Xarray engine: squeezing dimensions

First, we get some GRIB forecast data on pressure levels and read it into a GRIB fieldlist.

import earthkit.data as ekd

ds_fl = ekd.from_source("sample", "pl.grib").to_fieldlist()

By default, squeeze=True in to_xarray(). This means that if a dimension has only one value, it is removed from the dataset. E.g. in the following example the dimensions "member" and "level_type" are removed:

ds_fl.to_xarray()

<xarray.Dataset> Size: 176kB
Dimensions:                  (forecast_reference_time: 4, step: 2, level: 2,
                              latitude: 19, longitude: 36)
Coordinates:
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 32B 202...
  * step                     (step) timedelta64[ns] 16B 00:00:00 06:00:00
  * level                    (level) int64 16B 500 700
  * 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:
    r                        (forecast_reference_time, step, level, latitude, longitude) float64 88kB ...
    t                        (forecast_reference_time, step, level, latitude, longitude) float64 88kB ...
Attributes:
    Conventions:  CF-1.8
    institution:  ECMWF

xarray.Dataset

• Dimensions:
  • forecast_reference_time: 4
  • step: 2
  • level: 2
  • latitude: 19
  • longitude: 36
• Coordinates: (5)
  • forecast_reference_time
    (forecast_reference_time)
    datetime64[ns]
    2024-06-03 ... 2024-06-04T12:00:00

    array(['2024-06-03T00:00:00.000000000', '2024-06-03T12:00:00.000000000',
           '2024-06-04T00:00:00.000000000', '2024-06-04T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • step
    (step)
    timedelta64[ns]
    00:00:00 06:00:00

    standard_name :

    forecast_period

    long_name :

    time since forecast_reference_time

    array([             0, 21600000000000], dtype='timedelta64[ns]')

  • level
    (level)
    int64
    500 700

    standard_name :

    air_pressure

    long_name :

    pressure

    units :

    hectopascal

    positive :

    down

    array([500, 700])

  • 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: (2)
  • r
    (forecast_reference_time, step, level, latitude, longitude)
    float64
    ...

    standard_name :

    relative_humidity

    long_name :

    Relative humidity

    units :

    percent

    level_type :

    pressure

    _earthkit :

    {'message': b"GRIB\x00\x00l\x01\x00\x004\x80b\x9a\xff\x80\x9dd\x01\xf4\x18\x06\x03\x00\x00\x01\x00\x00\x01\x00\x00\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x01\t\x04\x010001\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': 16}

    [10944 values with dtype=float64]

  • t
    (forecast_reference_time, step, level, latitude, longitude)
    float64
    ...

    standard_name :

    air_temperature

    long_name :

    Temperature

    units :

    kelvin

    level_type :

    pressure

    _earthkit :

    {'message': b"GRIB\x00\x00l\x01\x00\x004\x80b\x9a\xff\x80\x82d\x01\xf4\x18\x06\x03\x00\x00\x01\x00\x00\x01\x00\x00\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x01\t\x04\x010001\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': 16}

    [10944 values with dtype=float64]

• Attributes: (2)

  Conventions :

  CF-1.8

  institution :

  ECMWF

When using squeeze=False these dimensions are added to the dataset.

ds_fl.to_xarray(squeeze=False)

<xarray.Dataset> Size: 176kB
Dimensions:                  (member: 1, forecast_reference_time: 4, step: 2,
                              level: 2, level_type: 1, latitude: 19,
                              longitude: 36)
Coordinates:
  * member                   (member) <U1 4B '0'
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 32B 202...
  * step                     (step) timedelta64[ns] 16B 00:00:00 06:00:00
  * level                    (level) int64 16B 500 700
  * level_type               (level_type) <U8 32B 'pressure'
  * 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:
    r                        (member, forecast_reference_time, step, level, level_type, latitude, longitude) float64 88kB ...
    t                        (member, forecast_reference_time, step, level, level_type, latitude, longitude) float64 88kB ...
Attributes:
    Conventions:  CF-1.8
    institution:  ECMWF

xarray.Dataset

• Dimensions:
  • member: 1
  • forecast_reference_time: 4
  • step: 2
  • level: 2
  • level_type: 1
  • latitude: 19
  • longitude: 36
• Coordinates: (7)
  • member
    (member)
    <U1
    '0'

    array(['0'], dtype='<U1')

  • forecast_reference_time
    (forecast_reference_time)
    datetime64[ns]
    2024-06-03 ... 2024-06-04T12:00:00

    array(['2024-06-03T00:00:00.000000000', '2024-06-03T12:00:00.000000000',
           '2024-06-04T00:00:00.000000000', '2024-06-04T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • step
    (step)
    timedelta64[ns]
    00:00:00 06:00:00

    standard_name :

    forecast_period

    long_name :

    time since forecast_reference_time

    array([             0, 21600000000000], dtype='timedelta64[ns]')

  • level
    (level)
    int64
    500 700

    standard_name :

    air_pressure

    long_name :

    pressure

    units :

    hectopascal

    positive :

    down

    array([500, 700])

  • level_type
    (level_type)
    <U8
    'pressure'

    array(['pressure'], dtype='<U8')

  • 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: (2)
  • r
    (member, forecast_reference_time, step, level, level_type, latitude, longitude)
    float64
    ...

    standard_name :

    relative_humidity

    long_name :

    Relative humidity

    units :

    percent

    level_type :

    pressure

    _earthkit :

    {'message': b"GRIB\x00\x00l\x01\x00\x004\x80b\x9a\xff\x80\x9dd\x01\xf4\x18\x06\x03\x00\x00\x01\x00\x00\x01\x00\x00\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x01\t\x04\x010001\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': 16}

    [10944 values with dtype=float64]

  • t
    (member, forecast_reference_time, step, level, level_type, latitude, longitude)
    float64
    ...

    standard_name :

    air_temperature

    long_name :

    Temperature

    units :

    kelvin

    level_type :

    pressure

    _earthkit :

    {'message': b"GRIB\x00\x00l\x01\x00\x004\x80b\x9a\xff\x80\x82d\x01\xf4\x18\x06\x03\x00\x00\x01\x00\x00\x01\x00\x00\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x01\t\x04\x010001\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': 16}

    [10944 values with dtype=float64]

• Attributes: (2)

  Conventions :

  CF-1.8

  institution :

  ECMWF

An alternative way to achieve this is to use the ensure_dims option.

ds_fl.to_xarray(ensure_dims=["member", "level_type"])

<xarray.Dataset> Size: 176kB
Dimensions:                  (member: 1, forecast_reference_time: 4, step: 2,
                              level: 2, level_type: 1, latitude: 19,
                              longitude: 36)
Coordinates:
  * member                   (member) <U1 4B '0'
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 32B 202...
  * step                     (step) timedelta64[ns] 16B 00:00:00 06:00:00
  * level                    (level) int64 16B 500 700
  * level_type               (level_type) <U8 32B 'pressure'
  * 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:
    r                        (member, forecast_reference_time, step, level, level_type, latitude, longitude) float64 88kB ...
    t                        (member, forecast_reference_time, step, level, level_type, latitude, longitude) float64 88kB ...
Attributes:
    Conventions:  CF-1.8
    institution:  ECMWF

xarray.Dataset

• Dimensions:
  • member: 1
  • forecast_reference_time: 4
  • step: 2
  • level: 2
  • level_type: 1
  • latitude: 19
  • longitude: 36
• Coordinates: (7)
  • member
    (member)
    <U1
    '0'

    array(['0'], dtype='<U1')

  • forecast_reference_time
    (forecast_reference_time)
    datetime64[ns]
    2024-06-03 ... 2024-06-04T12:00:00

    array(['2024-06-03T00:00:00.000000000', '2024-06-03T12:00:00.000000000',
           '2024-06-04T00:00:00.000000000', '2024-06-04T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • step
    (step)
    timedelta64[ns]
    00:00:00 06:00:00

    standard_name :

    forecast_period

    long_name :

    time since forecast_reference_time

    array([             0, 21600000000000], dtype='timedelta64[ns]')

  • level
    (level)
    int64
    500 700

    standard_name :

    air_pressure

    long_name :

    pressure

    units :

    hectopascal

    positive :

    down

    array([500, 700])

  • level_type
    (level_type)
    <U8
    'pressure'

    array(['pressure'], dtype='<U8')

  • 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: (2)
  • r
    (member, forecast_reference_time, step, level, level_type, latitude, longitude)
    float64
    ...

    standard_name :

    relative_humidity

    long_name :

    Relative humidity

    units :

    percent

    level_type :

    pressure

    _earthkit :

    {'message': b"GRIB\x00\x00l\x01\x00\x004\x80b\x9a\xff\x80\x9dd\x01\xf4\x18\x06\x03\x00\x00\x01\x00\x00\x01\x00\x00\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x01\t\x04\x010001\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': 16}

    [10944 values with dtype=float64]

  • t
    (member, forecast_reference_time, step, level, level_type, latitude, longitude)
    float64
    ...

    standard_name :

    air_temperature

    long_name :

    Temperature

    units :

    kelvin

    level_type :

    pressure

    _earthkit :

    {'message': b"GRIB\x00\x00l\x01\x00\x004\x80b\x9a\xff\x80\x82d\x01\xf4\x18\x06\x03\x00\x00\x01\x00\x00\x01\x00\x00\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x01\t\x04\x010001\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': 16}

    [10944 values with dtype=float64]

• Attributes: (2)

  Conventions :

  CF-1.8

  institution :

  ECMWF