Xarray engine: seasonal forecast

import earthkit.data as ekd

ds_fl = ekd.from_source("sample", "seasonal_monthly.grib")

The input data contains seasonal monthly forecast. Because the length of a month varies, for this data the forecastMonth key is better suited for describing the temporal structure than using the step* keys.

This is how the first few GRIB messages look like:

ds_fl[0:4].ls(extra_keys="forecastMonth")

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	number	gridType	forecastMonth
0	lfpw	2t	surface	0	19931001	0	744	fcmean	0	regular_ll	1
1	lfpw	2t	surface	0	19931001	0	744	fcmean	1	regular_ll	1
2	lfpw	2t	surface	0	19931001	0	744	fcmean	2	regular_ll	1
3	lfpw	2t	surface	0	19931001	0	1464	fcmean	0	regular_ll	2

In order to use forecastMonth instead of step we need to use the dim_roles option in to_xarray().

ds = ds_fl.to_xarray(time_dim_mode="forecast",
                     dim_roles={"step": "forecastMonth"})
ds

<xarray.Dataset> Size: 395kB
Dimensions:                  (number: 3, forecast_reference_time: 4, step: 6,
                              latitude: 19, longitude: 36)
Coordinates:
  * number                   (number) int64 24B 0 1 2
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 32B 199...
  * step                     (step) int64 48B 1 2 3 4 5 6
  * latitude                 (latitude) float64 152B 90.0 80.0 ... -80.0 -90.0
  * longitude                (longitude) float64 288B 0.0 10.0 ... 340.0 350.0
Data variables:
    2t                       (number, forecast_reference_time, step, latitude, longitude) float64 394kB ...
Attributes: (12/15)
    param:        2t
    paramId:      167
    class:        c3
    stream:       msmm
    levtype:      sfc
    type:         fcmean
    ...           ...
    fcmonth:      1
    origin:       lfpw
    domain:       g
    method:       1
    Conventions:  CF-1.8
    institution:  ECMWF

xarray.Dataset

• Dimensions:
  • number: 3
  • forecast_reference_time: 4
  • step: 6
  • latitude: 19
  • longitude: 36
• Coordinates: (5)
  • number
    (number)
    int64
    0 1 2

    array([0, 1, 2])

  • forecast_reference_time
    (forecast_reference_time)
    datetime64[ns]
    1993-10-01 ... 1996-10-01

    standard_name :

    forecast_reference_time

    long_name :

    initial time of forecast

    array(['1993-10-01T00:00:00.000000000', '1994-10-01T00:00:00.000000000',
           '1995-10-01T00:00:00.000000000', '1996-10-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • step
    (step)
    int64
    1 2 3 4 5 6

    units :

    months

    array([1, 2, 3, 4, 5, 6])

  • latitude
    (latitude)
    float64
    90.0 80.0 70.0 ... -80.0 -90.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([ 90.,  80.,  70.,  60.,  50.,  40.,  30.,  20.,  10.,   0., -10., -20.,
           -30., -40., -50., -60., -70., -80., -90.])

  • longitude
    (longitude)
    float64
    0.0 10.0 20.0 ... 330.0 340.0 350.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([  0.,  10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90., 100., 110.,
           120., 130., 140., 150., 160., 170., 180., 190., 200., 210., 220., 230.,
           240., 250., 260., 270., 280., 290., 300., 310., 320., 330., 340., 350.])

• Data variables: (1)
  • 2t
    (number, forecast_reference_time, step, latitude, longitude)
    float64
    ...

    standard_name :

    unknown

    long_name :

    2 metre temperature

    units :

    K

    _earthkit :

    {'message': b"GRIB\x00\x00\x88\x01\x00\x00P\x80U\x80\xff\x80\xa7\x01\x00\x00]\n\x01\x00\x00\x01\x02\xe8\n\x00\x00\x00\x14b\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x10\x1fP\x04\xc50001\x00\x00\x00\x08\x00\x01\x00\x03\n\x8e\x06\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00 \x00\xff\x00\x00$\x00\x13\x01_\x90\x00\x00\x00\x80\x81_\x90\x05W0'\x10'\x10\x00\x00\x00\x00\x00\x00\x00\x0c\x00\x80\x02D\xb9}n\x00\x007777", 'bitsPerValue': 24}

    [49248 values with dtype=float64]

• Indexes: (5)
  • number
    PandasIndex

    PandasIndex(Index([0, 1, 2], dtype='int64', name='number'))

  • forecast_reference_time
    PandasIndex

    PandasIndex(DatetimeIndex(['1993-10-01', '1994-10-01', '1995-10-01', '1996-10-01'], dtype='datetime64[ns]', name='forecast_reference_time', freq=None))

  • step
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6], dtype='int64', name='step'))

  • latitude
    PandasIndex

    PandasIndex(Index([ 90.0,  80.0,  70.0,  60.0,  50.0,  40.0,  30.0,  20.0,  10.0,   0.0,
           -10.0, -20.0, -30.0, -40.0, -50.0, -60.0, -70.0, -80.0, -90.0],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([  0.0,  10.0,  20.0,  30.0,  40.0,  50.0,  60.0,  70.0,  80.0,  90.0,
           100.0, 110.0, 120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0,
           200.0, 210.0, 220.0, 230.0, 240.0, 250.0, 260.0, 270.0, 280.0, 290.0,
           300.0, 310.0, 320.0, 330.0, 340.0, 350.0],
          dtype='float64', name='longitude'))

• Attributes: (15)

  param :

  2t

  paramId :

  167

  class :

  c3

  stream :

  msmm

  levtype :

  sfc

  type :

  fcmean

  expver :

  0001

  date :

  19931001

  time :

  0

  fcmonth :

  1

  origin :

  lfpw

  domain :

  g

  method :

  1

  Conventions :

  CF-1.8

  institution :

  ECMWF

When we check the “step” dimension we can see its units are “months”.

print(ds["step"])

<xarray.DataArray 'step' (step: 6)> Size: 48B
array([1, 2, 3, 4, 5, 6])
Coordinates:
  * step     (step) int64 48B 1 2 3 4 5 6
Attributes:
    units:    months

By default, the dimensions related to dimension roles are named after the roles. So, although the step dimension was generated from the “forecastMonth” GRIB key the dimension name is still “step”. To override this use the dim_name_from_role_name=False option in to_xarray().

ds = ds_fl.to_xarray(time_dim_mode="forecast",
                     dim_roles={"step": "forecastMonth"},
                     dim_name_from_role_name=False)
ds

<xarray.Dataset> Size: 395kB
Dimensions:                  (number: 3, forecast_reference_time: 4,
                              forecastMonth: 6, latitude: 19, longitude: 36)
Coordinates:
  * number                   (number) int64 24B 0 1 2
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 32B 199...
  * forecastMonth            (forecastMonth) int64 48B 1 2 3 4 5 6
  * latitude                 (latitude) float64 152B 90.0 80.0 ... -80.0 -90.0
  * longitude                (longitude) float64 288B 0.0 10.0 ... 340.0 350.0
Data variables:
    2t                       (number, forecast_reference_time, forecastMonth, latitude, longitude) float64 394kB ...
Attributes: (12/15)
    param:        2t
    paramId:      167
    class:        c3
    stream:       msmm
    levtype:      sfc
    type:         fcmean
    ...           ...
    fcmonth:      1
    origin:       lfpw
    domain:       g
    method:       1
    Conventions:  CF-1.8
    institution:  ECMWF

xarray.Dataset

• Dimensions:
  • number: 3
  • forecast_reference_time: 4
  • forecastMonth: 6
  • latitude: 19
  • longitude: 36
• Coordinates: (5)
  • number
    (number)
    int64
    0 1 2

    array([0, 1, 2])

  • forecast_reference_time
    (forecast_reference_time)
    datetime64[ns]
    1993-10-01 ... 1996-10-01

    standard_name :

    forecast_reference_time

    long_name :

    initial time of forecast

    array(['1993-10-01T00:00:00.000000000', '1994-10-01T00:00:00.000000000',
           '1995-10-01T00:00:00.000000000', '1996-10-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • forecastMonth
    (forecastMonth)
    int64
    1 2 3 4 5 6

    units :

    months

    array([1, 2, 3, 4, 5, 6])

  • latitude
    (latitude)
    float64
    90.0 80.0 70.0 ... -80.0 -90.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([ 90.,  80.,  70.,  60.,  50.,  40.,  30.,  20.,  10.,   0., -10., -20.,
           -30., -40., -50., -60., -70., -80., -90.])

  • longitude
    (longitude)
    float64
    0.0 10.0 20.0 ... 330.0 340.0 350.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([  0.,  10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90., 100., 110.,
           120., 130., 140., 150., 160., 170., 180., 190., 200., 210., 220., 230.,
           240., 250., 260., 270., 280., 290., 300., 310., 320., 330., 340., 350.])

• Data variables: (1)
  • 2t
    (number, forecast_reference_time, forecastMonth, latitude, longitude)
    float64
    ...

    standard_name :

    unknown

    long_name :

    2 metre temperature

    units :

    K

    _earthkit :

    {'message': b"GRIB\x00\x00\x88\x01\x00\x00P\x80U\x80\xff\x80\xa7\x01\x00\x00]\n\x01\x00\x00\x01\x02\xe8\n\x00\x00\x00\x14b\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x10\x1fP\x04\xc50001\x00\x00\x00\x08\x00\x01\x00\x03\n\x8e\x06\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00 \x00\xff\x00\x00$\x00\x13\x01_\x90\x00\x00\x00\x80\x81_\x90\x05W0'\x10'\x10\x00\x00\x00\x00\x00\x00\x00\x0c\x00\x80\x02D\xb9}n\x00\x007777", 'bitsPerValue': 24}

    [49248 values with dtype=float64]

• Indexes: (5)
  • number
    PandasIndex

    PandasIndex(Index([0, 1, 2], dtype='int64', name='number'))

  • forecast_reference_time
    PandasIndex

    PandasIndex(DatetimeIndex(['1993-10-01', '1994-10-01', '1995-10-01', '1996-10-01'], dtype='datetime64[ns]', name='forecast_reference_time', freq=None))

  • forecastMonth
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6], dtype='int64', name='forecastMonth'))

  • latitude
    PandasIndex

    PandasIndex(Index([ 90.0,  80.0,  70.0,  60.0,  50.0,  40.0,  30.0,  20.0,  10.0,   0.0,
           -10.0, -20.0, -30.0, -40.0, -50.0, -60.0, -70.0, -80.0, -90.0],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([  0.0,  10.0,  20.0,  30.0,  40.0,  50.0,  60.0,  70.0,  80.0,  90.0,
           100.0, 110.0, 120.0, 130.0, 140.0, 150.0, 160.0, 170.0, 180.0, 190.0,
           200.0, 210.0, 220.0, 230.0, 240.0, 250.0, 260.0, 270.0, 280.0, 290.0,
           300.0, 310.0, 320.0, 330.0, 340.0, 350.0],
          dtype='float64', name='longitude'))

• Attributes: (15)

  param :

  2t

  paramId :

  167

  class :

  c3

  stream :

  msmm

  levtype :

  sfc

  type :

  fcmean

  expver :

  0001

  date :

  19931001

  time :

  0

  fcmonth :

  1

  origin :

  lfpw

  domain :

  g

  method :

  1

  Conventions :

  CF-1.8

  institution :

  ECMWF