Calculate Daily Means

This notebook demonstrates how to calculate the daily mean of some sample 2m temperature from ERA5 data using earthkit-data and earthkit-transforms. Daily means are useful for summarizing hourly records into daily values for climate analysis or downstream workflows.

from earthkit import data as ekd
from earthkit import transforms as ekt
from earthkit.transforms._tools import earthkit_remote_test_data_file

remote_era5_file = earthkit_remote_test_data_file("era5-Europe-sfc-2m-temperature-3deg-2015-2017.grib")
era5_data = ekd.from_source("url", remote_era5_file)
ds = era5_data.to_xarray()

# Calculate daily mean
daily_mean = ekt.temporal.daily_mean(ds)

daily_mean

<xarray.Dataset> Size: 5MB
Dimensions:                  (forecast_reference_time: 1096, latitude: 19,
                              longitude: 29)
Coordinates:
  * forecast_reference_time  (forecast_reference_time) datetime64[us] 9kB 201...
  * latitude                 (latitude) float64 152B 79.0 76.0 ... 28.0 25.0
  * longitude                (longitude) float64 232B -25.0 -22.0 ... 56.0 59.0
Data variables:
    2t                       (forecast_reference_time, latitude, longitude) float64 5MB ...
Attributes:
    Conventions:  CF-1.8
    institution:  ECMWF

xarray.Dataset

• Dimensions:
  • forecast_reference_time: 1096
  • latitude: 19
  • longitude: 29
• Coordinates: (3)
  • forecast_reference_time
    (forecast_reference_time)
    datetime64[us]
    2015-01-01 ... 2017-12-31

    standard_name :

    forecast_reference_time

    long_name :

    initial time of forecast

    array(['2015-01-01T00:00:00.000000', '2015-01-02T00:00:00.000000',
           '2015-01-03T00:00:00.000000', ..., '2017-12-29T00:00:00.000000',
           '2017-12-30T00:00:00.000000', '2017-12-31T00:00:00.000000'],
          shape=(1096,), dtype='datetime64[us]')

  • latitude
    (latitude)
    float64
    79.0 76.0 73.0 ... 31.0 28.0 25.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([79., 76., 73., 70., 67., 64., 61., 58., 55., 52., 49., 46., 43., 40.,
           37., 34., 31., 28., 25.])

  • longitude
    (longitude)
    float64
    -25.0 -22.0 -19.0 ... 56.0 59.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-25., -22., -19., -16., -13., -10.,  -7.,  -4.,  -1.,   2.,   5.,   8.,
            11.,  14.,  17.,  20.,  23.,  26.,  29.,  32.,  35.,  38.,  41.,  44.,
            47.,  50.,  53.,  56.,  59.])

• Data variables: (1)
  • 2t
    (forecast_reference_time, latitude, longitude)
    float64
    247.1 252.6 250.4 ... 294.1 294.8

    standard_name :

    unknown

    long_name :

    2 metre temperature

    units :

    kelvin

    level_type :

    surface

    _earthkit :

    {"message": {"__bytes_b64__": "R1JJQgAAbAEAADSAYv//gKcBAAAPAQEAAAEAAAAAAAAVAAAAAAAAAAAAAAAAAAAAARcCBAEwMDAxAAAAAAAgAP8AAB0AEwE0mIBhqIAAYagA5ngLuAu4AAAAAAAAAAwAgAJEuX1uAAA3Nzc3"}, "bitsPerValue": 16, "grid_spec": {"area": [79, -25, 25, 59], "grid": [3, 3], "reference": [2, 1]}}

    array([[[247.09197998, 252.55828857, 250.38641357, ..., 264.21502686,
             263.68572998, 261.83660889],
            [244.34100342, 247.40203857, 254.18963623, ..., 265.75946045,
             265.04852295, 261.72821045],
            [246.31219482, 250.63397217, 256.11737061, ..., 253.54217529,
             249.0836792 , 249.04901123],
            ...,
            [291.58074951, 291.52362061, 291.53485107, ..., 278.82000732,
             280.15301514, 285.26287842],
            [292.74432373, 292.74725342, 292.49383545, ..., 289.37127686,
             287.54998779, 288.01483154],
            [293.76043701, 293.20281982, 292.54656982, ..., 295.22235107,
             293.18426514, 295.45672607]],
    
           [[250.18432999, 255.68432999, 255.69995499, ..., 264.74097061,
             263.34595108, 260.62622452],
            [245.86548233, 248.74341202, 256.42651749, ..., 265.26587296,
             264.93335342, 261.37768936],
            [245.93823624, 250.96411514, 255.58813858, ..., 248.93970108,
             244.44116592, 245.40991592],
    ...
            [292.79867935, 292.48666763, 292.2400856 , ..., 280.05893326,
             282.04135513, 283.1829567 ],
            [293.3260231 , 293.25180435, 292.64291763, ..., 289.76156998,
             286.42074966, 286.73373795],
            [294.36362076, 293.93979263, 293.19711685, ..., 296.08432388,
             293.3157692 , 294.97543716]],
    
           [[241.04741287, 248.64458084, 252.42338943, ..., 262.17485428,
             262.41557693, 262.60210037],
            [243.00444412, 245.94145584, 251.31889725, ..., 269.57866287,
             268.96489334, 266.82426834],
            [242.34282303, 248.79497147, 252.36821365, ..., 269.57670975,
             262.4346199 , 266.36137772],
            ...,
            [292.87602615, 292.69682693, 292.33696365, ..., 279.30571365,
             283.30034256, 285.30815506],
            [293.59135818, 293.32817459, 293.02299881, ..., 288.58696365,
             286.05815506, 287.2314949 ],
            [294.30620193, 293.9478035 , 293.79106522, ..., 294.88237381,
             294.08989334, 294.75346756]]], shape=(1096, 19, 29))

• Attributes: (2)

  Conventions :

  CF-1.8

  institution :

  ECMWF