Introduction to Xarray

What is Xarray?

• Multidimensional labled data arrays (like NumPy but with coordinates and metadata).

• Designed for Earth science and climate data.

• Handles NetCDF, GRIB, Zarr, and other formats.

• Supports lazy evaluation and Dask for large datasets.

Core Concepts

• DataArray:

  • One variable with coordinates and metadata.

  • Similar to numpy ndarray but with labels.

• Dataset:

  • A collection of DataArrays.

  • Similar to a dictionary of DataArrays.

  • Can have shared coordinates and attributes.

• Coordinates:

  • Labels for dimensions (e.g., time, latitude, longitude).

• Attributes:

  • Metadata for DataArrays and Datasets (e.g., units, descriptions).

• Indexing:

  • Accessing data using labels instead of integer indices.

Import libraries

import xarray as xr
import pandas as pd
import numpy as np

Create a DataArray

data = np.random.rand(3,4)
coords = {"time":pd.date_range("2024-01-01",periods = 3),"x":["a","b","c","d"]}
da = xr.DataArray(data, coords=coords, dims=["time", "x"],name="temperature")
da.attrs["created"] = pd.Timestamp.now()
da.attrs["description"] = "random temperature data"
print ("full data array:",da)
print ("coordinates:",da.coords)
print ("dimensions:",da.dims)
print ("shape:",da.shape)
print ("attr:",da.attrs)
print ("data",da.data)

full data array: <xarray.DataArray 'temperature' (time: 3, x: 4)> Size: 96B
array([[0.81622085, 0.84735095, 0.78511268, 0.42797343],
       [0.02546469, 0.75821049, 0.17291016, 0.96740412],
       [0.87075239, 0.52794664, 0.63172434, 0.07998903]])
Coordinates:
  * time     (time) datetime64[ns] 24B 2024-01-01 2024-01-02 2024-01-03
  * x        (x) <U1 16B 'a' 'b' 'c' 'd'
Attributes:
    created:      2025-06-23 10:49:44.606908
    description:  random temperature data
coordinates: Coordinates:
  * time     (time) datetime64[ns] 24B 2024-01-01 2024-01-02 2024-01-03
  * x        (x) <U1 16B 'a' 'b' 'c' 'd'
dimensions: ('time', 'x')
shape: (3, 4)
attr: {'created': Timestamp('2025-06-23 10:49:44.606908'), 'description': 'random temperature data'}
data [[0.81622085 0.84735095 0.78511268 0.42797343]
 [0.02546469 0.75821049 0.17291016 0.96740412]
 [0.87075239 0.52794664 0.63172434 0.07998903]]

Create a Dataset

ds = xr.Dataset({"temperature":da,"pressure":(("time","x"),np.random.rand(3,4))})
print ("full dataset:",ds)
print ("variables:",ds.variables)
print ("dimensions:",ds.dims)
print("coordinates:",ds.coords)
print ("attr:",ds.attrs)

full dataset: <xarray.Dataset> Size: 232B
Dimensions:      (time: 3, x: 4)
Coordinates:
  * time         (time) datetime64[ns] 24B 2024-01-01 2024-01-02 2024-01-03
  * x            (x) <U1 16B 'a' 'b' 'c' 'd'
Data variables:
    temperature  (time, x) float64 96B 0.8162 0.8474 0.7851 ... 0.6317 0.07999
    pressure     (time, x) float64 96B 0.4356 0.4608 0.6451 ... 0.9273 0.6405
variables: Frozen({'time': <xarray.IndexVariable 'time' (time: 3)> Size: 24B
array(['2024-01-01T00:00:00.000000000', '2024-01-02T00:00:00.000000000',
       '2024-01-03T00:00:00.000000000'], dtype='datetime64[ns]'), 'x': <xarray.IndexVariable 'x' (x: 4)> Size: 16B
array(['a', 'b', 'c', 'd'], dtype='<U1'), 'temperature': <xarray.Variable (time: 3, x: 4)> Size: 96B
array([[0.81622085, 0.84735095, 0.78511268, 0.42797343],
       [0.02546469, 0.75821049, 0.17291016, 0.96740412],
       [0.87075239, 0.52794664, 0.63172434, 0.07998903]])
Attributes:
    created:      2025-06-23 10:49:44.606908
    description:  random temperature data, 'pressure': <xarray.Variable (time: 3, x: 4)> Size: 96B
array([[0.43558267, 0.46083827, 0.64505775, 0.85668732],
       [0.44970239, 0.68484891, 0.02462753, 0.14572613],
       [0.4872204 , 0.4218475 , 0.92731831, 0.64052972]])})
dimensions: FrozenMappingWarningOnValuesAccess({'time': 3, 'x': 4})
coordinates: Coordinates:
  * time     (time) datetime64[ns] 24B 2024-01-01 2024-01-02 2024-01-03
  * x        (x) <U1 16B 'a' 'b' 'c' 'd'
attr: {}

Indexing, Selecting and Slicing

temps = xr.DataArray(
    data=[[15, 20, 25], [10, 12, 14]],
    dims=["location", "time"],
    coords={"location": ["CityA", "CityB"], "time": ["Morning", "Noon","Evening"]},
    name="temperature_C",
)
print(temps)

# Create a Dataset with multiple variables
ds = xr.Dataset(
    {
        "temperature_C": temps,
        "humidity": (("location", "time"), [[80, 75, 70], [65, 60, 55]]),
    }
)
print(ds)

<xarray.DataArray 'temperature_C' (location: 2, time: 3)> Size: 48B
array([[15, 20, 25],
       [10, 12, 14]])
Coordinates:
  * location  (location) <U5 40B 'CityA' 'CityB'
  * time      (time) <U7 84B 'Morning' 'Noon' 'Evening'
<xarray.Dataset> Size: 220B
Dimensions:        (location: 2, time: 3)
Coordinates:
  * location       (location) <U5 40B 'CityA' 'CityB'
  * time           (time) <U7 84B 'Morning' 'Noon' 'Evening'
Data variables:
    temperature_C  (location, time) int64 48B 15 20 25 10 12 14
    humidity       (location, time) int64 48B 80 75 70 65 60 55

ds

<xarray.Dataset> Size: 220B
Dimensions:        (location: 2, time: 3)
Coordinates:
  * location       (location) <U5 40B 'CityA' 'CityB'
  * time           (time) <U7 84B 'Morning' 'Noon' 'Evening'
Data variables:
    temperature_C  (location, time) int64 48B 15 20 25 10 12 14
    humidity       (location, time) int64 48B 80 75 70 65 60 55

xarray.Dataset

• Dimensions:
  • location: 2
  • time: 3
• Coordinates: (2)
  • location
    (location)
    <U5
    'CityA' 'CityB'

    array(['CityA', 'CityB'], dtype='<U5')

  • time
    (time)
    <U7
    'Morning' 'Noon' 'Evening'

    array(['Morning', 'Noon', 'Evening'], dtype='<U7')

• Data variables: (2)
  • temperature_C
    (location, time)
    int64
    15 20 25 10 12 14

    array([[15, 20, 25],
           [10, 12, 14]])

  • humidity
    (location, time)
    int64
    80 75 70 65 60 55

    array([[80, 75, 70],
           [65, 60, 55]])

• Indexes: (2)
  • location
    PandasIndex

    PandasIndex(Index(['CityA', 'CityB'], dtype='object', name='location'))

  • time
    PandasIndex

    PandasIndex(Index(['Morning', 'Noon', 'Evening'], dtype='object', name='time'))

• Attributes: (0)

# Select by coordinate label
print("Select CityA data:")
print(ds.sel(location="CityA"))

# Select by integer position
print("Select first location data:")
print(ds.isel(location=0))

# Select multiple locations
print("Select CityA and CityB data:")
print(ds.sel(location=["CityA", "CityB"]))

# Boolean indexing example
print("Select temperature > 12:")
print(ds["temperature_C"].where(ds["temperature_C"] > 12, drop=True))

Select CityA data:
<xarray.Dataset> Size: 152B
Dimensions:        (time: 3)
Coordinates:
    location       <U5 20B 'CityA'
  * time           (time) <U7 84B 'Morning' 'Noon' 'Evening'
Data variables:
    temperature_C  (time) int64 24B 15 20 25
    humidity       (time) int64 24B 80 75 70
Select first location data:
<xarray.Dataset> Size: 152B
Dimensions:        (time: 3)
Coordinates:
    location       <U5 20B 'CityA'
  * time           (time) <U7 84B 'Morning' 'Noon' 'Evening'
Data variables:
    temperature_C  (time) int64 24B 15 20 25
    humidity       (time) int64 24B 80 75 70
Select CityA and CityB data:
<xarray.Dataset> Size: 220B
Dimensions:        (location: 2, time: 3)
Coordinates:
  * location       (location) <U5 40B 'CityA' 'CityB'
  * time           (time) <U7 84B 'Morning' 'Noon' 'Evening'
Data variables:
    temperature_C  (location, time) int64 48B 15 20 25 10 12 14
    humidity       (location, time) int64 48B 80 75 70 65 60 55
Select temperature > 12:
<xarray.DataArray 'temperature_C' (location: 2, time: 3)> Size: 48B
array([[15., 20., 25.],
       [nan, nan, 14.]])
Coordinates:
  * location  (location) <U5 40B 'CityA' 'CityB'
  * time      (time) <U7 84B 'Morning' 'Noon' 'Evening'

Real Data Example

ds = xr.tutorial.open_dataset('era5-2mt-2019-03-uk.grib')
ds

/Users/syam/virtualenvs/myvenv/lib/python3.13/site-packages/cfgrib/xarray_plugin.py:131: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  vars, attrs, coord_names = xr.conventions.decode_cf_variables(

<xarray.Dataset> Size: 5MB
Dimensions:     (time: 744, latitude: 33, longitude: 49)
Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 6kB 2019-03-01 ... 2019-03-31T23:00:00
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 264B 58.0 57.75 57.5 ... 50.5 50.25 50.0
  * longitude   (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
    valid_time  (time) datetime64[ns] 6kB ...
Data variables:
    t2m         (time, latitude, longitude) float32 5MB ...
Attributes:
    GRIB_edition:            1
    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:                 2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • time: 744
  • latitude: 33
  • longitude: 49
• Coordinates: (7)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2019-03-01 ... 2019-03-31T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2019-03-01T00:00:00.000000000', '2019-03-01T01:00:00.000000000',
           '2019-03-01T02:00:00.000000000', ..., '2019-03-31T21:00:00.000000000',
           '2019-03-31T22:00:00.000000000', '2019-03-31T23:00:00.000000000'],
          shape=(744,), dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    (latitude)
    float64
    58.0 57.75 57.5 ... 50.5 50.25 50.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
           55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
           53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
           50.5 , 50.25, 50.  ])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 1.5 1.75 2.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
            -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
            -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
            -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
            -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
             1.25,   1.5 ,   1.75,   2.  ])

  • valid_time
    (time)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [744 values with dtype=datetime64[ns]]

• Data variables: (1)
  • t2m
    (time, latitude, longitude)
    float32
    ...

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    1617

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    49

    GRIB_Ny :

    33

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    58.0

    GRIB_latitudeOfLastGridPointInDegrees :

    50.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    2.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    [1203048 values with dtype=float32]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-03-01 00:00:00', '2019-03-01 01:00:00',
                   '2019-03-01 02:00:00', '2019-03-01 03:00:00',
                   '2019-03-01 04:00:00', '2019-03-01 05:00:00',
                   '2019-03-01 06:00:00', '2019-03-01 07:00:00',
                   '2019-03-01 08:00:00', '2019-03-01 09:00:00',
                   ...
                   '2019-03-31 14:00:00', '2019-03-31 15:00:00',
                   '2019-03-31 16:00:00', '2019-03-31 17:00:00',
                   '2019-03-31 18:00:00', '2019-03-31 19:00:00',
                   '2019-03-31 20:00:00', '2019-03-31 21:00:00',
                   '2019-03-31 22:00:00', '2019-03-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=744, freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0, 55.75,
            55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5, 53.25,
            53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25,  51.0, 50.75,
            50.5, 50.25,  50.0],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
            -7.5, -7.25,  -7.0, -6.75,  -6.5, -6.25,  -6.0, -5.75,  -5.5, -5.25,
            -5.0, -4.75,  -4.5, -4.25,  -4.0, -3.75,  -3.5, -3.25,  -3.0, -2.75,
            -2.5, -2.25,  -2.0, -1.75,  -1.5, -1.25,  -1.0, -0.75,  -0.5, -0.25,
             0.0,  0.25,   0.5,  0.75,   1.0,  1.25,   1.5,  1.75,   2.0],
          dtype='float64', name='longitude'))

• Attributes: (7)

  GRIB_edition :

  1

  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 :

  2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.15.0/ecCodes-2.41.0 with {"source": "../../../../../../Library/Caches/xarray_tutorial_data/fbb9a0d5090f059b49c88c719140d7ae-era5-2mt-2019-03-uk.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

Inspect the Dataset

print ("DIMENTIONS",ds.dims)
print ("COORDINATES",ds.coords)
print ("DATA VARIABLES",ds.data_vars)
print ("ATTRIBUTES",ds.attrs)

DIMENTIONS FrozenMappingWarningOnValuesAccess({'time': 744, 'latitude': 33, 'longitude': 49})
COORDINATES Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 6kB 2019-03-01 ... 2019-03-31T23:00:00
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 264B 58.0 57.75 57.5 ... 50.5 50.25 50.0
  * longitude   (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
    valid_time  (time) datetime64[ns] 6kB ...
DATA VARIABLES Data variables:
    t2m      (time, latitude, longitude) float32 5MB ...
ATTRIBUTES {'GRIB_edition': 1, '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': '2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.15.0/ecCodes-2.41.0 with {"source": "../../../../../../Library/Caches/xarray_tutorial_data/fbb9a0d5090f059b49c88c719140d7ae-era5-2mt-2019-03-uk.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}'}

ds["t2m"]

<xarray.DataArray 't2m' (time: 744, latitude: 33, longitude: 49)> Size: 5MB
[1203048 values with dtype=float32]
Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 6kB 2019-03-01 ... 2019-03-31T23:00:00
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 264B 58.0 57.75 57.5 ... 50.5 50.25 50.0
  * longitude   (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
    valid_time  (time) datetime64[ns] 6kB ...
Attributes: (12/31)
    GRIB_paramId:                             167
    GRIB_dataType:                            an
    GRIB_numberOfPoints:                      1617
    GRIB_typeOfLevel:                         surface
    GRIB_stepUnits:                           1
    GRIB_stepType:                            instant
    ...                                       ...
    GRIB_shortName:                           2t
    GRIB_totalNumber:                         0
    GRIB_units:                               K
    long_name:                                2 metre temperature
    units:                                    K
    standard_name:                            unknown

xarray.DataArray

't2m'

• time: 744
• latitude: 33
• longitude: 49

• ...

  [1203048 values with dtype=float32]

• Coordinates: (7)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2019-03-01 ... 2019-03-31T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2019-03-01T00:00:00.000000000', '2019-03-01T01:00:00.000000000',
           '2019-03-01T02:00:00.000000000', ..., '2019-03-31T21:00:00.000000000',
           '2019-03-31T22:00:00.000000000', '2019-03-31T23:00:00.000000000'],
          shape=(744,), dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    (latitude)
    float64
    58.0 57.75 57.5 ... 50.5 50.25 50.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
           55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
           53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
           50.5 , 50.25, 50.  ])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 1.5 1.75 2.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
            -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
            -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
            -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
            -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
             1.25,   1.5 ,   1.75,   2.  ])

  • valid_time
    (time)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [744 values with dtype=datetime64[ns]]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-03-01 00:00:00', '2019-03-01 01:00:00',
                   '2019-03-01 02:00:00', '2019-03-01 03:00:00',
                   '2019-03-01 04:00:00', '2019-03-01 05:00:00',
                   '2019-03-01 06:00:00', '2019-03-01 07:00:00',
                   '2019-03-01 08:00:00', '2019-03-01 09:00:00',
                   ...
                   '2019-03-31 14:00:00', '2019-03-31 15:00:00',
                   '2019-03-31 16:00:00', '2019-03-31 17:00:00',
                   '2019-03-31 18:00:00', '2019-03-31 19:00:00',
                   '2019-03-31 20:00:00', '2019-03-31 21:00:00',
                   '2019-03-31 22:00:00', '2019-03-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=744, freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0, 55.75,
            55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5, 53.25,
            53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25,  51.0, 50.75,
            50.5, 50.25,  50.0],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
            -7.5, -7.25,  -7.0, -6.75,  -6.5, -6.25,  -6.0, -5.75,  -5.5, -5.25,
            -5.0, -4.75,  -4.5, -4.25,  -4.0, -3.75,  -3.5, -3.25,  -3.0, -2.75,
            -2.5, -2.25,  -2.0, -1.75,  -1.5, -1.25,  -1.0, -0.75,  -0.5, -0.25,
             0.0,  0.25,   0.5,  0.75,   1.0,  1.25,   1.5,  1.75,   2.0],
          dtype='float64', name='longitude'))

• Attributes: (31)

  GRIB_paramId :

  167

  GRIB_dataType :

  an

  GRIB_numberOfPoints :

  1617

  GRIB_typeOfLevel :

  surface

  GRIB_stepUnits :

  1

  GRIB_stepType :

  instant

  GRIB_gridType :

  regular_ll

  GRIB_uvRelativeToGrid :

  0

  GRIB_NV :

  0

  GRIB_Nx :

  49

  GRIB_Ny :

  33

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  t2m

  GRIB_gridDefinitionDescription :

  Latitude/Longitude Grid

  GRIB_iDirectionIncrementInDegrees :

  0.25

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.25

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  58.0

  GRIB_latitudeOfLastGridPointInDegrees :

  50.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  -10.0

  GRIB_longitudeOfLastGridPointInDegrees :

  2.0

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  2 metre temperature

  GRIB_shortName :

  2t

  GRIB_totalNumber :

  0

  GRIB_units :

  K

  long_name :

  2 metre temperature

  units :

  K

  standard_name :

  unknown

Inspect the DataArray

da = ds.t2m
print(da.name)
print (da.dims)
print (da.coords)

t2m
('time', 'latitude', 'longitude')
Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 6kB 2019-03-01 ... 2019-03-31T23:00:00
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 264B 58.0 57.75 57.5 ... 50.5 50.25 50.0
  * longitude   (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
    valid_time  (time) datetime64[ns] 6kB ...

da.longitude

<xarray.DataArray 'longitude' (longitude: 49)> Size: 392B
array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
        -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
        -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
        -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
        -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
         1.25,   1.5 ,   1.75,   2.  ])
Coordinates:
    number     int64 8B ...
    step       timedelta64[ns] 8B ...
    surface    float64 8B ...
  * longitude  (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
Attributes:
    units:          degrees_east
    standard_name:  longitude
    long_name:      longitude

xarray.DataArray

'longitude'

• longitude: 49

• -10.0 -9.75 -9.5 -9.25 -9.0 -8.75 -8.5 ... 0.75 1.0 1.25 1.5 1.75 2.0

  array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
          -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
          -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
          -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
          -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
           1.25,   1.5 ,   1.75,   2.  ])

• Coordinates: (4)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 1.5 1.75 2.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
            -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
            -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
            -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
            -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
             1.25,   1.5 ,   1.75,   2.  ])

• Indexes: (1)
  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
            -7.5, -7.25,  -7.0, -6.75,  -6.5, -6.25,  -6.0, -5.75,  -5.5, -5.25,
            -5.0, -4.75,  -4.5, -4.25,  -4.0, -3.75,  -3.5, -3.25,  -3.0, -2.75,
            -2.5, -2.25,  -2.0, -1.75,  -1.5, -1.25,  -1.0, -0.75,  -0.5, -0.25,
             0.0,  0.25,   0.5,  0.75,   1.0,  1.25,   1.5,  1.75,   2.0],
          dtype='float64', name='longitude'))

• Attributes: (3)

  units :

  degrees_east

  standard_name :

  longitude

  long_name :

  longitude

da.coords["latitude"]

<xarray.DataArray 'latitude' (latitude: 33)> Size: 264B
array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
       55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
       53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
       50.5 , 50.25, 50.  ])
Coordinates:
    number    int64 8B ...
    step      timedelta64[ns] 8B ...
    surface   float64 8B ...
  * latitude  (latitude) float64 264B 58.0 57.75 57.5 57.25 ... 50.5 50.25 50.0
Attributes:
    units:             degrees_north
    standard_name:     latitude
    long_name:         latitude
    stored_direction:  decreasing

xarray.DataArray

'latitude'

• latitude: 33

• 58.0 57.75 57.5 57.25 57.0 56.75 ... 51.25 51.0 50.75 50.5 50.25 50.0

  array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
         55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
         53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
         50.5 , 50.25, 50.  ])

• Coordinates: (4)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    (latitude)
    float64
    58.0 57.75 57.5 ... 50.5 50.25 50.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
           55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
           53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
           50.5 , 50.25, 50.  ])

• Indexes: (1)
  • latitude
    PandasIndex

    PandasIndex(Index([ 58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0, 55.75,
            55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5, 53.25,
            53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25,  51.0, 50.75,
            50.5, 50.25,  50.0],
          dtype='float64', name='latitude'))

• Attributes: (4)

  units :

  degrees_north

  standard_name :

  latitude

  long_name :

  latitude

  stored_direction :

  decreasing

da.attrs

{'GRIB_paramId': 167,
 'GRIB_dataType': 'an',
 'GRIB_numberOfPoints': 1617,
 'GRIB_typeOfLevel': 'surface',
 'GRIB_stepUnits': 1,
 'GRIB_stepType': 'instant',
 'GRIB_gridType': 'regular_ll',
 'GRIB_uvRelativeToGrid': 0,
 'GRIB_NV': 0,
 'GRIB_Nx': 49,
 'GRIB_Ny': 33,
 'GRIB_cfName': 'unknown',
 'GRIB_cfVarName': 't2m',
 'GRIB_gridDefinitionDescription': 'Latitude/Longitude Grid',
 'GRIB_iDirectionIncrementInDegrees': 0.25,
 'GRIB_iScansNegatively': 0,
 'GRIB_jDirectionIncrementInDegrees': 0.25,
 'GRIB_jPointsAreConsecutive': 0,
 'GRIB_jScansPositively': 0,
 'GRIB_latitudeOfFirstGridPointInDegrees': 58.0,
 'GRIB_latitudeOfLastGridPointInDegrees': 50.0,
 'GRIB_longitudeOfFirstGridPointInDegrees': -10.0,
 'GRIB_longitudeOfLastGridPointInDegrees': 2.0,
 'GRIB_missingValue': 3.4028234663852886e+38,
 'GRIB_name': '2 metre temperature',
 'GRIB_shortName': '2t',
 'GRIB_totalNumber': 0,
 'GRIB_units': 'K',
 'long_name': '2 metre temperature',
 'units': 'K',
 'standard_name': 'unknown'}

da.attrs["meditwin"] = "mediterranean twin summer school"
da.attrs

{'GRIB_paramId': 167,
 'GRIB_dataType': 'an',
 'GRIB_numberOfPoints': 1617,
 'GRIB_typeOfLevel': 'surface',
 'GRIB_stepUnits': 1,
 'GRIB_stepType': 'instant',
 'GRIB_gridType': 'regular_ll',
 'GRIB_uvRelativeToGrid': 0,
 'GRIB_NV': 0,
 'GRIB_Nx': 49,
 'GRIB_Ny': 33,
 'GRIB_cfName': 'unknown',
 'GRIB_cfVarName': 't2m',
 'GRIB_gridDefinitionDescription': 'Latitude/Longitude Grid',
 'GRIB_iDirectionIncrementInDegrees': 0.25,
 'GRIB_iScansNegatively': 0,
 'GRIB_jDirectionIncrementInDegrees': 0.25,
 'GRIB_jPointsAreConsecutive': 0,
 'GRIB_jScansPositively': 0,
 'GRIB_latitudeOfFirstGridPointInDegrees': 58.0,
 'GRIB_latitudeOfLastGridPointInDegrees': 50.0,
 'GRIB_longitudeOfFirstGridPointInDegrees': -10.0,
 'GRIB_longitudeOfLastGridPointInDegrees': 2.0,
 'GRIB_missingValue': 3.4028234663852886e+38,
 'GRIB_name': '2 metre temperature',
 'GRIB_shortName': '2t',
 'GRIB_totalNumber': 0,
 'GRIB_units': 'K',
 'long_name': '2 metre temperature',
 'units': 'K',
 'standard_name': 'unknown',
 'meditwin': 'mediterranean twin summer school'}

Accessing Data in a DataArray as a NumPy Array

print(da.data)
print(da.data.shape)
print(type(da.data))

[[[282.4248  282.30762 282.18848 ... 279.29004 279.3545  279.40918]
  [282.55957 282.4502  282.33887 ... 279.22168 279.2412  279.26074]
  [282.68262 282.6162  282.55957 ... 279.12598 279.15723 279.18457]
  ...
  [283.8623  283.76855 283.66895 ... 282.4287  282.22754 281.8545 ]
  [283.87012 283.7627  283.65723 ... 282.4209  282.2373  281.9287 ]
  [283.87598 283.73535 283.62988 ... 281.98535 281.95996 282.08887]]

 [[282.5188  282.3977  282.27075 ... 279.3606  279.40356 279.43872]
  [282.66528 282.57153 282.47583 ... 279.2981  279.31372 279.3274 ]
  [282.75317 282.6809  282.6145  ... 279.15942 279.18286 279.20435]
  ...
  [283.91724 283.79614 283.67505 ... 282.34888 282.11646 281.78442]
  [283.9309  283.79614 283.6692  ... 282.38208 282.17505 281.88013]
  [283.94653 283.79614 283.66724 ... 281.82935 281.8235  281.9524 ]]

 [[282.6958  282.5708  282.42627 ... 279.44775 279.4751  279.49854]
  [282.7036  282.6372  282.5708  ... 279.42822 279.4419  279.45557]
  [282.70752 282.65283 282.60205 ... 279.23096 279.30518 279.36768]
  ...
  [284.00244 283.85986 283.7173  ... 282.2329  282.0415  281.82275]
  [283.99854 283.854   283.7212  ... 282.31494 282.14697 281.89307]
  [283.99854 283.86182 283.729   ... 281.68408 281.6333  281.75244]]

 ...

 [[281.23022 281.3181  281.4099  ... 278.17358 278.14624 278.11108]
  [281.2517  281.32593 281.3982  ... 278.13257 278.07593 278.01733]
  [281.29468 281.32593 281.3474  ... 278.09546 278.03296 277.97046]
  ...
  [283.4685  283.3806  283.32202 ... 282.32202 282.02905 281.86694]
  [283.4978  283.40405 283.32007 ... 283.15015 282.8728  282.59155]
  [283.47046 283.41577 283.32983 ... 283.07983 282.99585 282.98218]]

 [[281.15845 281.17212 281.18774 ... 278.0276  277.99634 277.95142]
  [281.17212 281.17798 281.1819  ... 278.03345 277.9436  277.8518 ]
  [281.1526  281.15845 281.1643  ... 278.02954 277.9729  277.9104 ]
  ...
  [283.51782 283.36743 283.3186  ... 281.8772  281.54517 281.3772 ]
  [283.47876 283.36743 283.3225  ... 282.469   282.16626 281.92017]
  [283.4104  283.42212 283.37915 ... 282.1565  282.1194  282.12134]]

 [[281.09937 281.14038 281.1853  ... 278.13257 278.05835 277.9685 ]
  [281.1189  281.15405 281.18726 ... 278.16382 278.0271  277.88843]
  [281.1599  281.17554 281.1853  ... 278.11108 278.04272 277.9607 ]
  ...
  [283.65015 283.4646  283.406   ... 281.64624 281.15796 280.86108]
  [283.60132 283.4607  283.406   ... 282.1267  281.66772 281.28882]
  [283.47437 283.52124 283.4685  ... 281.66968 281.54468 281.45483]]]
(744, 33, 49)
<class 'numpy.ndarray'>

Analysis Using Xarray

ds.t2m.isel(time=0).plot(x="longitude")

<matplotlib.collections.QuadMesh at 0x15168f770>

import xarray as xr
import ipywidgets as widgets
import matplotlib.pyplot as plt
from IPython.display import display

time_slider = widgets.IntSlider(
    value=0,
    min=0,
    max=len(ds.time) - 1,
    step=1,
    description="Time Index:",
    continuous_update=False,
)


# Plot function that updates with slider
def plot_time_index(time_idx):
    plt.figure(figsize=(10, 5))
    ds.t2m.isel(time=time_idx).plot(x="longitude")
    plt.title(f"t2m at time index {time_idx} ({str(ds.time[time_idx].values)})")
    plt.show()


# Link widget to plot function
widgets.interact(plot_time_index, time_idx=time_slider)

<function __main__.plot_time_index(time_idx)>

ds.t2m.mean(dim="time").plot(x="longitude")

<matplotlib.collections.QuadMesh at 0x151bb8910>

ds.sel(time="2019-03-10")

<xarray.Dataset> Size: 156kB
Dimensions:     (time: 24, latitude: 33, longitude: 49)
Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 192B 2019-03-10 ... 2019-03-10T23:00:00
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 264B 58.0 57.75 57.5 ... 50.5 50.25 50.0
  * longitude   (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
    valid_time  (time) datetime64[ns] 192B ...
Data variables:
    t2m         (time, latitude, longitude) float32 155kB 278.3 278.5 ... 279.0
Attributes:
    GRIB_edition:            1
    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:                 2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • time: 24
  • latitude: 33
  • longitude: 49
• Coordinates: (7)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2019-03-10 ... 2019-03-10T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2019-03-10T00:00:00.000000000', '2019-03-10T01:00:00.000000000',
           '2019-03-10T02:00:00.000000000', '2019-03-10T03:00:00.000000000',
           '2019-03-10T04:00:00.000000000', '2019-03-10T05:00:00.000000000',
           '2019-03-10T06:00:00.000000000', '2019-03-10T07:00:00.000000000',
           '2019-03-10T08:00:00.000000000', '2019-03-10T09:00:00.000000000',
           '2019-03-10T10:00:00.000000000', '2019-03-10T11:00:00.000000000',
           '2019-03-10T12:00:00.000000000', '2019-03-10T13:00:00.000000000',
           '2019-03-10T14:00:00.000000000', '2019-03-10T15:00:00.000000000',
           '2019-03-10T16:00:00.000000000', '2019-03-10T17:00:00.000000000',
           '2019-03-10T18:00:00.000000000', '2019-03-10T19:00:00.000000000',
           '2019-03-10T20:00:00.000000000', '2019-03-10T21:00:00.000000000',
           '2019-03-10T22:00:00.000000000', '2019-03-10T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    (latitude)
    float64
    58.0 57.75 57.5 ... 50.5 50.25 50.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
           55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
           53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
           50.5 , 50.25, 50.  ])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 1.5 1.75 2.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
            -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
            -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
            -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
            -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
             1.25,   1.5 ,   1.75,   2.  ])

  • valid_time
    (time)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [24 values with dtype=datetime64[ns]]

• Data variables: (1)
  • t2m
    (time, latitude, longitude)
    float32
    278.3 278.5 278.7 ... 279.2 279.0

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    1617

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    49

    GRIB_Ny :

    33

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    58.0

    GRIB_latitudeOfLastGridPointInDegrees :

    50.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    2.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    meditwin :

    mediterranean twin summer school

    array([[[278.31934, 278.49316, ..., 277.36426, 277.18457],
            [278.06152, 278.2373 , ..., 278.01855, 277.81934],
            ...,
            [284.44238, 284.57715, ..., 281.77246, 281.43848],
            [284.5713 , 284.6963 , ..., 281.84668, 281.77246]],
    
           [[277.9746 , 278.13867, ..., 277.54688, 277.3379 ],
            [277.5996 , 277.70117, ..., 278.1836 , 278.01172],
            ...,
            [283.60156, 283.69922, ..., 281.45898, 281.10938],
            [283.7129 , 283.7871 , ..., 281.58984, 281.45312]],
    
           ...,
    
           [[277.20483, 277.1853 , ..., 278.13843, 278.20288],
            [277.26147, 277.28882, ..., 277.95093, 277.97437],
            ...,
            [281.87476, 281.85718, ..., 279.4314 , 278.77905],
            [282.00757, 281.9646 , ..., 279.01538, 278.89624]],
    
           [[277.70557, 277.5923 , ..., 278.27588, 278.34814],
            [277.69775, 277.5005 , ..., 278.0083 , 278.0005 ],
            ...,
            [282.0337 , 281.99463, ..., 279.85986, 279.23682],
            [282.1548 , 282.10205, ..., 279.16064, 279.0122 ]]],
          shape=(24, 33, 49), dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-03-10 00:00:00', '2019-03-10 01:00:00',
                   '2019-03-10 02:00:00', '2019-03-10 03:00:00',
                   '2019-03-10 04:00:00', '2019-03-10 05:00:00',
                   '2019-03-10 06:00:00', '2019-03-10 07:00:00',
                   '2019-03-10 08:00:00', '2019-03-10 09:00:00',
                   '2019-03-10 10:00:00', '2019-03-10 11:00:00',
                   '2019-03-10 12:00:00', '2019-03-10 13:00:00',
                   '2019-03-10 14:00:00', '2019-03-10 15:00:00',
                   '2019-03-10 16:00:00', '2019-03-10 17:00:00',
                   '2019-03-10 18:00:00', '2019-03-10 19:00:00',
                   '2019-03-10 20:00:00', '2019-03-10 21:00:00',
                   '2019-03-10 22:00:00', '2019-03-10 23:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0, 55.75,
            55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5, 53.25,
            53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25,  51.0, 50.75,
            50.5, 50.25,  50.0],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
            -7.5, -7.25,  -7.0, -6.75,  -6.5, -6.25,  -6.0, -5.75,  -5.5, -5.25,
            -5.0, -4.75,  -4.5, -4.25,  -4.0, -3.75,  -3.5, -3.25,  -3.0, -2.75,
            -2.5, -2.25,  -2.0, -1.75,  -1.5, -1.25,  -1.0, -0.75,  -0.5, -0.25,
             0.0,  0.25,   0.5,  0.75,   1.0,  1.25,   1.5,  1.75,   2.0],
          dtype='float64', name='longitude'))

• Attributes: (7)

  GRIB_edition :

  1

  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 :

  2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.15.0/ecCodes-2.41.0 with {"source": "../../../../../../Library/Caches/xarray_tutorial_data/fbb9a0d5090f059b49c88c719140d7ae-era5-2mt-2019-03-uk.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

ds.sel(time=slice("2019-03-02", "2019-03-04"))

<xarray.Dataset> Size: 468kB
Dimensions:     (time: 72, latitude: 33, longitude: 49)
Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 576B 2019-03-02 ... 2019-03-04T23:00:00
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 264B 58.0 57.75 57.5 ... 50.5 50.25 50.0
  * longitude   (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
    valid_time  (time) datetime64[ns] 576B ...
Data variables:
    t2m         (time, latitude, longitude) float32 466kB 281.7 281.8 ... 278.5
Attributes:
    GRIB_edition:            1
    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:                 2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • time: 72
  • latitude: 33
  • longitude: 49
• Coordinates: (7)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2019-03-02 ... 2019-03-04T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2019-03-02T00:00:00.000000000', '2019-03-02T01:00:00.000000000',
           '2019-03-02T02:00:00.000000000', '2019-03-02T03:00:00.000000000',
           '2019-03-02T04:00:00.000000000', '2019-03-02T05:00:00.000000000',
           '2019-03-02T06:00:00.000000000', '2019-03-02T07:00:00.000000000',
           '2019-03-02T08:00:00.000000000', '2019-03-02T09:00:00.000000000',
           '2019-03-02T10:00:00.000000000', '2019-03-02T11:00:00.000000000',
           '2019-03-02T12:00:00.000000000', '2019-03-02T13:00:00.000000000',
           '2019-03-02T14:00:00.000000000', '2019-03-02T15:00:00.000000000',
           '2019-03-02T16:00:00.000000000', '2019-03-02T17:00:00.000000000',
           '2019-03-02T18:00:00.000000000', '2019-03-02T19:00:00.000000000',
           '2019-03-02T20:00:00.000000000', '2019-03-02T21:00:00.000000000',
           '2019-03-02T22:00:00.000000000', '2019-03-02T23:00:00.000000000',
           '2019-03-03T00:00:00.000000000', '2019-03-03T01:00:00.000000000',
           '2019-03-03T02:00:00.000000000', '2019-03-03T03:00:00.000000000',
           '2019-03-03T04:00:00.000000000', '2019-03-03T05:00:00.000000000',
           '2019-03-03T06:00:00.000000000', '2019-03-03T07:00:00.000000000',
           '2019-03-03T08:00:00.000000000', '2019-03-03T09:00:00.000000000',
           '2019-03-03T10:00:00.000000000', '2019-03-03T11:00:00.000000000',
           '2019-03-03T12:00:00.000000000', '2019-03-03T13:00:00.000000000',
           '2019-03-03T14:00:00.000000000', '2019-03-03T15:00:00.000000000',
           '2019-03-03T16:00:00.000000000', '2019-03-03T17:00:00.000000000',
           '2019-03-03T18:00:00.000000000', '2019-03-03T19:00:00.000000000',
           '2019-03-03T20:00:00.000000000', '2019-03-03T21:00:00.000000000',
           '2019-03-03T22:00:00.000000000', '2019-03-03T23:00:00.000000000',
           '2019-03-04T00:00:00.000000000', '2019-03-04T01:00:00.000000000',
           '2019-03-04T02:00:00.000000000', '2019-03-04T03:00:00.000000000',
           '2019-03-04T04:00:00.000000000', '2019-03-04T05:00:00.000000000',
           '2019-03-04T06:00:00.000000000', '2019-03-04T07:00:00.000000000',
           '2019-03-04T08:00:00.000000000', '2019-03-04T09:00:00.000000000',
           '2019-03-04T10:00:00.000000000', '2019-03-04T11:00:00.000000000',
           '2019-03-04T12:00:00.000000000', '2019-03-04T13:00:00.000000000',
           '2019-03-04T14:00:00.000000000', '2019-03-04T15:00:00.000000000',
           '2019-03-04T16:00:00.000000000', '2019-03-04T17:00:00.000000000',
           '2019-03-04T18:00:00.000000000', '2019-03-04T19:00:00.000000000',
           '2019-03-04T20:00:00.000000000', '2019-03-04T21:00:00.000000000',
           '2019-03-04T22:00:00.000000000', '2019-03-04T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    (latitude)
    float64
    58.0 57.75 57.5 ... 50.5 50.25 50.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
           55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
           53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
           50.5 , 50.25, 50.  ])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 1.5 1.75 2.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
            -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
            -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
            -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
            -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
             1.25,   1.5 ,   1.75,   2.  ])

  • valid_time
    (time)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [72 values with dtype=datetime64[ns]]

• Data variables: (1)
  • t2m
    (time, latitude, longitude)
    float32
    281.7 281.8 281.9 ... 278.6 278.5

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    1617

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    49

    GRIB_Ny :

    33

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    58.0

    GRIB_latitudeOfLastGridPointInDegrees :

    50.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    2.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    meditwin :

    mediterranean twin summer school

    array([[[281.70813, 281.7765 , ..., 279.60657, 279.5343 ],
            [281.69836, 281.74524, ..., 279.58704, 279.4015 ],
            ...,
            [283.73743, 283.72766, ..., 280.98938, 280.64172],
            [283.74915, 283.65344, ..., 280.9093 , 280.84485]],
    
           [[281.59692, 281.6438 , ..., 279.68677, 279.50708],
            [281.58325, 281.6184 , ..., 279.7395 , 279.49146],
            ...,
            [283.78638, 283.71802, ..., 281.12036, 280.75708],
            [283.81567, 283.71997, ..., 281.12817, 281.04224]],
    
           ...,
    
           [[280.86572, 280.89502, ..., 279.85596, 279.8579 ],
            [280.83643, 280.84424, ..., 279.82666, 279.7954 ],
            ...,
            [282.17236, 282.1333 , ..., 279.01025, 278.52588],
            [281.9048 , 281.8501 , ..., 278.68604, 278.7036 ]],
    
           [[280.9026 , 280.92993, ..., 279.83618, 279.90454],
            [280.87524, 280.86743, ..., 279.86353, 279.8733 ],
            ...,
            [282.3186 , 282.31665, ..., 278.97485, 278.45532],
            [282.28345, 282.21704, ..., 278.5647 , 278.5022 ]]],
          shape=(72, 33, 49), dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-03-02 00:00:00', '2019-03-02 01:00:00',
                   '2019-03-02 02:00:00', '2019-03-02 03:00:00',
                   '2019-03-02 04:00:00', '2019-03-02 05:00:00',
                   '2019-03-02 06:00:00', '2019-03-02 07:00:00',
                   '2019-03-02 08:00:00', '2019-03-02 09:00:00',
                   '2019-03-02 10:00:00', '2019-03-02 11:00:00',
                   '2019-03-02 12:00:00', '2019-03-02 13:00:00',
                   '2019-03-02 14:00:00', '2019-03-02 15:00:00',
                   '2019-03-02 16:00:00', '2019-03-02 17:00:00',
                   '2019-03-02 18:00:00', '2019-03-02 19:00:00',
                   '2019-03-02 20:00:00', '2019-03-02 21:00:00',
                   '2019-03-02 22:00:00', '2019-03-02 23:00:00',
                   '2019-03-03 00:00:00', '2019-03-03 01:00:00',
                   '2019-03-03 02:00:00', '2019-03-03 03:00:00',
                   '2019-03-03 04:00:00', '2019-03-03 05:00:00',
                   '2019-03-03 06:00:00', '2019-03-03 07:00:00',
                   '2019-03-03 08:00:00', '2019-03-03 09:00:00',
                   '2019-03-03 10:00:00', '2019-03-03 11:00:00',
                   '2019-03-03 12:00:00', '2019-03-03 13:00:00',
                   '2019-03-03 14:00:00', '2019-03-03 15:00:00',
                   '2019-03-03 16:00:00', '2019-03-03 17:00:00',
                   '2019-03-03 18:00:00', '2019-03-03 19:00:00',
                   '2019-03-03 20:00:00', '2019-03-03 21:00:00',
                   '2019-03-03 22:00:00', '2019-03-03 23:00:00',
                   '2019-03-04 00:00:00', '2019-03-04 01:00:00',
                   '2019-03-04 02:00:00', '2019-03-04 03:00:00',
                   '2019-03-04 04:00:00', '2019-03-04 05:00:00',
                   '2019-03-04 06:00:00', '2019-03-04 07:00:00',
                   '2019-03-04 08:00:00', '2019-03-04 09:00:00',
                   '2019-03-04 10:00:00', '2019-03-04 11:00:00',
                   '2019-03-04 12:00:00', '2019-03-04 13:00:00',
                   '2019-03-04 14:00:00', '2019-03-04 15:00:00',
                   '2019-03-04 16:00:00', '2019-03-04 17:00:00',
                   '2019-03-04 18:00:00', '2019-03-04 19:00:00',
                   '2019-03-04 20:00:00', '2019-03-04 21:00:00',
                   '2019-03-04 22:00:00', '2019-03-04 23:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0, 55.75,
            55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5, 53.25,
            53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25,  51.0, 50.75,
            50.5, 50.25,  50.0],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
            -7.5, -7.25,  -7.0, -6.75,  -6.5, -6.25,  -6.0, -5.75,  -5.5, -5.25,
            -5.0, -4.75,  -4.5, -4.25,  -4.0, -3.75,  -3.5, -3.25,  -3.0, -2.75,
            -2.5, -2.25,  -2.0, -1.75,  -1.5, -1.25,  -1.0, -0.75,  -0.5, -0.25,
             0.0,  0.25,   0.5,  0.75,   1.0,  1.25,   1.5,  1.75,   2.0],
          dtype='float64', name='longitude'))

• Attributes: (7)

  GRIB_edition :

  1

  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 :

  2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.15.0/ecCodes-2.41.0 with {"source": "../../../../../../Library/Caches/xarray_tutorial_data/fbb9a0d5090f059b49c88c719140d7ae-era5-2mt-2019-03-uk.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

ds.sel(longitude=58.5, method="nearest")

<xarray.Dataset> Size: 110kB
Dimensions:     (time: 744, latitude: 33)
Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 6kB 2019-03-01 ... 2019-03-31T23:00:00
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 264B 58.0 57.75 57.5 ... 50.5 50.25 50.0
    longitude   float64 8B 2.0
    valid_time  (time) datetime64[ns] 6kB ...
Data variables:
    t2m         (time, latitude) float32 98kB 279.4 279.3 279.2 ... 281.3 281.5
Attributes:
    GRIB_edition:            1
    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:                 2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • time: 744
  • latitude: 33
• Coordinates: (7)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2019-03-01 ... 2019-03-31T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2019-03-01T00:00:00.000000000', '2019-03-01T01:00:00.000000000',
           '2019-03-01T02:00:00.000000000', ..., '2019-03-31T21:00:00.000000000',
           '2019-03-31T22:00:00.000000000', '2019-03-31T23:00:00.000000000'],
          shape=(744,), dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    (latitude)
    float64
    58.0 57.75 57.5 ... 50.5 50.25 50.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
           55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
           53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
           50.5 , 50.25, 50.  ])

  • longitude
    ()
    float64
    2.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array(2.)

  • valid_time
    (time)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [744 values with dtype=datetime64[ns]]

• Data variables: (1)
  • t2m
    (time, latitude)
    float32
    279.4 279.3 279.2 ... 281.3 281.5

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    1617

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    49

    GRIB_Ny :

    33

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    58.0

    GRIB_latitudeOfLastGridPointInDegrees :

    50.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    2.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    meditwin :

    mediterranean twin summer school

    array([[279.40918, 279.26074, 279.18457, ..., 281.8545 , 281.9287 , 282.08887],
           [279.43872, 279.3274 , 279.20435, ..., 281.78442, 281.88013, 281.9524 ],
           [279.49854, 279.45557, 279.36768, ..., 281.82275, 281.89307, 281.75244],
           ...,
           [278.11108, 278.01733, 277.97046, ..., 281.86694, 282.59155, 282.98218],
           [277.95142, 277.8518 , 277.9104 , ..., 281.3772 , 281.92017, 282.12134],
           [277.9685 , 277.88843, 277.9607 , ..., 280.86108, 281.28882, 281.45483]],
          shape=(744, 33), dtype=float32)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-03-01 00:00:00', '2019-03-01 01:00:00',
                   '2019-03-01 02:00:00', '2019-03-01 03:00:00',
                   '2019-03-01 04:00:00', '2019-03-01 05:00:00',
                   '2019-03-01 06:00:00', '2019-03-01 07:00:00',
                   '2019-03-01 08:00:00', '2019-03-01 09:00:00',
                   ...
                   '2019-03-31 14:00:00', '2019-03-31 15:00:00',
                   '2019-03-31 16:00:00', '2019-03-31 17:00:00',
                   '2019-03-31 18:00:00', '2019-03-31 19:00:00',
                   '2019-03-31 20:00:00', '2019-03-31 21:00:00',
                   '2019-03-31 22:00:00', '2019-03-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=744, freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0, 55.75,
            55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5, 53.25,
            53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25,  51.0, 50.75,
            50.5, 50.25,  50.0],
          dtype='float64', name='latitude'))

• Attributes: (7)

  GRIB_edition :

  1

  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 :

  2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.15.0/ecCodes-2.41.0 with {"source": "../../../../../../Library/Caches/xarray_tutorial_data/fbb9a0d5090f059b49c88c719140d7ae-era5-2mt-2019-03-uk.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

ds.t2m.data[0,0,0]

np.float32(282.4248)

ds.t2m.isel(time = 0, latitude = 0, longitude = 0)

<xarray.DataArray 't2m' ()> Size: 4B
array(282.4248, dtype=float32)
Coordinates:
    number      int64 8B ...
    time        datetime64[ns] 8B 2019-03-01
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
    latitude    float64 8B 58.0
    longitude   float64 8B -10.0
    valid_time  datetime64[ns] 8B ...
Attributes: (12/32)
    GRIB_paramId:                             167
    GRIB_dataType:                            an
    GRIB_numberOfPoints:                      1617
    GRIB_typeOfLevel:                         surface
    GRIB_stepUnits:                           1
    GRIB_stepType:                            instant
    ...                                       ...
    GRIB_totalNumber:                         0
    GRIB_units:                               K
    long_name:                                2 metre temperature
    units:                                    K
    standard_name:                            unknown
    meditwin:                                 mediterranean twin summer school

xarray.DataArray

't2m'

• 282.4

  array(282.4248, dtype=float32)

• Coordinates: (7)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    ()
    datetime64[ns]
    2019-03-01

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array('2019-03-01T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    ()
    float64
    58.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array(58.)

  • longitude
    ()
    float64
    -10.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array(-10.)

  • valid_time
    ()
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [1 values with dtype=datetime64[ns]]

• Indexes: (0)
• Attributes: (32)

  GRIB_paramId :

  167

  GRIB_dataType :

  an

  GRIB_numberOfPoints :

  1617

  GRIB_typeOfLevel :

  surface

  GRIB_stepUnits :

  1

  GRIB_stepType :

  instant

  GRIB_gridType :

  regular_ll

  GRIB_uvRelativeToGrid :

  0

  GRIB_NV :

  0

  GRIB_Nx :

  49

  GRIB_Ny :

  33

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  t2m

  GRIB_gridDefinitionDescription :

  Latitude/Longitude Grid

  GRIB_iDirectionIncrementInDegrees :

  0.25

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.25

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  58.0

  GRIB_latitudeOfLastGridPointInDegrees :

  50.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  -10.0

  GRIB_longitudeOfLastGridPointInDegrees :

  2.0

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  2 metre temperature

  GRIB_shortName :

  2t

  GRIB_totalNumber :

  0

  GRIB_units :

  K

  long_name :

  2 metre temperature

  units :

  K

  standard_name :

  unknown

  meditwin :

  mediterranean twin summer school

ds.groupby("time.day")

<DatasetGroupBy, grouped over 1 grouper(s), 31 groups in total:
    'day': 31/31 groups present with labels 1, 2, 3, 4, 5, 6, ..., 27, 28, 29, 30, 31>

daily_mean = ds.groupby("time.day").mean()
daily_mean

<xarray.Dataset> Size: 201kB
Dimensions:    (day: 31, latitude: 33, longitude: 49)
Coordinates:
    number     int64 8B 0
    step       timedelta64[ns] 8B 00:00:00
    surface    float64 8B 0.0
  * latitude   (latitude) float64 264B 58.0 57.75 57.5 57.25 ... 50.5 50.25 50.0
  * longitude  (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
  * day        (day) int64 248B 1 2 3 4 5 6 7 8 9 ... 23 24 25 26 27 28 29 30 31
Data variables:
    t2m        (day, latitude, longitude) float32 201kB 282.6 282.6 ... 284.3
Attributes:
    GRIB_edition:            1
    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:                 2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • day: 31
  • latitude: 33
  • longitude: 49
• Coordinates: (6)
  • number
    ()
    int64
    0

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    array(0)

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

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

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

  • surface
    ()
    float64
    0.0

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    array(0.)

  • latitude
    (latitude)
    float64
    58.0 57.75 57.5 ... 50.5 50.25 50.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
           55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
           53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
           50.5 , 50.25, 50.  ])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 1.5 1.75 2.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
            -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
            -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
            -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
            -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
             1.25,   1.5 ,   1.75,   2.  ])

  • day
    (day)
    int64
    1 2 3 4 5 6 7 ... 26 27 28 29 30 31

    array([ 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])

• Data variables: (1)
  • t2m
    (day, latitude, longitude)
    float32
    282.6 282.6 282.6 ... 284.3 284.3

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    1617

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    49

    GRIB_Ny :

    33

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    58.0

    GRIB_latitudeOfLastGridPointInDegrees :

    50.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    2.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    meditwin :

    mediterranean twin summer school

    array([[[282.64688, 282.64532, 282.64426, ..., 279.42715, 279.4343 ,
             279.43683],
            [282.67447, 282.67032, 282.6664 , ..., 279.4982 , 279.48825,
             279.47867],
            [282.74088, 282.74313, 282.74664, ..., 279.51944, 279.49167,
             279.4676 ],
            ...,
            [284.09732, 284.00192, 283.8986 , ..., 281.6794 , 281.45694,
             281.0668 ],
            [284.12115, 284.02887, 283.94553, ..., 281.7791 , 281.57697,
             281.2797 ],
            [284.1148 , 284.04245, 283.95865, ..., 281.54514, 281.492  ,
             281.49216]],
    
           [[281.38705, 281.42715, 281.47095, ..., 280.754  , 280.72144,
             280.683  ],
            [281.4619 , 281.4916 , 281.52075, ..., 280.81723, 280.75912,
             280.70117],
            [281.58627, 281.60645, 281.6245 , ..., 280.84976, 280.78696,
             280.72485],
    ...
            [281.92288, 281.85852, 281.7931 , ..., 284.96243, 285.7423 ,
             285.72897],
            [281.8839 , 281.83313, 281.79684, ..., 285.09128, 285.8341 ,
             285.60617],
            [281.87863, 281.88074, 281.8442 , ..., 285.58426, 285.55286,
             285.38293]],
    
           [[280.86697, 280.86526, 280.8674 , ..., 277.78064, 277.76096,
             277.7367 ],
            [280.84143, 280.82883, 280.81522, ..., 277.88318, 277.84476,
             277.80545],
            [280.8485 , 280.8154 , 280.77737, ..., 277.96927, 277.943  ,
             277.91336],
            ...,
            [283.0659 , 283.0076 , 282.96048, ..., 283.63318, 283.66956,
             283.29782],
            [283.17868, 283.11942, 283.06955, ..., 283.92566, 283.94315,
             283.6384 ],
            [283.20578, 283.17444, 283.12463, ..., 284.18738, 284.3088 ,
             284.31555]]], shape=(31, 33, 49), dtype=float32)

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0, 55.75,
            55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5, 53.25,
            53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25,  51.0, 50.75,
            50.5, 50.25,  50.0],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
            -7.5, -7.25,  -7.0, -6.75,  -6.5, -6.25,  -6.0, -5.75,  -5.5, -5.25,
            -5.0, -4.75,  -4.5, -4.25,  -4.0, -3.75,  -3.5, -3.25,  -3.0, -2.75,
            -2.5, -2.25,  -2.0, -1.75,  -1.5, -1.25,  -1.0, -0.75,  -0.5, -0.25,
             0.0,  0.25,   0.5,  0.75,   1.0,  1.25,   1.5,  1.75,   2.0],
          dtype='float64', name='longitude'))

  • day
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int64', name='day'))

• Attributes: (7)

  GRIB_edition :

  1

  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 :

  2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.15.0/ecCodes-2.41.0 with {"source": "../../../../../../Library/Caches/xarray_tutorial_data/fbb9a0d5090f059b49c88c719140d7ae-era5-2mt-2019-03-uk.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

daily_mean.t2m.plot(col="day", col_wrap=3, cmap="viridis")

<xarray.plot.facetgrid.FacetGrid at 0x1517397f0>

daily_mean.t2m.plot.contour(col="day",col_wrap=3, cmap="viridis",levels=20,add_colorbar=True)

<xarray.plot.facetgrid.FacetGrid at 0x1537dad50>

Xarray Read and Write

ds.to_netcdf("../data/era5-test-data.nc")

ds = xr.open_dataset("../data/era5-test-data.nc")
ds

/var/folders/j3/513qxyhx4l30byl48tz1k1jr0000gn/T/ipykernel_70205/1144642969.py:1: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  ds = xr.open_dataset("../data/era5-test-data.nc")

<xarray.Dataset> Size: 5MB
Dimensions:     (time: 744, latitude: 33, longitude: 49)
Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 6kB 2019-03-01 ... 2019-03-31T23:00:00
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 264B 58.0 57.75 57.5 ... 50.5 50.25 50.0
  * longitude   (longitude) float64 392B -10.0 -9.75 -9.5 -9.25 ... 1.5 1.75 2.0
    valid_time  (time) datetime64[ns] 6kB ...
Data variables:
    t2m         (time, latitude, longitude) float32 5MB ...
Attributes:
    GRIB_edition:            1
    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:                 2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • time: 744
  • latitude: 33
  • longitude: 49
• Coordinates: (7)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2019-03-01 ... 2019-03-31T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2019-03-01T00:00:00.000000000', '2019-03-01T01:00:00.000000000',
           '2019-03-01T02:00:00.000000000', ..., '2019-03-31T21:00:00.000000000',
           '2019-03-31T22:00:00.000000000', '2019-03-31T23:00:00.000000000'],
          shape=(744,), dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    (latitude)
    float64
    58.0 57.75 57.5 ... 50.5 50.25 50.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75,
           55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25,
           53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75,
           50.5 , 50.25, 50.  ])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 1.5 1.75 2.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-10.  ,  -9.75,  -9.5 ,  -9.25,  -9.  ,  -8.75,  -8.5 ,  -8.25,  -8.  ,
            -7.75,  -7.5 ,  -7.25,  -7.  ,  -6.75,  -6.5 ,  -6.25,  -6.  ,  -5.75,
            -5.5 ,  -5.25,  -5.  ,  -4.75,  -4.5 ,  -4.25,  -4.  ,  -3.75,  -3.5 ,
            -3.25,  -3.  ,  -2.75,  -2.5 ,  -2.25,  -2.  ,  -1.75,  -1.5 ,  -1.25,
            -1.  ,  -0.75,  -0.5 ,  -0.25,   0.  ,   0.25,   0.5 ,   0.75,   1.  ,
             1.25,   1.5 ,   1.75,   2.  ])

  • valid_time
    (time)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [744 values with dtype=datetime64[ns]]

• Data variables: (1)
  • t2m
    (time, latitude, longitude)
    float32
    ...

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    1617

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    49

    GRIB_Ny :

    33

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    58.0

    GRIB_latitudeOfLastGridPointInDegrees :

    50.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    2.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    meditwin :

    mediterranean twin summer school

    [1203048 values with dtype=float32]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-03-01 00:00:00', '2019-03-01 01:00:00',
                   '2019-03-01 02:00:00', '2019-03-01 03:00:00',
                   '2019-03-01 04:00:00', '2019-03-01 05:00:00',
                   '2019-03-01 06:00:00', '2019-03-01 07:00:00',
                   '2019-03-01 08:00:00', '2019-03-01 09:00:00',
                   ...
                   '2019-03-31 14:00:00', '2019-03-31 15:00:00',
                   '2019-03-31 16:00:00', '2019-03-31 17:00:00',
                   '2019-03-31 18:00:00', '2019-03-31 19:00:00',
                   '2019-03-31 20:00:00', '2019-03-31 21:00:00',
                   '2019-03-31 22:00:00', '2019-03-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=744, freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0, 55.75,
            55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5, 53.25,
            53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25,  51.0, 50.75,
            50.5, 50.25,  50.0],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
            -7.5, -7.25,  -7.0, -6.75,  -6.5, -6.25,  -6.0, -5.75,  -5.5, -5.25,
            -5.0, -4.75,  -4.5, -4.25,  -4.0, -3.75,  -3.5, -3.25,  -3.0, -2.75,
            -2.5, -2.25,  -2.0, -1.75,  -1.5, -1.25,  -1.0, -0.75,  -0.5, -0.25,
             0.0,  0.25,   0.5,  0.75,   1.0,  1.25,   1.5,  1.75,   2.0],
          dtype='float64', name='longitude'))

• Attributes: (7)

  GRIB_edition :

  1

  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 :

  2025-06-23T11:08 GRIB to CDM+CF via cfgrib-0.9.15.0/ecCodes-2.41.0 with {"source": "../../../../../../Library/Caches/xarray_tutorial_data/fbb9a0d5090f059b49c88c719140d7ae-era5-2mt-2019-03-uk.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}