface
      ()
      float64
      ...
      long_name :
      original GRIB coordinate for key: level(surface)
      units :
      1
      [1 values with dtype=float64]
    • valid_time
      (valid_time)
      datetime64[ns]
      1993-09-01 ... 2023-09-30T23:00:00
      array(['1993-09-01T00:00:00.000000000', '1993-09-01T01:00:00.000000000',
       '1993-09-01T02:00:00.000000000', ..., '2023-09-30T21:00:00.000000000',
       '2023-09-30T22:00:00.000000000', '2023-09-30T23:00:00.000000000'],
      dtype='datetime64[ns]')
    • latitude
      PandasIndex
      PandasIndex(Index([ 40.90000000000115,  40.80000000000115,  40.70000000000115,
       40.600000000001145, 40.500000000001144,  40.40000000000114,
        40.30000000000114,  40.20000000000114,  40.10000000000114,
        40.00000000000114, 39.900000000001135, 39.800000000001134,
        39.70000000000113,  39.60000000000113,  39.50000000000113,
        39.40000000000113,  39.30000000000113, 39.200000000001125,
       39.100000000001124,  39.00000000000112,  38.90000000000112,
        38.80000000000112,  38.70000000000112,  38.60000000000112,
       38.500000000001116, 38.400000000001114,  38.30000000000111,
        38.20000000000111,  38.10000000000111,  38.00000000000111,
        37.90000000000111, 37.800000000001106, 37.700000000001104,
         37.6000000000011,   37.5000000000011,   37.4000000000011,
         37.3000000000011,   37.2000000000011, 37.100000000001096,
       37.000000000001094,  36.90000000000109,  36.80000000000109,
        36.70000000000109,  36.60000000000109,  36.50000000000109,
       36.400000000001086, 36.300000000001084,  36.20000000000108,
        36.10000000000108,  36.00000000000108,  35.90000000000108,
        35.80000000000108, 35.700000000001076, 35.600000000001074,
        35.50000000000107,  35.40000000000107,  35.30000000000107,
        35.20000000000107,  35.10000000000107, 35.000000000001066,
       34.900000000001064,  34.80000000000106,  34.70000000000106,
        34.60000000000106,  34.50000000000106,  34.40000000000106,
       34.300000000001056, 34.200000000001054,  34.10000000000105,
        34.00000000000105],
      dtype='float64', name='latitude'))
    • longitude
      PandasIndex
      PandasIndex(Index([              19.0,               19.1, 19.200000000000003,
       19.300000000000004, 19.400000000000006, 19.500000000000007,
        19.60000000000001,  19.70000000000001,  19.80000000000001,
       19.900000000000013, 20.000000000000014, 20.100000000000016,
       20.200000000000017,  20.30000000000002,  20.40000000000002,
        20.50000000000002, 20.600000000000023, 20.700000000000024,
       20.800000000000026, 20.900000000000027,  21.00000000000003,
        21.10000000000003,  21.20000000000003, 21.300000000000033,
       21.400000000000034, 21.500000000000036, 21.600000000000037,
        21.70000000000004,  21.80000000000004,  21.90000000000004,
       22.000000000000043, 22.100000000000044, 22.200000000000045,
       22.300000000000047,  22.40000000000005,  22.50000000000005,
        22.60000000000005, 22.700000000000053, 22.800000000000054,
       22.900000000000055, 23.000000000000057,  23.10000000000006,
        23.20000000000006,  23.30000000000006, 23.400000000000063,
       23.500000000000064, 23.600000000000065, 23.700000000000067,
       23.800000000000068,  23.90000000000007,  24.00000000000007,
       24.100000000000072, 24.200000000000074, 24.300000000000075,
       24.400000000000077, 24.500000000000078,  24.60000000000008,
        24.70000000000008, 24.800000000000082, 24.900000000000084,
       25.000000000000085, 25.100000000000087, 25.200000000000088,
        25.30000000000009,  25.40000000000009, 25.500000000000092,
       25.600000000000094, 25.700000000000095, 25.800000000000097,
       25.900000000000098,   26.0000000000001,   26.1000000000001,
       26.200000000000102, 26.300000000000104, 26.400000000000105,
       26.500000000000107, 26.600000000000108,  26.70000000000011,
        26.80000000000011, 26.900000000000112, 27.000000000000114,
       27.100000000000115, 27.200000000000117, 27.300000000000118,
        27.40000000000012,  27.50000000000012, 27.600000000000122,
       27.700000000000124, 27.800000000000125, 27.900000000000126],
      dtype='float64', name='longitude'))
    • valid_time
      PandasIndex
      PandasIndex(DatetimeIndex(['1993-09-01 00:00:00', '1993-09-01 01:00:00',
               '1993-09-01 02:00:00', '1993-09-01 03:00:00',
               '1993-09-01 04:00:00', '1993-09-01 05:00:00',
               '1993-09-01 06:00:00', '1993-09-01 07:00:00',
               '1993-09-01 08:00:00', '1993-09-01 09:00:00',
               ...
               '2023-09-30 14:00:00', '2023-09-30 15:00:00',
               '2023-09-30 16:00:00', '2023-09-30 17:00:00',
               '2023-09-30 18:00:00', '2023-09-30 19:00:00',
               '2023-09-30 20:00:00', '2023-09-30 21:00:00',
               '2023-09-30 22:00:00', '2023-09-30 23:00:00'],
              dtype='datetime64[ns]', name='valid_time', length=22320, freq=None))
  • GRIB_NV :
    0
    GRIB_Nx :
    3600
    GRIB_Ny :
    1472
    GRIB_cfName :
    unknown
    GRIB_cfVarName :
    tp
    GRIB_dataType :
    fc
    GRIB_gridDefinitionDescription :
    Latitude/Longitude Grid
    GRIB_gridType :
    regular_ll
    GRIB_iDirectionIncrementInDegrees :
    0.1
    GRIB_iScansNegatively :
    0
    GRIB_jDirectionIncrementInDegrees :
    0.1
    GRIB_jPointsAreConsecutive :
    0
    GRIB_jScansPositively :
    0
    GRIB_latitudeOfFirstGridPointInDegrees :
    90.0
    GRIB_latitudeOfLastGridPointInDegrees :
    -57.1
    GRIB_longitudeOfFirstGridPointInDegrees :
    0.0
    GRIB_longitudeOfLastGridPointInDegrees :
    359.9
    GRIB_missingValue :
    3.4028234663852886e+38
    GRIB_name :
    Total precipitation
    GRIB_numberOfPoints :
    5299200
    GRIB_paramId :
    228
    GRIB_shortName :
    tp
    GRIB_stepType :
    accum
    GRIB_stepUnits :
    1
    GRIB_totalNumber :
    0
    GRIB_typeOfLevel :
    surface
    GRIB_units :
    m
    last_restart_dim_updated :
    654528
    long_name :
    Total precipitation
    standard_name :
    unknown
    units :
    mm

At this point, the data should be small enough to fit in memory. We can check the size of it with the costing.py module:

In [5]:
import costing

costing.estimate_download_size(tp, tp_greece_september_1993_2023)

estimated_needed_chunks: 54
estimated_memory_size: 2.548 GB
estimated_download_size: 0.255 GB

4. Data download¶

This is the phase where we explicitly trigger the download of the data. Remember to assign the return of the compute() function to a new variable, so that the data is kept in memory.

We can measure the time it takes:

In [6]:
%%time

tp_greece_september_1993_2023_computed = tp_greece_september_1993_2023.compute()

CPU times: user 20.6 s, sys: 13.7 s, total: 34.3 s
Wall time: 17.5 s

3. Data reduction¶

With the data loaded in memory we can easily compute the total precipitation for the Storm Daniel event. Notice that for each day of the year the total daily precipitation is given at T00:00:00.

In [7]:
tp_greece_storm_daniel = tp_greece_september_1993_2023_computed.sel(valid_time=["2023-09-06", "2023-09-07"]).sum("valid_time") # only takes the data at T00:00:00 timestamps
tp_greece_storm_daniel
Out[7]:
<xarray.DataArray 'tp' (latitude: 70, longitude: 90)> Size: 25kB
array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)
Coordinates:
    depthBelowLandLayer  float32 4B 100.0
  * latitude             (latitude) float64 560B 40.9 40.8 40.7 ... 34.1 34.0
  * longitude            (longitude) float64 720B 19.0 19.1 19.2 ... 27.8 27.9
    number               int64 8B 0
    surface              float64 8B 0.0
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  3600
    GRIB_Ny:                                  1472
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           tp
    GRIB_dataType:                            fc
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               m
    last_restart_dim_updated:                 654528
    long_name:                                Total precipitation
    standard_name:                            unknown
    units:                                    mm
xarray.DataArray
'tp'
  • latitude: 70
  • longitude: 90
  • 0.0 0.0 0.0 0.0 0.05838 0.126 0.2575 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
    array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)
    • depthBelowLandLayer
      ()
      float32
      100.0
      long_name :
      soil depth
      positive :
      down
      standard_name :
      depth
      units :
      m
      array(100., dtype=float32)
    • latitude
      (latitude)
      float64
      40.9 40.8 40.7 ... 34.2 34.1 34.0
      long_name :
      latitude
      standard_name :
      latitude
      stored_direction :
      decreasing
      units :
      degrees_north
      array([40.9, 40.8, 40.7, 40.6, 40.5, 40.4, 40.3, 40.2, 40.1, 40. , 39.9, 39.8,
       39.7, 39.6, 39.5, 39.4, 39.3, 39.2, 39.1, 39. , 38.9, 38.8, 38.7, 38.6,
       38.5, 38.4, 38.3, 38.2, 38.1, 38. , 37.9, 37.8, 37.7, 37.6, 37.5, 37.4,
       37.3, 37.2, 37.1, 37. , 36.9, 36.8, 36.7, 36.6, 36.5, 36.4, 36.3, 36.2,
       36.1, 36. , 35.9, 35.8, 35.7, 35.6, 35.5, 35.4, 35.3, 35.2, 35.1, 35. ,
       34.9, 34.8, 34.7, 34.6, 34.5, 34.4, 34.3, 34.2, 34.1, 34. ])
    • longitude
      (longitude)
      float64
      19.0 19.1 19.2 ... 27.7 27.8 27.9
      long_name :
      longitude
      standard_name :
      longitude
      units :
      degrees_east
      array([19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8, 19.9, 20. , 20.1,
       20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9, 21. , 21.1, 21.2, 21.3,
       21.4, 21.5, 21.6, 21.7, 21.8, 21.9, 22. , 22.1, 22.2, 22.3, 22.4, 22.5,
       22.6, 22.7, 22.8, 22.9, 23. , 23.1, 23.2, 23.3, 23.4, 23.5, 23.6, 23.7,
       23.8, 23.9, 24. , 24.1, 24.2, 24.3, 24.4, 24.5, 24.6, 24.7, 24.8, 24.9,
       25. , 25.1, 25.2, 25.3, 25.4, 25.5, 25.6, 25.7, 25.8, 25.9, 26. , 26.1,
       26.2, 26.3, 26.4, 26.5, 26.6, 26.7, 26.8, 26.9, 27. , 27.1, 27.2, 27.3,
       27.4, 27.5, 27.6, 27.7, 27.8, 27.9])
    • number
      ()
      int64
      0
      long_name :
      ensemble member numerical id
      standard_name :
      realization
      units :
      1
      array(0)
    • surface
      ()
      float64
      0.0
      long_name :
      original GRIB coordinate for key: level(surface)
      units :
      1
      array(0.)
    • latitude
      PandasIndex
      PandasIndex(Index([ 40.90000000000115,  40.80000000000115,  40.70000000000115,
       40.600000000001145, 40.500000000001144,  40.40000000000114,
        40.30000000000114,  40.20000000000114,  40.10000000000114,
        40.00000000000114, 39.900000000001135, 39.800000000001134,
        39.70000000000113,  39.60000000000113,  39.50000000000113,
        39.40000000000113,  39.30000000000113, 39.200000000001125,
       39.100000000001124,  39.00000000000112,  38.90000000000112,
        38.80000000000112,  38.70000000000112,  38.60000000000112,
       38.500000000001116, 38.400000000001114,  38.30000000000111,
        38.20000000000111,  38.10000000000111,  38.00000000000111,
        37.90000000000111, 37.800000000001106, 37.700000000001104,
         37.6000000000011,   37.5000000000011,   37.4000000000011,
         37.3000000000011,   37.2000000000011, 37.100000000001096,
       37.000000000001094,  36.90000000000109,  36.80000000000109,
        36.70000000000109,  36.60000000000109,  36.50000000000109,
       36.400000000001086, 36.300000000001084,  36.20000000000108,
        36.10000000000108,  36.00000000000108,  35.90000000000108,
        35.80000000000108, 35.700000000001076, 35.600000000001074,
        35.50000000000107,  35.40000000000107,  35.30000000000107,
        35.20000000000107,  35.10000000000107, 35.000000000001066,
       34.900000000001064,  34.80000000000106,  34.70000000000106,
        34.60000000000106,  34.50000000000106,  34.40000000000106,
       34.300000000001056, 34.200000000001054,  34.10000000000105,
        34.00000000000105],
      dtype='float64', name='latitude'))
    • longitude
      PandasIndex
      PandasIndex(Index([              19.0,               19.1, 19.200000000000003,
       19.300000000000004, 19.400000000000006, 19.500000000000007,
        19.60000000000001,  19.70000000000001,  19.80000000000001,
       19.900000000000013, 20.000000000000014, 20.100000000000016,
       20.200000000000017,  20.30000000000002,  20.40000000000002,
        20.50000000000002, 20.600000000000023, 20.700000000000024,
       20.800000000000026, 20.900000000000027,  21.00000000000003,
        21.10000000000003,  21.20000000000003, 21.300000000000033,
       21.400000000000034, 21.500000000000036, 21.600000000000037,
        21.70000000000004,  21.80000000000004,  21.90000000000004,
       22.000000000000043, 22.100000000000044, 22.200000000000045,
       22.300000000000047,  22.40000000000005,  22.50000000000005,
        22.60000000000005, 22.700000000000053, 22.800000000000054,
       22.900000000000055, 23.000000000000057,  23.10000000000006,
        23.20000000000006,  23.30000000000006, 23.400000000000063,
       23.500000000000064, 23.600000000000065, 23.700000000000067,
       23.800000000000068,  23.90000000000007,  24.00000000000007,
       24.100000000000072, 24.200000000000074, 24.300000000000075,
       24.400000000000077, 24.500000000000078,  24.60000000000008,
        24.70000000000008, 24.800000000000082, 24.900000000000084,
       25.000000000000085, 25.100000000000087, 25.200000000000088,
        25.30000000000009,  25.40000000000009, 25.500000000000092,
       25.600000000000094, 25.700000000000095, 25.800000000000097,
       25.900000000000098,   26.0000000000001,   26.1000000000001,
       26.200000000000102, 26.300000000000104, 26.400000000000105,
       26.500000000000107, 26.600000000000108,  26.70000000000011,
        26.80000000000011, 26.900000000000112, 27.000000000000114,
       27.100000000000115, 27.200000000000117, 27.300000000000118,
        27.40000000000012,  27.50000000000012, 27.600000000000122,
       27.700000000000124, 27.800000000000125, 27.900000000000126],
      dtype='float64', name='longitude'))
  • GRIB_NV :
    0
    GRIB_Nx :
    3600
    GRIB_Ny :
    1472
    GRIB_cfName :
    unknown
    GRIB_cfVarName :
    tp
    GRIB_dataType :
    fc
    GRIB_gridDefinitionDescription :
    Latitude/Longitude Grid
    GRIB_gridType :
    regular_ll
    GRIB_iDirectionIncrementInDegrees :
    0.1
    GRIB_iScansNegatively :
    0
    GRIB_jDirectionIncrementInDegrees :
    0.1
    GRIB_jPointsAreConsecutive :
    0
    GRIB_jScansPositively :
    0
    GRIB_latitudeOfFirstGridPointInDegrees :
    90.0
    GRIB_latitudeOfLastGridPointInDegrees :
    -57.1
    GRIB_longitudeOfFirstGridPointInDegrees :
    0.0
    GRIB_longitudeOfLastGridPointInDegrees :
    359.9
    GRIB_missingValue :
    3.4028234663852886e+38
    GRIB_name :
    Total precipitation
    GRIB_numberOfPoints :
    5299200
    GRIB_paramId :
    228
    GRIB_shortName :
    tp
    GRIB_stepType :
    accum
    GRIB_stepUnits :
    1
    GRIB_totalNumber :
    0
    GRIB_typeOfLevel :
    surface
    GRIB_units :
    m
    last_restart_dim_updated :
    654528
    long_name :
    Total precipitation
    standard_name :
    unknown
    units :
    mm

5. Visualization¶

Finally, we can plot the Storm Daniel event on a map:

In [8]:
import display
import matplotlib.pyplot as plt
import cartopy.feature as cfeature

from cartopy import crs
In [9]:
plt.style.use("bmh")

ax = display.map(
    tp_greece_storm_daniel, 
    projection=crs.Miller(), 
    vmax=400, 
    title="Storm Daniel total precipitation, 6-7 September 2023"
);

ax.gridlines(draw_labels=True, alpha = 0.2)
Out[9]:
<cartopy.mpl.gridliner.Gridliner at 0x7f1d66ab69d0>
No description has been provided for this image

We want to compare the total precipitation observed during Storm Daniel with the average total precipitation observed in September between 1993 and 2023 (30 years). To do this we compute the 1993_2022 september mean:

In [10]:
tp_greece_september_1993_2022_mean = tp_greece_september_1993_2023_computed[tp_greece_september_1993_2023_computed['valid_time'].dt.hour == 0].sel(valid_time=slice("1993", "2022")).sum("valid_time")/30
tp_greece_september_1993_2022_mean
Out[10]:
<xarray.DataArray 'tp' (latitude: 70, longitude: 90)> Size: 25kB
array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)
Coordinates:
    depthBelowLandLayer  float32 4B 100.0
  * latitude             (latitude) float64 560B 40.9 40.8 40.7 ... 34.1 34.0
  * longitude            (longitude) float64 720B 19.0 19.1 19.2 ... 27.8 27.9
    number               int64 8B 0
    surface              float64 8B 0.0
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  3600
    GRIB_Ny:                                  1472
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           tp
    GRIB_dataType:                            fc
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               m
    last_restart_dim_updated:                 654528
    long_name:                                Total precipitation
    standard_name:                            unknown
    units:                                    mm
xarray.DataArray
'tp'
  • latitude: 70
  • longitude: 90
  • 0.0 0.0 0.0 0.0 88.14 86.63 84.66 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
    array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)
    • depthBelowLandLayer
      ()
      float32
      100.0
      long_name :
      soil depth
      positive :
      down
      standard_name :
      depth
      units :
      m
      array(100., dtype=float32)
    • latitude
      (latitude)
      float64
      40.9 40.8 40.7 ... 34.2 34.1 34.0
      long_name :
      latitude
      standard_name :
      latitude
      stored_direction :
      decreasing
      units :
      degrees_north
      array([40.9, 40.8, 40.7, 40.6, 40.5, 40.4, 40.3, 40.2, 40.1, 40. , 39.9, 39.8,
       39.7, 39.6, 39.5, 39.4, 39.3, 39.2, 39.1, 39. , 38.9, 38.8, 38.7, 38.6,
       38.5, 38.4, 38.3, 38.2, 38.1, 38. , 37.9, 37.8, 37.7, 37.6, 37.5, 37.4,
       37.3, 37.2, 37.1, 37. , 36.9, 36.8, 36.7, 36.6, 36.5, 36.4, 36.3, 36.2,
       36.1, 36. , 35.9, 35.8, 35.7, 35.6, 35.5, 35.4, 35.3, 35.2, 35.1, 35. ,
       34.9, 34.8, 34.7, 34.6, 34.5, 34.4, 34.3, 34.2, 34.1, 34. ])
    • longitude
      (longitude)
      float64
      19.0 19.1 19.2 ... 27.7 27.8 27.9
      long_name :
      longitude
      standard_name :
      longitude
      units :
      degrees_east
      array([19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8, 19.9, 20. , 20.1,
       20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9, 21. , 21.1, 21.2, 21.3,
       21.4, 21.5, 21.6, 21.7, 21.8, 21.9, 22. , 22.1, 22.2, 22.3, 22.4, 22.5,
       22.6, 22.7, 22.8, 22.9, 23. , 23.1, 23.2, 23.3, 23.4, 23.5, 23.6, 23.7,
       23.8, 23.9, 24. , 24.1, 24.2, 24.3, 24.4, 24.5, 24.6, 24.7, 24.8, 24.9,
       25. , 25.1, 25.2, 25.3, 25.4, 25.5, 25.6, 25.7, 25.8, 25.9, 26. , 26.1,
       26.2, 26.3, 26.4, 26.5, 26.6, 26.7, 26.8, 26.9, 27. , 27.1, 27.2, 27.3,
       27.4, 27.5, 27.6, 27.7, 27.8, 27.9])
    • number
      ()
      int64
      0
      long_name :
      ensemble member numerical id
      standard_name :
      realization
      units :
      1
      array(0)
    • surface
      ()
      float64
      0.0
      long_name :
      original GRIB coordinate for key: level(surface)
      units :
      1
      array(0.)
    • latitude
      PandasIndex
      PandasIndex(Index([ 40.90000000000115,  40.80000000000115,  40.70000000000115,
       40.600000000001145, 40.500000000001144,  40.40000000000114,
        40.30000000000114,  40.20000000000114,  40.10000000000114,
        40.00000000000114, 39.900000000001135, 39.800000000001134,
        39.70000000000113,  39.60000000000113,  39.50000000000113,
        39.40000000000113,  39.30000000000113, 39.200000000001125,
       39.100000000001124,  39.00000000000112,  38.90000000000112,
        38.80000000000112,  38.70000000000112,  38.60000000000112,
       38.500000000001116, 38.400000000001114,  38.30000000000111,
        38.20000000000111,  38.10000000000111,  38.00000000000111,
        37.90000000000111, 37.800000000001106, 37.700000000001104,
         37.6000000000011,   37.5000000000011,   37.4000000000011,
         37.3000000000011,   37.2000000000011, 37.100000000001096,
       37.000000000001094,  36.90000000000109,  36.80000000000109,
        36.70000000000109,  36.60000000000109,  36.50000000000109,
       36.400000000001086, 36.300000000001084,  36.20000000000108,
        36.10000000000108,  36.00000000000108,  35.90000000000108,
        35.80000000000108, 35.700000000001076, 35.600000000001074,
        35.50000000000107,  35.40000000000107,  35.30000000000107,
        35.20000000000107,  35.10000000000107, 35.000000000001066,
       34.900000000001064,  34.80000000000106,  34.70000000000106,
        34.60000000000106,  34.50000000000106,  34.40000000000106,
       34.300000000001056, 34.200000000001054,  34.10000000000105,
        34.00000000000105],
      dtype='float64', name='latitude'))
    • longitude
      PandasIndex
      PandasIndex(Index([              19.0,               19.1, 19.200000000000003,
       19.300000000000004, 19.400000000000006, 19.500000000000007,
        19.60000000000001,  19.70000000000001,  19.80000000000001,
       19.900000000000013, 20.000000000000014, 20.100000000000016,
       20.200000000000017,  20.30000000000002,  20.40000000000002,
        20.50000000000002, 20.600000000000023, 20.700000000000024,
       20.800000000000026, 20.900000000000027,  21.00000000000003,
        21.10000000000003,  21.20000000000003, 21.300000000000033,
       21.400000000000034, 21.500000000000036, 21.600000000000037,
        21.70000000000004,  21.80000000000004,  21.90000000000004,
       22.000000000000043, 22.100000000000044, 22.200000000000045,
       22.300000000000047,  22.40000000000005,  22.50000000000005,
        22.60000000000005, 22.700000000000053, 22.800000000000054,
       22.900000000000055, 23.000000000000057,  23.10000000000006,
        23.20000000000006,  23.30000000000006, 23.400000000000063,
       23.500000000000064, 23.600000000000065, 23.700000000000067,
       23.800000000000068,  23.90000000000007,  24.00000000000007,
       24.100000000000072, 24.200000000000074, 24.300000000000075,
       24.400000000000077, 24.500000000000078,  24.60000000000008,
        24.70000000000008, 24.800000000000082, 24.900000000000084,
       25.000000000000085, 25.100000000000087, 25.200000000000088,
        25.30000000000009,  25.40000000000009, 25.500000000000092,
       25.600000000000094, 25.700000000000095, 25.800000000000097,
       25.900000000000098,   26.0000000000001,   26.1000000000001,
       26.200000000000102, 26.300000000000104, 26.400000000000105,
       26.500000000000107, 26.600000000000108,  26.70000000000011,
        26.80000000000011, 26.900000000000112, 27.000000000000114,
       27.100000000000115, 27.200000000000117, 27.300000000000118,
        27.40000000000012,  27.50000000000012, 27.600000000000122,
       27.700000000000124, 27.800000000000125, 27.900000000000126],
      dtype='float64', name='longitude'))
  • GRIB_NV :
    0
    GRIB_Nx :
    3600
    GRIB_Ny :
    1472
    GRIB_cfName :
    unknown
    GRIB_cfVarName :
    tp
    GRIB_dataType :
    fc
    GRIB_gridDefinitionDescription :
    Latitude/Longitude Grid
    GRIB_gridType :
    regular_ll
    GRIB_iDirectionIncrementInDegrees :
    0.1
    GRIB_iScansNegatively :
    0
    GRIB_jDirectionIncrementInDegrees :
    0.1
    GRIB_jPointsAreConsecutive :
    0
    GRIB_jScansPositively :
    0
    GRIB_latitudeOfFirstGridPointInDegrees :
    90.0
    GRIB_latitudeOfLastGridPointInDegrees :
    -57.1
    GRIB_longitudeOfFirstGridPointInDegrees :
    0.0
    GRIB_longitudeOfLastGridPointInDegrees :
    359.9
    GRIB_missingValue :
    3.4028234663852886e+38
    GRIB_name :
    Total precipitation
    GRIB_numberOfPoints :
    5299200
    GRIB_paramId :
    228
    GRIB_shortName :
    tp
    GRIB_stepType :
    accum
    GRIB_stepUnits :
    1
    GRIB_totalNumber :
    0
    GRIB_typeOfLevel :
    surface
    GRIB_units :
    m
    last_restart_dim_updated :
    654528
    long_name :
    Total precipitation
    standard_name :
    unknown
    units :
    mm

Finally, we can plot the Storm Daniel event and the average September 1993-2022 precipitation side by side:

In [11]:
axs = display.maps(
    [tp_greece_storm_daniel, tp_greece_september_1993_2022_mean],
    projection=crs.Miller(),
    vmax=400,
    axs_set=[
        {"title": "Storm Daniel precipitation, 6-7 September 2023"},
        {"title": "Average September precipitation, 1993-2022"},
    ],
)

for ax in axs:
    ax.gridlines(draw_labels=True, alpha = 0.2)
No description has been provided for this image

Accumulated precipitation: 2023 vs 1993-2022¶

In this section we will compare the accumulated 2023 precipitation on an specific inland location (39.25 N, 21.9 E) with the accumulated precipitation of each year between 1993 and 2022 in the same location. We will also highlight the 1993-2022 accumulated mean. We follow the same logic as before for selecting the data, estimating the download cost, computing the selection and plotting the result.

In [12]:
tp_greece_inland_1993_2023 = ds.tp.sel(**{"latitude": 39.25, "longitude": 21.90, "method": "nearest"}).sel(valid_time=slice("1993", "2023"))
tp_greece_inland_1993_2023
Out[12]:
<xarray.DataArray 'tp' (valid_time: 271728)> Size: 1MB
dask.array<getitem, shape=(271728,), dtype=float32, chunksize=(2880,), chunktype=numpy.ndarray>
Coordinates:
    depthBelowLandLayer  float32 4B ...
    latitude             float64 8B 39.2
    longitude            float64 8B 21.9
    number               int64 8B ...
    surface              float64 8B ...
  * valid_time           (valid_time) datetime64[ns] 2MB 1993-01-01 ... 2023-...
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  3600
    GRIB_Ny:                                  1472
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           tp
    GRIB_dataType:                            fc
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               m
    last_restart_dim_updated:                 654528
    long_name:                                Total precipitation
    standard_name:                            unknown
    units:                                    m
xarray.DataArray
'tp'
  • valid_time: 271728
  • dask.array<chunksize=(336,), meta=np.ndarray>
    Array Chunk
    Bytes 1.04 MiB 11.25 kiB
    Shape (271728,) (2880,)
    Dask graph 96 chunks in 4 graph layers
    Data type float32 numpy.ndarray
    		271728 1
    • depthBelowLandLayer
      ()
      float32
      ...
      long_name :
      soil depth
      positive :
      down
      standard_name :
      depth
      units :
      m
      [1 values with dtype=float32]
    • latitude
      ()
      float64
      39.2
      long_name :
      latitude
      standard_name :
      latitude
      stored_direction :
      decreasing
      units :
      degrees_north
      array(39.2)
    • longitude
      ()
      float64
      21.9
      long_name :
      longitude
      standard_name :
      longitude
      units :
      degrees_east
      array(21.9)
    • number
      ()
      int64
      ...
      long_name :
      ensemble member numerical id
      standard_name :
      realization
      units :
      1
      [1 values with dtype=int64]
    • surface
      ()
      float64
      ...
      long_name :
      original GRIB coordinate for key: level(surface)
      units :
      1
      [1 values with dtype=float64]
    • valid_time
      (valid_time)
      datetime64[ns]
      1993-01-01 ... 2023-12-31T23:00:00
      array(['1993-01-01T00:00:00.000000000', '1993-01-01T01:00:00.000000000',
       '1993-01-01T02:00:00.000000000', ..., '2023-12-31T21:00:00.000000000',
       '2023-12-31T22:00:00.000000000', '2023-12-31T23:00:00.000000000'],
      dtype='datetime64[ns]')
    • valid_time
      PandasIndex
      PandasIndex(DatetimeIndex(['1993-01-01 00:00:00', '1993-01-01 01:00:00',
               '1993-01-01 02:00:00', '1993-01-01 03:00:00',
               '1993-01-01 04:00:00', '1993-01-01 05:00:00',
               '1993-01-01 06:00:00', '1993-01-01 07:00:00',
               '1993-01-01 08:00:00', '1993-01-01 09:00:00',
               ...
               '2023-12-31 14:00:00', '2023-12-31 15:00:00',
               '2023-12-31 16:00:00', '2023-12-31 17:00:00',
               '2023-12-31 18:00:00', '2023-12-31 19:00:00',
               '2023-12-31 20:00:00', '2023-12-31 21:00:00',
               '2023-12-31 22:00:00', '2023-12-31 23:00:00'],
              dtype='datetime64[ns]', name='valid_time', length=271728, freq=None))
  • GRIB_NV :
    0
    GRIB_Nx :
    3600
    GRIB_Ny :
    1472
    GRIB_cfName :
    unknown
    GRIB_cfVarName :
    tp
    GRIB_dataType :
    fc
    GRIB_gridDefinitionDescription :
    Latitude/Longitude Grid
    GRIB_gridType :
    regular_ll
    GRIB_iDirectionIncrementInDegrees :
    0.1
    GRIB_iScansNegatively :
    0
    GRIB_jDirectionIncrementInDegrees :
    0.1
    GRIB_jPointsAreConsecutive :
    0
    GRIB_jScansPositively :
    0
    GRIB_latitudeOfFirstGridPointInDegrees :
    90.0
    GRIB_latitudeOfLastGridPointInDegrees :
    -57.1
    GRIB_longitudeOfFirstGridPointInDegrees :
    0.0
    GRIB_longitudeOfLastGridPointInDegrees :
    359.9
    GRIB_missingValue :
    3.4028234663852886e+38
    GRIB_name :
    Total precipitation
    GRIB_numberOfPoints :
    5299200
    GRIB_paramId :
    228
    GRIB_shortName :
    tp
    GRIB_stepType :
    accum
    GRIB_stepUnits :
    1
    GRIB_totalNumber :
    0
    GRIB_typeOfLevel :
    surface
    GRIB_units :
    m
    last_restart_dim_updated :
    654528
    long_name :
    Total precipitation
    standard_name :
    unknown
    units :
    m
In [13]:
import costing

costing.estimate_download_size(tp, tp_greece_inland_1993_2023)

estimated_needed_chunks: 96
estimated_memory_size: 4.53 GB
estimated_download_size: 0.453 GB
In [14]:
%%time

tp_greece_inland_1993_2023_computed = tp_greece_inland_1993_2023.compute()

CPU times: user 9.99 s, sys: 5.08 s, total: 15.1 s
Wall time: 11 s

Now that the data is loaded in memory, we can easily extreact the precipitation values for each year:

In [15]:
import datetime
tp_greece_inland_2023 = tp_greece_inland_1993_2023_computed[tp_greece_inland_1993_2023_computed['valid_time'].dt.hour == 0].sel(valid_time="2023")
tp_greece_inland_1993_2022 = tp_greece_inland_1993_2023_computed[tp_greece_inland_1993_2023_computed['valid_time'].dt.hour == 0].sel(valid_time=slice("1993", "2022"))
In [16]:
%load_ext autoreload
%autoreload 2

Using the display.compare() method we can plot the accumulated precipitation for all the years between 1993 and 2023, highlighting the $10^{th}$, $50^{th}$, $90^{th}$ percentiles and the 2023 data.

In [17]:
plt.style.use('fivethirtyeight')
ax = display.compare(
    tp_greece_inland_2023, 
    tp_greece_inland_1993_2022, 
    time="valid_time", 
    ylim=[-100, 1750],
)
ax.set_title('Accumulated precipitation [mm], Greece (39.25 N, 21.90 E)',  loc='left', fontsize = 14)
plt.legend(['10$^{th}$ percetile', '90$^{th}$ percetile', '50$^{th}$ percetile', '2023'],  prop={'size': 10});
No description has been provided for this image