GloFAS seasonal reforecast

import climetlab as cml
import climetlab_cems_flood as cmf

ps = {'name':'pontelagoscuro','lat':44.886111, 'lon':11.604444}

ref = cml.load_dataset(
            'cems-glofas-seasonal-reforecast',
            model='lisflood',
            system_version='version_3_1',
            temporal_filter= '2019 07-11 01',
            leadtime_hour = '24-240',
            variable="river_discharge_in_the_last_24_hours",
            coords= [ps],
            split_on = ['hmonth'],
            threads = 6
        )

By downloading data from this dataset, you agree to the terms and conditions defined at https://github.com/ecmwf-lab/climetlab_cems_flood/LICENSEIf you do not agree with such terms, do not download the data. 

ref.to_xarray()

<xarray.Dataset>
Dimensions:                  (realization: 51, forecast_reference_time: 5,
                              leadtime: 10, lat: 2, lon: 2)
Coordinates:
  * realization              (realization) int64 0 1 2 3 4 5 ... 46 47 48 49 50
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 2019-07...
  * leadtime                 (leadtime) timedelta64[ns] 1 days ... 10 days
  * lat                      (lat) float64 44.85 44.95
  * lon                      (lon) float64 11.55 11.65
    time                     (forecast_reference_time, leadtime) datetime64[ns] ...
Data variables:
    dis24                    (realization, forecast_reference_time, leadtime, lat, lon) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 2023-01-02T10:14 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • realization: 51
  • forecast_reference_time: 5
  • leadtime: 10
  • lat: 2
  • lon: 2
• Coordinates: (6)
  • realization
    (realization)
    int64
    0 1 2 3 4 5 6 ... 45 46 47 48 49 50

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])

  • forecast_reference_time
    (forecast_reference_time)
    datetime64[ns]
    2019-07-01 ... 2019-11-01

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2019-07-01T00:00:00.000000000', '2019-08-01T00:00:00.000000000',
           '2019-09-01T00:00:00.000000000', '2019-10-01T00:00:00.000000000',
           '2019-11-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • leadtime
    (leadtime)
    timedelta64[ns]
    1 days 2 days ... 9 days 10 days

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([ 86400000000000, 172800000000000, 259200000000000, 345600000000000,
           432000000000000, 518400000000000, 604800000000000, 691200000000000,
           777600000000000, 864000000000000], dtype='timedelta64[ns]')

  • lat
    (lat)
    float64
    44.85 44.95

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([44.85, 44.95])

  • lon
    (lon)
    float64
    11.55 11.65

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([11.55, 11.65])

  • time
    (forecast_reference_time, leadtime)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    array([['2019-07-02T00:00:00.000000000', '2019-07-03T00:00:00.000000000',
            '2019-07-04T00:00:00.000000000', '2019-07-05T00:00:00.000000000',
            '2019-07-06T00:00:00.000000000', '2019-07-07T00:00:00.000000000',
            '2019-07-08T00:00:00.000000000', '2019-07-09T00:00:00.000000000',
            '2019-07-10T00:00:00.000000000', '2019-07-11T00:00:00.000000000'],
           ['2019-08-02T00:00:00.000000000', '2019-08-03T00:00:00.000000000',
            '2019-08-04T00:00:00.000000000', '2019-08-05T00:00:00.000000000',
            '2019-08-06T00:00:00.000000000', '2019-08-07T00:00:00.000000000',
            '2019-08-08T00:00:00.000000000', '2019-08-09T00:00:00.000000000',
            '2019-08-10T00:00:00.000000000', '2019-08-11T00:00:00.000000000'],
           ['2019-09-02T00:00:00.000000000', '2019-09-03T00:00:00.000000000',
            '2019-09-04T00:00:00.000000000', '2019-09-05T00:00:00.000000000',
            '2019-09-06T00:00:00.000000000', '2019-09-07T00:00:00.000000000',
            '2019-09-08T00:00:00.000000000', '2019-09-09T00:00:00.000000000',
            '2019-09-10T00:00:00.000000000', '2019-09-11T00:00:00.000000000'],
           ['2019-10-02T00:00:00.000000000', '2019-10-03T00:00:00.000000000',
            '2019-10-04T00:00:00.000000000', '2019-10-05T00:00:00.000000000',
            '2019-10-06T00:00:00.000000000', '2019-10-07T00:00:00.000000000',
            '2019-10-08T00:00:00.000000000', '2019-10-09T00:00:00.000000000',
            '2019-10-10T00:00:00.000000000', '2019-10-11T00:00:00.000000000'],
           ['2019-11-02T00:00:00.000000000', '2019-11-03T00:00:00.000000000',
            '2019-11-04T00:00:00.000000000', '2019-11-05T00:00:00.000000000',
            '2019-11-06T00:00:00.000000000', '2019-11-07T00:00:00.000000000',
            '2019-11-08T00:00:00.000000000', '2019-11-09T00:00:00.000000000',
            '2019-11-10T00:00:00.000000000', '2019-11-11T00:00:00.000000000']],
          dtype='datetime64[ns]')

• Data variables: (1)
  • dis24
    (realization, forecast_reference_time, leadtime, lat, lon)
    float32
    ...

    GRIB_paramId :

    240024

    GRIB_dataType :

    fc

    GRIB_numberOfPoints :

    4

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    avg

    GRIB_gridType :

    regular_ll

    GRIB_NV :

    0

    GRIB_Nx :

    2

    GRIB_Ny :

    2

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    dis24

    GRIB_gridDefinitionDescription :

    Latitude/longitude

    GRIB_iDirectionIncrementInDegrees :

    0.1

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.1

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    44.95

    GRIB_latitudeOfLastGridPointInDegrees :

    44.85

    GRIB_longitudeOfFirstGridPointInDegrees :

    11.55

    GRIB_longitudeOfLastGridPointInDegrees :

    11.65

    GRIB_missingValue :

    9999

    GRIB_name :

    Mean discharge in the last 24 hours

    GRIB_shortName :

    dis24

    GRIB_totalNumber :

    0

    GRIB_units :

    m**3 s**-1

    long_name :

    Mean discharge in the last 24 hours

    units :

    m**3 s**-1

    standard_name :

    unknown

    array([[[[[5.937500e-01, 3.354594e+03],
              [3.334844e+03, 4.093750e+00]],
    
             ...,
    
             [[5.625000e-01, 2.358719e+03],
              [2.339000e+03, 4.968750e+00]]],
    
    
            ...,
    
    
            [[[1.031250e+00, 3.640820e+03],
              [3.603805e+03, 6.382812e+00]],
    
             ...,
    
             [[2.937500e+00, 3.215180e+03],
              [3.181086e+03, 1.666406e+01]]]],
    
    
    
           ...,
    
    
    
           [[[[5.937500e-01, 3.354531e+03],
              [3.334781e+03, 4.093750e+00]],
    
             ...,
    
             [[2.125000e+00, 2.675219e+03],
              [2.747281e+03, 2.028125e+01]]],
    
    
            ...,
    
    
            [[[1.031250e+00, 3.640820e+03],
              [3.603805e+03, 6.382812e+00]],
    
             ...,
    
             [[4.203125e+00, 3.574148e+03],
              [3.599305e+03, 4.510156e+01]]]]], dtype=float32)

• Attributes: (7)

  GRIB_edition :

  2

  GRIB_centre :

  ecmf

  GRIB_centreDescription :

  European Centre for Medium-Range Weather Forecasts

  GRIB_subCentre :

  0

  Conventions :

  CF-1.7

  institution :

  European Centre for Medium-Range Weather Forecasts

  history :

  2023-01-02T10:14 GRIB to CDM+CF via cfgrib-0.9.10.1/ecCodes-2.26.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}