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TutorialsERA5 land: storm Daniel flood, Greece 2023

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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: ⤵

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:

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

<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

• Dimensions:
  • valid_time: 655992
  • latitude: 1472
  • longitude: 3600
• Coordinates: (6)
  • 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]')

• Data variables: (50)
  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

• Indexes: (3)
  • 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))

• Attributes: (7)

  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

⚠ 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:

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

<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

• Coordinates: (6)
  • 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]')

• Indexes: (3)
  • 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))

• Attributes: (31)

  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.

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

<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

• Coordinates: (6)
  • 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]')

• Indexes: (3)
  • 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))

• Attributes: (31)

  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:

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:

%%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.

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

<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)

• Coordinates: (5)
  • 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.)

• Indexes: (2)
  • 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'))

• Attributes: (31)

  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:

import display
import matplotlib.pyplot as plt
import cartopy.feature as cfeature

from cartopy import crs

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)

<cartopy.mpl.gridliner.Gridliner at 0x7f1d66ab69d0>

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:

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

<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)

• Coordinates: (5)
  • 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.)

• Indexes: (2)
  • 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'))

• Attributes: (31)

  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:

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)

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.

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

<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

• Coordinates: (6)
  • 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]')

• Indexes: (1)
  • 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))

• Attributes: (31)

  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

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

%%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:

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"))

%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.

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});

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