GloFAS 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-reforecast',
            model='lisflood',
            product_type='ensemble_perturbed_reforecasts',
            system_version='version_3_1',
            temporal_filter= '2018 07 01-10',
            leadtime_hour = '24-72',
            variable="river_discharge_in_the_last_24_hours",
            coords= [ps],
            split_on = ['hday'],
            threads = 6
        )

ref.to_xarray()

<xarray.Dataset>
Dimensions:                  (realization: 10, forecast_reference_time: 3,
                              leadtime: 3, lat: 2, lon: 2)
Coordinates:
  * realization              (realization) int64 1 2 3 4 5 6 7 8 9 10
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 2018-07...
  * leadtime                 (leadtime) timedelta64[ns] 1 days 2 days 3 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:                 2023-01-02T10:13 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • realization: 10
  • forecast_reference_time: 3
  • leadtime: 3
  • lat: 2
  • lon: 2
• Coordinates: (6)
  • realization
    (realization)
    int64
    1 2 3 4 5 6 7 8 9 10

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

  • forecast_reference_time
    (forecast_reference_time)
    datetime64[ns]
    2018-07-01 2018-07-04 2018-07-08

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2018-07-01T00:00:00.000000000', '2018-07-04T00:00:00.000000000',
           '2018-07-08T00:00:00.000000000'], dtype='datetime64[ns]')

  • leadtime
    (leadtime)
    timedelta64[ns]
    1 days 2 days 3 days

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([ 86400000000000, 172800000000000, 259200000000000],
          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

    [9 values with dtype=datetime64[ns]]

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

    GRIB_paramId :

    240024

    GRIB_dataType :

    pf

    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 :

    11

    GRIB_units :

    m**3 s**-1

    long_name :

    Mean discharge in the last 24 hours

    units :

    m**3 s**-1

    standard_name :

    unknown

    [360 values with dtype=float32]

• Indexes: (5)
  • realization
    PandasIndex

    PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64', name='realization'))

  • forecast_reference_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-07-01', '2018-07-04', '2018-07-08'], dtype='datetime64[ns]', name='forecast_reference_time', freq=None))

  • leadtime
    PandasIndex

    PandasIndex(TimedeltaIndex(['1 days', '2 days', '3 days'], dtype='timedelta64[ns]', name='leadtime', freq=None))

  • lat
    PandasIndex

    PandasIndex(Float64Index([44.85, 44.95], dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Float64Index([11.55, 11.65], dtype='float64', name='lon'))

• 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 :

  2023-01-02T10:13 GRIB to CDM+CF via cfgrib-0.9.10.3/ecCodes-2.27.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}