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TutorialsMultisource datasets integration: population weighted temperature

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Multisource datasets integration: population weighted temperature from ERA5 and

This notebook will provide you guidance on how to access and use the https://data.earthdatahub.destine.eu/era5/reanalysis-era5-single-levels-v0.zarr dataset on Earth Data Hub, together with the Hybrid gridded demographic data for the world dataset https://data.earthdatahub.destine.eu/derived-GPWv4-Histsoc/demographics-hybrid-1950-2020-15-min-v0.

ERA5 single level data contains variables describing the global climate and weather for the past 8 decades. Data is available from 1940 onwards on a regular lat-lon grid of 0.25 degrees.

The Hybrid gridded demographic totals for the world contains the 1950-2020 demographic given as 5-year population bands at a 0.25 degree grid resolution.

Goal of this tutorial

The goal of this tutorial is to visualize the area-averaged temperature in Europe as a function of time, and compare it with the population-averaged temperature.

What you will learn:

• how to access the data
• select and reduce the data
• use different datasets together
• plot the results

---

To access datasets on Earth Data Hub (EDH) you need to instruct your tools (xarray, Zarr, etc.) to use EDH personal access token when downloading the data.

To obtain a personal access token you first need to register to the Destination Earth platform platform. Then you can go to Earth Data Hub account settings where you can find your default personal access token or create others. After retrieving your personal access token, please cut and paste it below: ⤵

PAT = "your_personal_access_token"

# e.g. PAT="edh_pat_44bbb7e9192a4c6bb47ddf07d07564eee5d17de8dfc48f7118f88e3bc4a4157f8fe2403f5aa0a2d53441b6922ea9a33a"

Working with EDH data

Datasets on Earth Data Hub are often very large and remotely hosted. Typical use imply a selection of the data followed by one or more reduction steps to be performed in a local or distributed Dask environment.

The structure of a workflow that uses EDH data tipically looks like this:

• data access
• data selection
• (optional) data reduction
• data download
• further operations and visualization

Xarray and Dask work together following a lazy principle. This means that when you access and manipulate a Zarr store the data is in not immediately downloaded and loaded in memory. Instead, Dask constructs a task graph that represents the operations to be performed. A smart user will first reduce the amount of data that needs to be downloaded and explicitly call compute() on it. Once the compute() operation is complete the data is loaded into memory and available for subsequent fast processing.

Preparing the population data

1. Data access

To access the data, only the dataset metadata must be downloaded. Xarray does this automatically when you access a Zarr dataset.

import xarray as xr

world_population = xr.open_dataset(
    "https://hedp-data.bopen.eu/public/derived-GPWv4-Histsoc/demographics-hybrid-1950-2020-15-min-v0",
    chunks={},
    engine="zarr",
)
world_population = world_population.demographic_totals
world_population

<xarray.DataArray 'demographic_totals' (latitude: 720, longitude: 1440,
                                        age_band_lower_bound: 14, year: 71)> Size: 4GB
dask.array<open_dataset-demographic_totals, shape=(720, 1440, 14, 71), dtype=float32, chunksize=(64, 64, 14, 71), chunktype=numpy.ndarray>
Coordinates:
  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65
  * latitude              (latitude) float64 6kB 90.0 89.75 ... -89.5 -89.75
  * longitude             (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8
  * year                  (year) int64 568B 1950 1951 1952 ... 2018 2019 2020

xarray.DataArray

'demographic_totals'

• latitude: 720
• longitude: 1440
• age_band_lower_bound: 14
• year: 71

• dask.array<chunksize=(64, 64, 14, 71), meta=np.ndarray>

  Array Chunk Bytes 3.84 GiB 15.53 MiB Shape (720, 1440, 14, 71) (64, 64, 14, 71) Dask graph 276 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • age_band_lower_bound
    (age_band_lower_bound)
    int64
    0 5 10 15 20 25 ... 45 50 55 60 65

    array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65])

  • latitude
    (latitude)
    float64
    90.0 89.75 89.5 ... -89.5 -89.75

    array([ 90.  ,  89.75,  89.5 , ..., -89.25, -89.5 , -89.75])

  • longitude
    (longitude)
    float64
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02])

  • year
    (year)
    int64
    1950 1951 1952 ... 2018 2019 2020

    array([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,
           1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
           1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
           1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
           1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
           2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])

• Indexes: (4)
  • age_band_lower_bound
    PandasIndex

    PandasIndex(Index([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65], dtype='int64', name='age_band_lower_bound'))

  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
            -87.5, -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5,
           -89.75],
          dtype='float64', name='latitude', length=720))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float64', name='longitude', length=1440))

  • year
    PandasIndex

    PandasIndex(Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,
           1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
           1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
           1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
           1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
           2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],
          dtype='int64', name='year'))

• Attributes: (0)

The longitude of the dataset is set from 0 to 360 degrees, which makes it uncomfortable to deal with data in the European area when using xarray:

world_population.sel(age_band_lower_bound=0, year=2020).plot(vmax=50_000, cmap="YlGnBu_r")

<matplotlib.collections.QuadMesh at 0x7f571d6dcf50>

We can roll the longitude to a -180 to 180 extent:

xr.set_options(keep_attrs=True)

world_population = world_population.assign_coords(longitude=(((world_population.longitude + 180) % 360) - 180))
world_population = world_population.roll(longitude=int(len(world_population.longitude) / 2), roll_coords=True)
world_population

<xarray.DataArray 'demographic_totals' (latitude: 720, longitude: 1440,
                                        age_band_lower_bound: 14, year: 71)> Size: 4GB
dask.array<rechunk-merge, shape=(720, 1440, 14, 71), dtype=float32, chunksize=(64, 64, 14, 71), chunktype=numpy.ndarray>
Coordinates:
  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65
  * latitude              (latitude) float64 6kB 90.0 89.75 ... -89.5 -89.75
  * year                  (year) int64 568B 1950 1951 1952 ... 2018 2019 2020
  * longitude             (longitude) float64 12kB -180.0 -179.8 ... 179.5 179.8

xarray.DataArray

'demographic_totals'

• latitude: 720
• longitude: 1440
• age_band_lower_bound: 14
• year: 71

• dask.array<chunksize=(64, 64, 14, 71), meta=np.ndarray>

  Array Chunk Bytes 3.84 GiB 15.53 MiB Shape (720, 1440, 14, 71) (64, 64, 14, 71) Dask graph 276 chunks in 6 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • age_band_lower_bound
    (age_band_lower_bound)
    int64
    0 5 10 15 20 25 ... 45 50 55 60 65

    array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65])

  • latitude
    (latitude)
    float64
    90.0 89.75 89.5 ... -89.5 -89.75

    array([ 90.  ,  89.75,  89.5 , ..., -89.25, -89.5 , -89.75])

  • year
    (year)
    int64
    1950 1951 1952 ... 2018 2019 2020

    array([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,
           1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
           1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
           1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
           1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
           2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])

  • longitude
    (longitude)
    float64
    -180.0 -179.8 ... 179.5 179.8

    array([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75])

• Indexes: (4)
  • age_band_lower_bound
    PandasIndex

    PandasIndex(Index([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65], dtype='int64', name='age_band_lower_bound'))

  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
            -87.5, -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5,
           -89.75],
          dtype='float64', name='latitude', length=720))

  • year
    PandasIndex

    PandasIndex(Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,
           1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
           1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
           1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
           1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
           2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],
          dtype='int64', name='year'))

  • longitude
    PandasIndex

    PandasIndex(Index([ -180.0, -179.75,  -179.5, -179.25,  -179.0, -178.75,  -178.5, -178.25,
            -178.0, -177.75,
           ...
             177.5,  177.75,   178.0,  178.25,   178.5,  178.75,   179.0,  179.25,
             179.5,  179.75],
          dtype='float64', name='longitude', length=1440))

• Attributes: (0)

world_population.sel(age_band_lower_bound=0, year=2020).plot(vmax=50_000, cmap="YlGnBu_r")

<matplotlib.collections.QuadMesh at 0x7f571d72eb10>

2. Data selection

On the world data, we perform a geographical selection corresponding to continental Europe:

europe_population = world_population.sel(latitude=slice(71, 36), longitude=slice(-10,35))
europe_population

<xarray.DataArray 'demographic_totals' (latitude: 141, longitude: 181,
                                        age_band_lower_bound: 14, year: 71)> Size: 101MB
dask.array<getitem, shape=(141, 181, 14, 71), dtype=float32, chunksize=(64, 64, 14, 71), chunktype=numpy.ndarray>
Coordinates:
  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65
  * latitude              (latitude) float64 1kB 71.0 70.75 70.5 ... 36.25 36.0
  * year                  (year) int64 568B 1950 1951 1952 ... 2018 2019 2020
  * longitude             (longitude) float64 1kB -10.0 -9.75 ... 34.75 35.0

xarray.DataArray

'demographic_totals'

• latitude: 141
• longitude: 181
• age_band_lower_bound: 14
• year: 71

• dask.array<chunksize=(52, 24, 14, 71), meta=np.ndarray>

  Array Chunk Bytes 96.77 MiB 15.53 MiB Shape (141, 181, 14, 71) (64, 64, 14, 71) Dask graph 12 chunks in 7 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • age_band_lower_bound
    (age_band_lower_bound)
    int64
    0 5 10 15 20 25 ... 45 50 55 60 65

    array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65])

  • latitude
    (latitude)
    float64
    71.0 70.75 70.5 ... 36.5 36.25 36.0

    array([71.  , 70.75, 70.5 , 70.25, 70.  , 69.75, 69.5 , 69.25, 69.  , 68.75,
           68.5 , 68.25, 68.  , 67.75, 67.5 , 67.25, 67.  , 66.75, 66.5 , 66.25,
           66.  , 65.75, 65.5 , 65.25, 65.  , 64.75, 64.5 , 64.25, 64.  , 63.75,
           63.5 , 63.25, 63.  , 62.75, 62.5 , 62.25, 62.  , 61.75, 61.5 , 61.25,
           61.  , 60.75, 60.5 , 60.25, 60.  , 59.75, 59.5 , 59.25, 59.  , 58.75,
           58.5 , 58.25, 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.  , 49.75, 49.5 , 49.25, 49.  , 48.75,
           48.5 , 48.25, 48.  , 47.75, 47.5 , 47.25, 47.  , 46.75, 46.5 , 46.25,
           46.  , 45.75, 45.5 , 45.25, 45.  , 44.75, 44.5 , 44.25, 44.  , 43.75,
           43.5 , 43.25, 43.  , 42.75, 42.5 , 42.25, 42.  , 41.75, 41.5 , 41.25,
           41.  , 40.75, 40.5 , 40.25, 40.  , 39.75, 39.5 , 39.25, 39.  , 38.75,
           38.5 , 38.25, 38.  , 37.75, 37.5 , 37.25, 37.  , 36.75, 36.5 , 36.25,
           36.  ])

  • year
    (year)
    int64
    1950 1951 1952 ... 2018 2019 2020

    array([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,
           1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
           1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
           1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
           1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
           2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 34.75 35.0

    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.  ,   2.25,   2.5 ,   2.75,   3.  ,   3.25,
             3.5 ,   3.75,   4.  ,   4.25,   4.5 ,   4.75,   5.  ,   5.25,   5.5 ,
             5.75,   6.  ,   6.25,   6.5 ,   6.75,   7.  ,   7.25,   7.5 ,   7.75,
             8.  ,   8.25,   8.5 ,   8.75,   9.  ,   9.25,   9.5 ,   9.75,  10.  ,
            10.25,  10.5 ,  10.75,  11.  ,  11.25,  11.5 ,  11.75,  12.  ,  12.25,
            12.5 ,  12.75,  13.  ,  13.25,  13.5 ,  13.75,  14.  ,  14.25,  14.5 ,
            14.75,  15.  ,  15.25,  15.5 ,  15.75,  16.  ,  16.25,  16.5 ,  16.75,
            17.  ,  17.25,  17.5 ,  17.75,  18.  ,  18.25,  18.5 ,  18.75,  19.  ,
            19.25,  19.5 ,  19.75,  20.  ,  20.25,  20.5 ,  20.75,  21.  ,  21.25,
            21.5 ,  21.75,  22.  ,  22.25,  22.5 ,  22.75,  23.  ,  23.25,  23.5 ,
            23.75,  24.  ,  24.25,  24.5 ,  24.75,  25.  ,  25.25,  25.5 ,  25.75,
            26.  ,  26.25,  26.5 ,  26.75,  27.  ,  27.25,  27.5 ,  27.75,  28.  ,
            28.25,  28.5 ,  28.75,  29.  ,  29.25,  29.5 ,  29.75,  30.  ,  30.25,
            30.5 ,  30.75,  31.  ,  31.25,  31.5 ,  31.75,  32.  ,  32.25,  32.5 ,
            32.75,  33.  ,  33.25,  33.5 ,  33.75,  34.  ,  34.25,  34.5 ,  34.75,
            35.  ])

• Indexes: (4)
  • age_band_lower_bound
    PandasIndex

    PandasIndex(Index([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65], dtype='int64', name='age_band_lower_bound'))

  • latitude
    PandasIndex

    PandasIndex(Index([ 71.0, 70.75,  70.5, 70.25,  70.0, 69.75,  69.5, 69.25,  69.0, 68.75,
           ...
           38.25,  38.0, 37.75,  37.5, 37.25,  37.0, 36.75,  36.5, 36.25,  36.0],
          dtype='float64', name='latitude', length=141))

  • year
    PandasIndex

    PandasIndex(Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,
           1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
           1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
           1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
           1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
           2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],
          dtype='int64', name='year'))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
           ...
           32.75,  33.0, 33.25,  33.5, 33.75,  34.0, 34.25,  34.5, 34.75,  35.0],
          dtype='float64', name='longitude', length=181))

• Attributes: (0)

This is a relatively small dataset, we don't need to worry too much about the download size.

3. Data reduction

Next, we calculate the total population for each and every year. Notice that the data spans fron 1950 to 2020.

europe_population_bands_total = europe_population.sum("age_band_lower_bound")
europe_population_bands_total

<xarray.DataArray 'demographic_totals' (latitude: 141, longitude: 181, year: 71)> Size: 7MB
dask.array<sum-aggregate, shape=(141, 181, 71), dtype=float32, chunksize=(64, 64, 71), chunktype=numpy.ndarray>
Coordinates:
  * latitude   (latitude) float64 1kB 71.0 70.75 70.5 70.25 ... 36.5 36.25 36.0
  * year       (year) int64 568B 1950 1951 1952 1953 ... 2017 2018 2019 2020
  * longitude  (longitude) float64 1kB -10.0 -9.75 -9.5 ... 34.5 34.75 35.0

xarray.DataArray

'demographic_totals'

• latitude: 141
• longitude: 181
• year: 71

• dask.array<chunksize=(52, 24, 71), meta=np.ndarray>

  Array Chunk Bytes 6.91 MiB 1.11 MiB Shape (141, 181, 71) (64, 64, 71) Dask graph 12 chunks in 12 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • latitude
    (latitude)
    float64
    71.0 70.75 70.5 ... 36.5 36.25 36.0

    array([71.  , 70.75, 70.5 , 70.25, 70.  , 69.75, 69.5 , 69.25, 69.  , 68.75,
           68.5 , 68.25, 68.  , 67.75, 67.5 , 67.25, 67.  , 66.75, 66.5 , 66.25,
           66.  , 65.75, 65.5 , 65.25, 65.  , 64.75, 64.5 , 64.25, 64.  , 63.75,
           63.5 , 63.25, 63.  , 62.75, 62.5 , 62.25, 62.  , 61.75, 61.5 , 61.25,
           61.  , 60.75, 60.5 , 60.25, 60.  , 59.75, 59.5 , 59.25, 59.  , 58.75,
           58.5 , 58.25, 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.  , 49.75, 49.5 , 49.25, 49.  , 48.75,
           48.5 , 48.25, 48.  , 47.75, 47.5 , 47.25, 47.  , 46.75, 46.5 , 46.25,
           46.  , 45.75, 45.5 , 45.25, 45.  , 44.75, 44.5 , 44.25, 44.  , 43.75,
           43.5 , 43.25, 43.  , 42.75, 42.5 , 42.25, 42.  , 41.75, 41.5 , 41.25,
           41.  , 40.75, 40.5 , 40.25, 40.  , 39.75, 39.5 , 39.25, 39.  , 38.75,
           38.5 , 38.25, 38.  , 37.75, 37.5 , 37.25, 37.  , 36.75, 36.5 , 36.25,
           36.  ])

  • year
    (year)
    int64
    1950 1951 1952 ... 2018 2019 2020

    array([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,
           1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
           1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
           1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
           1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
           2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 34.75 35.0

    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.  ,   2.25,   2.5 ,   2.75,   3.  ,   3.25,
             3.5 ,   3.75,   4.  ,   4.25,   4.5 ,   4.75,   5.  ,   5.25,   5.5 ,
             5.75,   6.  ,   6.25,   6.5 ,   6.75,   7.  ,   7.25,   7.5 ,   7.75,
             8.  ,   8.25,   8.5 ,   8.75,   9.  ,   9.25,   9.5 ,   9.75,  10.  ,
            10.25,  10.5 ,  10.75,  11.  ,  11.25,  11.5 ,  11.75,  12.  ,  12.25,
            12.5 ,  12.75,  13.  ,  13.25,  13.5 ,  13.75,  14.  ,  14.25,  14.5 ,
            14.75,  15.  ,  15.25,  15.5 ,  15.75,  16.  ,  16.25,  16.5 ,  16.75,
            17.  ,  17.25,  17.5 ,  17.75,  18.  ,  18.25,  18.5 ,  18.75,  19.  ,
            19.25,  19.5 ,  19.75,  20.  ,  20.25,  20.5 ,  20.75,  21.  ,  21.25,
            21.5 ,  21.75,  22.  ,  22.25,  22.5 ,  22.75,  23.  ,  23.25,  23.5 ,
            23.75,  24.  ,  24.25,  24.5 ,  24.75,  25.  ,  25.25,  25.5 ,  25.75,
            26.  ,  26.25,  26.5 ,  26.75,  27.  ,  27.25,  27.5 ,  27.75,  28.  ,
            28.25,  28.5 ,  28.75,  29.  ,  29.25,  29.5 ,  29.75,  30.  ,  30.25,
            30.5 ,  30.75,  31.  ,  31.25,  31.5 ,  31.75,  32.  ,  32.25,  32.5 ,
            32.75,  33.  ,  33.25,  33.5 ,  33.75,  34.  ,  34.25,  34.5 ,  34.75,
            35.  ])

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 71.0, 70.75,  70.5, 70.25,  70.0, 69.75,  69.5, 69.25,  69.0, 68.75,
           ...
           38.25,  38.0, 37.75,  37.5, 37.25,  37.0, 36.75,  36.5, 36.25,  36.0],
          dtype='float64', name='latitude', length=141))

  • year
    PandasIndex

    PandasIndex(Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,
           1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,
           1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,
           1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
           1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
           2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],
          dtype='int64', name='year'))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
           ...
           32.75,  33.0, 33.25,  33.5, 33.75,  34.0, 34.25,  34.5, 34.75,  35.0],
          dtype='float64', name='longitude', length=181))

• Attributes: (0)

4. Data download

This is the phase where we explicitly trigger the download of the data. To do so we will call compute() on the European total population. The result will be small enought to easily fit into memory. Remember to assign the return of the compute() function to a new variable, so that the data is kept in memory.

We can measure the time it takes:

%%time

europe_population_bands_total_computed = europe_population_bands_total.compute()

CPU times: user 874 ms, sys: 255 ms, total: 1.13 s
Wall time: 865 ms

Preparing ERA5-single levels data

We will now follow the same logic to load ERA5 single levels data.

era5_single_levels = xr.open_dataset(
    f"https://edh:{PAT}@data.earthdatahub.destine.eu/era5/reanalysis-era5-single-levels-v0.zarr",
    chunks={},
    engine="zarr",
    storage_options={"client_kwargs": {"trust_env": True}},
)
era5_single_levels

<xarray.Dataset> Size: 256TB
Dimensions:           (valid_time: 742824, latitude: 721, longitude: 1440)
Coordinates:
    entireAtmosphere  float32 4B ...
  * latitude          (latitude) float64 6kB 90.0 89.75 89.5 ... -89.75 -90.0
  * longitude         (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8
    number            int64 8B ...
    surface           float64 8B ...
  * valid_time        (valid_time) datetime64[ns] 6MB 1940-01-01 ... 2024-09-...
Data variables: (12/83)
    alnid             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    alnip             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    aluvd             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    aluvp             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    blh               (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    cdir              (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    ...                ...
    viiwe             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    viiwn             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    vilwd             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    vilwe             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    vilwn             (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
    z                 (valid_time, latitude, longitude) float32 3TB dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_edition:            1
    GRIB_subCentre:          0
    history:                 2024-10-02T12:52 GRIB to CDM+CF via cfgrib-0.9.1...
    institution:             European Centre for Medium-Range Weather Forecasts

xarray.Dataset

• Dimensions:
  • valid_time: 742824
  • latitude: 721
  • longitude: 1440
• Coordinates: (6)
  • entireAtmosphere
    ()
    float32
    ...

    long_name :

    original GRIB coordinate for key: level(entireAtmosphere)

    units :

    1

    [1 values with dtype=float32]

  • latitude
    (latitude)
    float64
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])

  • longitude
    (longitude)
    float64
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02])

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    [1 values with dtype=int64]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • valid_time
    (valid_time)
    datetime64[ns]
    1940-01-01 ... 2024-09-26T23:00:00

    array(['1940-01-01T00:00:00.000000000', '1940-01-01T01:00:00.000000000',
           '1940-01-01T02:00:00.000000000', ..., '2024-09-26T21:00:00.000000000',
           '2024-09-26T22:00:00.000000000', '2024-09-26T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (83)
  • alnid
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    alnid

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Near IR albedo for diffuse radiation (climatological)

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    18

    GRIB_shortName :

    alnid

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    Near IR albedo for diffuse radiation (climatological)

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • alnip
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    alnip

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Near IR albedo for direct radiation (climatological)

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    17

    GRIB_shortName :

    alnip

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    Near IR albedo for direct radiation (climatological)

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aluvd
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    aluvd

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    UV visible albedo for diffuse radiation (climatological)

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    16

    GRIB_shortName :

    aluvd

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    UV visible albedo for diffuse radiation (climatological)

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aluvp
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    aluvp

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    UV visible albedo for direct radiation (climatological)

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    15

    GRIB_shortName :

    aluvp

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    UV visible albedo for direct radiation (climatological)

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • blh
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    blh

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Boundary layer height

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    159

    GRIB_shortName :

    blh

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    last_restart_dim_updated :

    742824

    long_name :

    Boundary layer height

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cdir
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    cdir

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Surface direct short-wave radiation, clear sky

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228022

    GRIB_shortName :

    cdir

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Surface direct short-wave radiation, clear sky

    standard_name :

    unknown

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cl
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    cl

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake cover

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    26

    GRIB_shortName :

    cl

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    Lake cover

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cp
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    lwe_thickness_of_convective_precipitation_amount

    GRIB_cfVarName :

    cp

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Convective precipitation

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    143

    GRIB_shortName :

    cp

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Convective precipitation

    standard_name :

    lwe_thickness_of_convective_precipitation_amount

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • crr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    crr

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Convective rain rate

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228218

    GRIB_shortName :

    crr

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    kg m**-2 s**-1

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Convective rain rate

    standard_name :

    unknown

    units :

    kg m**-2 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • d2m
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    d2m

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre dewpoint temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    168

    GRIB_shortName :

    2d

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    2 metre dewpoint temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dl
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    dl

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake total depth

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228007

    GRIB_shortName :

    dl

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    last_restart_dim_updated :

    742824

    long_name :

    Lake total depth

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • e
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    lwe_thickness_of_water_evaporation_amount

    GRIB_cfVarName :

    e

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Evaporation

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    182

    GRIB_shortName :

    e

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m of water equivalent

    last_restart_dim_updated :

    742824

    long_name :

    Evaporation

    standard_name :

    lwe_thickness_of_water_evaporation_amount

    units :

    m of water equivalent

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • fdir
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    fdir

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Total sky direct short-wave (solar) radiation at surface

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228021

    GRIB_shortName :

    fdir

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Total sky direct short-wave (solar) radiation at surface

    standard_name :

    unknown

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • flsr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    flsr

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Forecast logarithm of surface roughness for heat

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    245

    GRIB_shortName :

    flsr

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    Numeric

    last_restart_dim_updated :

    742824

    long_name :

    Forecast logarithm of surface roughness for heat

    standard_name :

    unknown

    units :

    Numeric

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hcc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    hcc

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    High cloud cover

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    188

    GRIB_shortName :

    hcc

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    High cloud cover

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ie
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    ie

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Instantaneous moisture flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    232

    GRIB_shortName :

    ie

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    kg m**-2 s**-1

    last_restart_dim_updated :

    742824

    long_name :

    Instantaneous moisture flux

    standard_name :

    unknown

    units :

    kg m**-2 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ilspf
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    ilspf

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Instantaneous large-scale surface precipitation fraction

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228217

    GRIB_shortName :

    ilspf

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Instantaneous large-scale surface precipitation fraction

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • inss
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    inss

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Instantaneous northward turbulent surface stress

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    230

    GRIB_shortName :

    inss

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    N m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Instantaneous northward turbulent surface stress

    standard_name :

    unknown

    units :

    N m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ishf
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    ishf

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Instantaneous surface sensible heat flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    231

    GRIB_shortName :

    ishf

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    W m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Instantaneous surface sensible heat flux

    standard_name :

    unknown

    units :

    W m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • istl1
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    istl1

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Ice temperature layer 1

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    35

    GRIB_shortName :

    istl1

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    depthBelowLandLayer

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Ice temperature layer 1

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • istl2
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    istl2

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Ice temperature layer 2

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    36

    GRIB_shortName :

    istl2

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    depthBelowLandLayer

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Ice temperature layer 2

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • istl3
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    istl3

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Ice temperature layer 3

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    37

    GRIB_shortName :

    istl3

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    depthBelowLandLayer

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Ice temperature layer 3

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • istl4
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    istl4

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Ice temperature layer 4

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    38

    GRIB_shortName :

    istl4

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    depthBelowLandLayer

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Ice temperature layer 4

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lblt
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    lblt

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake bottom temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228010

    GRIB_shortName :

    lblt

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Lake bottom temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lcc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    lcc

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Low cloud cover

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    186

    GRIB_shortName :

    lcc

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    Low cloud cover

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • licd
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    licd

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake ice total depth

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228014

    GRIB_shortName :

    licd

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    last_restart_dim_updated :

    742824

    long_name :

    Lake ice total depth

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lict
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    lict

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake ice surface temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228013

    GRIB_shortName :

    lict

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Lake ice surface temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lmld
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    lmld

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake mix-layer depth

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228009

    GRIB_shortName :

    lmld

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    last_restart_dim_updated :

    742824

    long_name :

    Lake mix-layer depth

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lmlt
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    lmlt

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake mix-layer temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228008

    GRIB_shortName :

    lmlt

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Lake mix-layer temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lshf
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    lshf

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake shape factor

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228012

    GRIB_shortName :

    lshf

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    dimensionless

    last_restart_dim_updated :

    742824

    long_name :

    Lake shape factor

    standard_name :

    unknown

    units :

    dimensionless

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lsp
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    lwe_thickness_of_stratiform_precipitation_amount

    GRIB_cfVarName :

    lsp

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Large-scale precipitation

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    142

    GRIB_shortName :

    lsp

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Large-scale precipitation

    standard_name :

    lwe_thickness_of_stratiform_precipitation_amount

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lspf
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    lspf

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Large-scale precipitation fraction

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    50

    GRIB_shortName :

    lspf

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    s

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Large-scale precipitation fraction

    standard_name :

    unknown

    units :

    s

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lsrr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    lsrr

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Large scale rain rate

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228219

    GRIB_shortName :

    lsrr

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    kg m**-2 s**-1

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Large scale rain rate

    standard_name :

    unknown

    units :

    kg m**-2 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ltlt
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    ltlt

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Lake total layer temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228011

    GRIB_shortName :

    ltlt

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Lake total layer temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mcc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    mcc

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Medium cloud cover

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    187

    GRIB_shortName :

    mcc

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    Medium cloud cover

    standard_name :

    unknown

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mn2t
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    mn2t

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Minimum temperature at 2 metres since previous post-processing

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    202

    GRIB_shortName :

    mn2t

    GRIB_stepType :

    max

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Minimum temperature at 2 metres since previous post-processing

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mntpr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    mntpr

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Minimum total precipitation rate since previous post-processing

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228227

    GRIB_shortName :

    mntpr

    GRIB_stepType :

    max

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    kg m**-2 s**-1

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Minimum total precipitation rate since previous post-processing

    standard_name :

    unknown

    units :

    kg m**-2 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • msl
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    air_pressure_at_mean_sea_level

    GRIB_cfVarName :

    msl

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Mean sea level pressure

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    151

    GRIB_shortName :

    msl

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    Pa

    last_restart_dim_updated :

    742824

    long_name :

    Mean sea level pressure

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mx2t
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    mx2t

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Maximum temperature at 2 metres since previous post-processing

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    201

    GRIB_shortName :

    mx2t

    GRIB_stepType :

    max

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Maximum temperature at 2 metres since previous post-processing

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mxtpr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    mxtpr

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Maximum total precipitation rate since previous post-processing

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228226

    GRIB_shortName :

    mxtpr

    GRIB_stepType :

    max

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    kg m**-2 s**-1

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Maximum total precipitation rate since previous post-processing

    standard_name :

    unknown

    units :

    kg m**-2 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pev
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    pev

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Potential evaporation

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228251

    GRIB_shortName :

    pev

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    last_restart_dim_updated :

    742824

    long_name :

    Potential evaporation

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ptype
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    ptype

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/longitude

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Precipitation type

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    260015

    GRIB_shortName :

    ptype

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (Code table 4.201)

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Precipitation type

    standard_name :

    unknown

    units :

    (Code table 4.201)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ro
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    ro

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Runoff

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    205

    GRIB_shortName :

    ro

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    last_restart_dim_updated :

    742824

    long_name :

    Runoff

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • skt
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    skt

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Skin temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    235

    GRIB_shortName :

    skt

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    Skin temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • slhf
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    surface_upward_latent_heat_flux

    GRIB_cfVarName :

    slhf

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Surface latent heat flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    147

    GRIB_shortName :

    slhf

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Surface latent heat flux

    standard_name :

    surface_upward_latent_heat_flux

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sp
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    surface_air_pressure

    GRIB_cfVarName :

    sp

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Surface pressure

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    134

    GRIB_shortName :

    sp

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    Pa

    last_restart_dim_updated :

    742824

    long_name :

    Surface pressure

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sro
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

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    GRIB_name :

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    long_name :

    Surface runoff

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sshf
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

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    last_restart_dim_updated :

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    long_name :

    Surface sensible heat flux

    standard_name :

    surface_upward_sensible_heat_flux

    units :

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ssr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

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    Latitude/Longitude Grid

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    J m**-2

    last_restart_dim_updated :

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    long_name :

    Surface net short-wave (solar) radiation

    standard_name :

    surface_net_downward_shortwave_flux

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ssrc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

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    fc

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    Latitude/Longitude Grid

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    0

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    0

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    GRIB_longitudeOfFirstGridPointInDegrees :

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    GRIB_name :

    Surface net short-wave (solar) radiation, clear sky

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    accum

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    1

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    0

    GRIB_typeOfLevel :

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    J m**-2

    last_restart_dim_updated :

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    long_name :

    Surface net short-wave (solar) radiation, clear sky

    standard_name :

    surface_net_downward_shortwave_flux_assuming_clear_sky

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ssrd
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

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    long_name :

    Surface short-wave (solar) radiation downwards

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    units :

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ssrdc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

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    Latitude/Longitude Grid

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    GRIB_name :

    Surface short-wave (solar) radiation downward clear-sky

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    long_name :

    Surface short-wave (solar) radiation downward clear-sky

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ssro
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

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    Latitude/Longitude Grid

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    0

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    Sub-surface runoff

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    long_name :

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sst
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

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    Latitude/Longitude Grid

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    GRIB_name :

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    long_name :

    Sea surface temperature

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    units :

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • str
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

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    Surface net long-wave (thermal) radiation

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    J m**-2

    last_restart_dim_updated :

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    long_name :

    Surface net long-wave (thermal) radiation

    standard_name :

    surface_net_upward_longwave_flux

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • strc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

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    0

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    Surface net long-wave (thermal) radiation, clear sky

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    accum

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    0

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    J m**-2

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    long_name :

    Surface net long-wave (thermal) radiation, clear sky

    standard_name :

    surface_net_downward_longwave_flux_assuming_clear_sky

    units :

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • strd
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

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    Latitude/Longitude Grid

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    0

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    0

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    GRIB_longitudeOfFirstGridPointInDegrees :

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    GRIB_longitudeOfLastGridPointInDegrees :

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    3.4028234663852886e+38

    GRIB_name :

    Surface long-wave (thermal) radiation downwards

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    strd

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    0

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    last_restart_dim_updated :

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    long_name :

    Surface long-wave (thermal) radiation downwards

    standard_name :

    unknown

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • strdc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

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    Latitude/Longitude Grid

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    0

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    0.25

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    0

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    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

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    GRIB_name :

    Surface long-wave (thermal) radiation downward clear-sky

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    long_name :

    Surface long-wave (thermal) radiation downward clear-sky

    standard_name :

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    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • t2m
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

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    unknown

    GRIB_cfVarName :

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    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

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    regular_ll

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    GRIB_iScansNegatively :

    0

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    0.25

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    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    167

    GRIB_shortName :

    2t

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    last_restart_dim_updated :

    742824

    long_name :

    2 metre temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tcc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    cloud_area_fraction

    GRIB_cfVarName :

    tcc

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Total cloud cover

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    164

    GRIB_shortName :

    tcc

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    (0 - 1)

    last_restart_dim_updated :

    742824

    long_name :

    Total cloud cover

    standard_name :

    cloud_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tciw
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    tciw

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Total column cloud ice water

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    79

    GRIB_shortName :

    tciw

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    kg m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Total column cloud ice water

    standard_name :

    unknown

    units :

    kg m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tclw
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    tclw

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Total column cloud liquid water

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    78

    GRIB_shortName :

    tclw

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    kg m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Total column cloud liquid water

    standard_name :

    unknown

    units :

    kg m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tcrw
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    tcrw

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Total column rain water

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228089

    GRIB_shortName :

    tcrw

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    kg m**-2

    GRIB_uvRelativeToGrid :

    0

    last_restart_dim_updated :

    742824

    long_name :

    Total column rain water

    standard_name :

    unknown

    units :

    kg m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tisr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    tisr

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    TOA incident short-wave (solar) radiation

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    212

    GRIB_shortName :

    tisr

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    TOA incident short-wave (solar) radiation

    standard_name :

    unknown

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tp
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    tp

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Total precipitation

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228

    GRIB_shortName :

    tp

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    last_restart_dim_updated :

    742824

    long_name :

    Total precipitation

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tsr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    toa_net_upward_shortwave_flux

    GRIB_cfVarName :

    tsr

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Top net short-wave (solar) radiation

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    178

    GRIB_shortName :

    tsr

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Top net short-wave (solar) radiation

    standard_name :

    toa_net_upward_shortwave_flux

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tsrc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    tsrc

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Top net short-wave (solar) radiation, clear sky

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    208

    GRIB_shortName :

    tsrc

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Top net short-wave (solar) radiation, clear sky

    standard_name :

    unknown

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ttr
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    toa_outgoing_longwave_flux

    GRIB_cfVarName :

    ttr

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Top net long-wave (thermal) radiation

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    179

    GRIB_shortName :

    ttr

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Top net long-wave (thermal) radiation

    standard_name :

    toa_outgoing_longwave_flux

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ttrc
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    ttrc

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Top net long-wave (thermal) radiation, clear sky

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    209

    GRIB_shortName :

    ttrc

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Top net long-wave (thermal) radiation, clear sky

    standard_name :

    unknown

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u10
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    u10

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    10 metre U wind component

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    165

    GRIB_shortName :

    10u

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m s**-1

    last_restart_dim_updated :

    742824

    long_name :

    10 metre U wind component

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u100
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    u100

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    100 metre U wind component

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228246

    GRIB_shortName :

    100u

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m s**-1

    last_restart_dim_updated :

    742824

    long_name :

    100 metre U wind component

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u10n
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    u10n

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    10 metre u-component of neutral wind

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228131

    GRIB_shortName :

    u10n

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m s**-1

    last_restart_dim_updated :

    742824

    long_name :

    10 metre u-component of neutral wind

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uvb
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    uvb

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Downward UV radiation at the surface

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    57

    GRIB_shortName :

    uvb

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    last_restart_dim_updated :

    742824

    long_name :

    Downward UV radiation at the surface

    standard_name :

    unknown

    units :

    J m**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v10
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    v10

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    10 metre V wind component

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    166

    GRIB_shortName :

    10v

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m s**-1

    last_restart_dim_updated :

    742824

    long_name :

    10 metre V wind component

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v100
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    v100

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    100 metre V wind component

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228247

    GRIB_shortName :

    100v

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m s**-1

    last_restart_dim_updated :

    742824

    long_name :

    100 metre V wind component

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v10n
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    v10n

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    10 metre v-component of neutral wind

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    228132

    GRIB_shortName :

    v10n

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m s**-1

    last_restart_dim_updated :

    742824

    long_name :

    10 metre v-component of neutral wind

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • viiwd
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    viiwd

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Vertical integral of divergence of cloud frozen water flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    162080

    GRIB_shortName :

    viiwd

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    entireAtmosphere

    GRIB_units :

    kg m**-2 s**-1

    last_restart_dim_updated :

    742824

    long_name :

    Vertical integral of divergence of cloud frozen water flux

    standard_name :

    unknown

    units :

    kg m**-2 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • viiwe
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    viiwe

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Vertical integral of eastward cloud frozen water flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    162090

    GRIB_shortName :

    viiwe

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    entireAtmosphere

    GRIB_units :

    kg m**-1 s**-1

    last_restart_dim_updated :

    742824

    long_name :

    Vertical integral of eastward cloud frozen water flux

    standard_name :

    unknown

    units :

    kg m**-1 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • viiwn
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    viiwn

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Vertical integral of northward cloud frozen water flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    162091

    GRIB_shortName :

    viiwn

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    entireAtmosphere

    GRIB_units :

    kg m**-1 s**-1

    last_restart_dim_updated :

    742824

    long_name :

    Vertical integral of northward cloud frozen water flux

    standard_name :

    unknown

    units :

    kg m**-1 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vilwd
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    vilwd

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Vertical integral of divergence of cloud liquid water flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    162079

    GRIB_shortName :

    vilwd

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    entireAtmosphere

    GRIB_units :

    kg m**-2 s**-1

    last_restart_dim_updated :

    742824

    long_name :

    Vertical integral of divergence of cloud liquid water flux

    standard_name :

    unknown

    units :

    kg m**-2 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vilwe
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    vilwe

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Vertical integral of eastward cloud liquid water flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    162088

    GRIB_shortName :

    vilwe

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    entireAtmosphere

    GRIB_units :

    kg m**-1 s**-1

    last_restart_dim_updated :

    742824

    long_name :

    Vertical integral of eastward cloud liquid water flux

    standard_name :

    unknown

    units :

    kg m**-1 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vilwn
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    vilwn

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Vertical integral of northward cloud liquid water flux

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    162089

    GRIB_shortName :

    vilwn

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    entireAtmosphere

    GRIB_units :

    kg m**-1 s**-1

    last_restart_dim_updated :

    742824

    long_name :

    Vertical integral of northward cloud liquid water flux

    standard_name :

    unknown

    units :

    kg m**-1 s**-1

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

  • z
    (valid_time, latitude, longitude)
    float32
    dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    geopotential

    GRIB_cfVarName :

    z

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Geopotential

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    129

    GRIB_shortName :

    z

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m**2 s**-2

    last_restart_dim_updated :

    742824

    long_name :

    Geopotential

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float64', name='latitude', length=721))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float64', name='longitude', length=1440))

  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['1940-01-01 00:00:00', '1940-01-01 01:00:00',
                   '1940-01-01 02:00:00', '1940-01-01 03:00:00',
                   '1940-01-01 04:00:00', '1940-01-01 05:00:00',
                   '1940-01-01 06:00:00', '1940-01-01 07:00:00',
                   '1940-01-01 08:00:00', '1940-01-01 09:00:00',
                   ...
                   '2024-09-26 14:00:00', '2024-09-26 15:00:00',
                   '2024-09-26 16:00:00', '2024-09-26 17:00:00',
                   '2024-09-26 18:00:00', '2024-09-26 19:00:00',
                   '2024-09-26 20:00:00', '2024-09-26 21:00:00',
                   '2024-09-26 22:00:00', '2024-09-26 23:00:00'],
                  dtype='datetime64[ns]', name='valid_time', length=742824, freq=None))

• Attributes: (7)

  Conventions :

  CF-1.7

  GRIB_centre :

  ecmf

  GRIB_centreDescription :

  European Centre for Medium-Range Weather Forecasts

  GRIB_edition :

  1

  GRIB_subCentre :

  0

  history :

  2024-10-02T12:52 GRIB to CDM+CF via cfgrib-0.9.14.1/ecCodes-2.38.0 with {"source": ".xarray-ecmwf-cache/60b4e879cc647fea654b5515e1f182e4.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

  institution :

  European Centre for Medium-Range Weather Forecasts

From the dataset we extract the 2 metre temperature:

t2m = era5_single_levels.t2m - 273.15 # to celsius
t2m.attrs["units"] = "°C"
t2m

<xarray.DataArray 't2m' (valid_time: 742824, latitude: 721, longitude: 1440)> Size: 3TB
dask.array<sub, shape=(742824, 721, 1440), dtype=float32, chunksize=(4320, 64, 64), chunktype=numpy.ndarray>
Coordinates:
    entireAtmosphere  float32 4B ...
  * latitude          (latitude) float64 6kB 90.0 89.75 89.5 ... -89.75 -90.0
  * longitude         (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8
    number            int64 8B ...
    surface           float64 8B ...
  * valid_time        (valid_time) datetime64[ns] 6MB 1940-01-01 ... 2024-09-...
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  1440
    GRIB_Ny:                                  721
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           t2m
    GRIB_dataType:                            an
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               K
    last_restart_dim_updated:                 742824
    long_name:                                2 metre temperature
    standard_name:                            unknown
    units:                                    °C

xarray.DataArray

't2m'

• valid_time: 742824
• latitude: 721
• longitude: 1440

• dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

  Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • entireAtmosphere
    ()
    float32
    ...

    long_name :

    original GRIB coordinate for key: level(entireAtmosphere)

    units :

    1

    [1 values with dtype=float32]

  • latitude
    (latitude)
    float64
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])

  • longitude
    (longitude)
    float64
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02])

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    [1 values with dtype=int64]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • valid_time
    (valid_time)
    datetime64[ns]
    1940-01-01 ... 2024-09-26T23:00:00

    array(['1940-01-01T00:00:00.000000000', '1940-01-01T01:00:00.000000000',
           '1940-01-01T02:00:00.000000000', ..., '2024-09-26T21:00:00.000000000',
           '2024-09-26T22:00:00.000000000', '2024-09-26T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float64', name='latitude', length=721))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float64', name='longitude', length=1440))

  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['1940-01-01 00:00:00', '1940-01-01 01:00:00',
                   '1940-01-01 02:00:00', '1940-01-01 03:00:00',
                   '1940-01-01 04:00:00', '1940-01-01 05:00:00',
                   '1940-01-01 06:00:00', '1940-01-01 07:00:00',
                   '1940-01-01 08:00:00', '1940-01-01 09:00:00',
                   ...
                   '2024-09-26 14:00:00', '2024-09-26 15:00:00',
                   '2024-09-26 16:00:00', '2024-09-26 17:00:00',
                   '2024-09-26 18:00:00', '2024-09-26 19:00:00',
                   '2024-09-26 20:00:00', '2024-09-26 21:00:00',
                   '2024-09-26 22:00:00', '2024-09-26 23:00:00'],
                  dtype='datetime64[ns]', name='valid_time', length=742824, freq=None))

• Attributes: (31)

  GRIB_NV :

  0

  GRIB_Nx :

  1440

  GRIB_Ny :

  721

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  t2m

  GRIB_dataType :

  an

  GRIB_gridDefinitionDescription :

  Latitude/Longitude Grid

  GRIB_gridType :

  regular_ll

  GRIB_iDirectionIncrementInDegrees :

  0.25

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.25

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  90.0

  GRIB_latitudeOfLastGridPointInDegrees :

  -90.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  0.0

  GRIB_longitudeOfLastGridPointInDegrees :

  359.75

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  2 metre temperature

  GRIB_numberOfPoints :

  1038240

  GRIB_paramId :

  167

  GRIB_shortName :

  2t

  GRIB_stepType :

  instant

  GRIB_stepUnits :

  1

  GRIB_totalNumber :

  0

  GRIB_typeOfLevel :

  surface

  GRIB_units :

  K

  last_restart_dim_updated :

  742824

  long_name :

  2 metre temperature

  standard_name :

  unknown

  units :

  °C

We perform the same coordinate rolling as we did before:

t2m = t2m.assign_coords(longitude=(((t2m.longitude + 180) % 360) - 180))
t2m = t2m.roll(longitude=int(len(t2m.longitude) / 2), roll_coords=True)
t2m

<xarray.DataArray 't2m' (valid_time: 742824, latitude: 721, longitude: 1440)> Size: 3TB
dask.array<rechunk-merge, shape=(742824, 721, 1440), dtype=float32, chunksize=(4320, 64, 64), chunktype=numpy.ndarray>
Coordinates:
    entireAtmosphere  float32 4B ...
  * latitude          (latitude) float64 6kB 90.0 89.75 89.5 ... -89.75 -90.0
    number            int64 8B ...
    surface           float64 8B ...
  * valid_time        (valid_time) datetime64[ns] 6MB 1940-01-01 ... 2024-09-...
  * longitude         (longitude) float64 12kB -180.0 -179.8 ... 179.5 179.8
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  1440
    GRIB_Ny:                                  721
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           t2m
    GRIB_dataType:                            an
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               K
    last_restart_dim_updated:                 742824
    long_name:                                2 metre temperature
    standard_name:                            unknown
    units:                                    °C

xarray.DataArray

't2m'

• valid_time: 742824
• latitude: 721
• longitude: 1440

• dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>

  Array Chunk Bytes 2.81 TiB 67.50 MiB Shape (742824, 721, 1440) (4320, 64, 64) Dask graph 47472 chunks in 7 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • entireAtmosphere
    ()
    float32
    ...

    long_name :

    original GRIB coordinate for key: level(entireAtmosphere)

    units :

    1

    [1 values with dtype=float32]

  • latitude
    (latitude)
    float64
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    [1 values with dtype=int64]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • valid_time
    (valid_time)
    datetime64[ns]
    1940-01-01 ... 2024-09-26T23:00:00

    array(['1940-01-01T00:00:00.000000000', '1940-01-01T01:00:00.000000000',
           '1940-01-01T02:00:00.000000000', ..., '2024-09-26T21:00:00.000000000',
           '2024-09-26T22:00:00.000000000', '2024-09-26T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • longitude
    (longitude)
    float64
    -180.0 -179.8 ... 179.5 179.8

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75])

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float64', name='latitude', length=721))

  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['1940-01-01 00:00:00', '1940-01-01 01:00:00',
                   '1940-01-01 02:00:00', '1940-01-01 03:00:00',
                   '1940-01-01 04:00:00', '1940-01-01 05:00:00',
                   '1940-01-01 06:00:00', '1940-01-01 07:00:00',
                   '1940-01-01 08:00:00', '1940-01-01 09:00:00',
                   ...
                   '2024-09-26 14:00:00', '2024-09-26 15:00:00',
                   '2024-09-26 16:00:00', '2024-09-26 17:00:00',
                   '2024-09-26 18:00:00', '2024-09-26 19:00:00',
                   '2024-09-26 20:00:00', '2024-09-26 21:00:00',
                   '2024-09-26 22:00:00', '2024-09-26 23:00:00'],
                  dtype='datetime64[ns]', name='valid_time', length=742824, freq=None))

  • longitude
    PandasIndex

    PandasIndex(Index([ -180.0, -179.75,  -179.5, -179.25,  -179.0, -178.75,  -178.5, -178.25,
            -178.0, -177.75,
           ...
             177.5,  177.75,   178.0,  178.25,   178.5,  178.75,   179.0,  179.25,
             179.5,  179.75],
          dtype='float64', name='longitude', length=1440))

• Attributes: (31)

  GRIB_NV :

  0

  GRIB_Nx :

  1440

  GRIB_Ny :

  721

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  t2m

  GRIB_dataType :

  an

  GRIB_gridDefinitionDescription :

  Latitude/Longitude Grid

  GRIB_gridType :

  regular_ll

  GRIB_iDirectionIncrementInDegrees :

  0.25

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.25

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  90.0

  GRIB_latitudeOfLastGridPointInDegrees :

  -90.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  0.0

  GRIB_longitudeOfLastGridPointInDegrees :

  359.75

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  2 metre temperature

  GRIB_numberOfPoints :

  1038240

  GRIB_paramId :

  167

  GRIB_shortName :

  2t

  GRIB_stepType :

  instant

  GRIB_stepUnits :

  1

  GRIB_totalNumber :

  0

  GRIB_typeOfLevel :

  surface

  GRIB_units :

  K

  last_restart_dim_updated :

  742824

  long_name :

  2 metre temperature

  standard_name :

  unknown

  units :

  °C

Also, we perform a geographical selection corresponding to continental Europe and a temporal selection of the years between 1950 and 2020.

t2m_europe = t2m.sel(latitude=slice(71, 36), longitude=slice(-10,35))
t2m_europe_1950_2020 = t2m_europe.sel(valid_time=slice("1950", "2020"))
t2m_europe_1950_2020

<xarray.DataArray 't2m' (valid_time: 622392, latitude: 141, longitude: 181)> Size: 64GB
dask.array<getitem, shape=(622392, 141, 181), dtype=float32, chunksize=(4320, 64, 64), chunktype=numpy.ndarray>
Coordinates:
    entireAtmosphere  float32 4B ...
  * latitude          (latitude) float64 1kB 71.0 70.75 70.5 ... 36.5 36.25 36.0
    number            int64 8B ...
    surface           float64 8B ...
  * valid_time        (valid_time) datetime64[ns] 5MB 1950-01-01 ... 2020-12-...
  * longitude         (longitude) float64 1kB -10.0 -9.75 -9.5 ... 34.75 35.0
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  1440
    GRIB_Ny:                                  721
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           t2m
    GRIB_dataType:                            an
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               K
    last_restart_dim_updated:                 742824
    long_name:                                2 metre temperature
    standard_name:                            unknown
    units:                                    °C

xarray.DataArray

't2m'

• valid_time: 622392
• latitude: 141
• longitude: 181

• dask.array<chunksize=(3048, 52, 24), meta=np.ndarray>

  Array Chunk Bytes 59.17 GiB 67.50 MiB Shape (622392, 141, 181) (4320, 64, 64) Dask graph 1740 chunks in 9 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • entireAtmosphere
    ()
    float32
    ...

    long_name :

    original GRIB coordinate for key: level(entireAtmosphere)

    units :

    1

    [1 values with dtype=float32]

  • latitude
    (latitude)
    float64
    71.0 70.75 70.5 ... 36.5 36.25 36.0

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    array([71.  , 70.75, 70.5 , 70.25, 70.  , 69.75, 69.5 , 69.25, 69.  , 68.75,
           68.5 , 68.25, 68.  , 67.75, 67.5 , 67.25, 67.  , 66.75, 66.5 , 66.25,
           66.  , 65.75, 65.5 , 65.25, 65.  , 64.75, 64.5 , 64.25, 64.  , 63.75,
           63.5 , 63.25, 63.  , 62.75, 62.5 , 62.25, 62.  , 61.75, 61.5 , 61.25,
           61.  , 60.75, 60.5 , 60.25, 60.  , 59.75, 59.5 , 59.25, 59.  , 58.75,
           58.5 , 58.25, 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.  , 49.75, 49.5 , 49.25, 49.  , 48.75,
           48.5 , 48.25, 48.  , 47.75, 47.5 , 47.25, 47.  , 46.75, 46.5 , 46.25,
           46.  , 45.75, 45.5 , 45.25, 45.  , 44.75, 44.5 , 44.25, 44.  , 43.75,
           43.5 , 43.25, 43.  , 42.75, 42.5 , 42.25, 42.  , 41.75, 41.5 , 41.25,
           41.  , 40.75, 40.5 , 40.25, 40.  , 39.75, 39.5 , 39.25, 39.  , 38.75,
           38.5 , 38.25, 38.  , 37.75, 37.5 , 37.25, 37.  , 36.75, 36.5 , 36.25,
           36.  ])

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    [1 values with dtype=int64]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • valid_time
    (valid_time)
    datetime64[ns]
    1950-01-01 ... 2020-12-31T23:00:00

    array(['1950-01-01T00:00:00.000000000', '1950-01-01T01:00:00.000000000',
           '1950-01-01T02:00:00.000000000', ..., '2020-12-31T21:00:00.000000000',
           '2020-12-31T22:00:00.000000000', '2020-12-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 34.75 35.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    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.  ,   2.25,   2.5 ,   2.75,   3.  ,   3.25,
             3.5 ,   3.75,   4.  ,   4.25,   4.5 ,   4.75,   5.  ,   5.25,   5.5 ,
             5.75,   6.  ,   6.25,   6.5 ,   6.75,   7.  ,   7.25,   7.5 ,   7.75,
             8.  ,   8.25,   8.5 ,   8.75,   9.  ,   9.25,   9.5 ,   9.75,  10.  ,
            10.25,  10.5 ,  10.75,  11.  ,  11.25,  11.5 ,  11.75,  12.  ,  12.25,
            12.5 ,  12.75,  13.  ,  13.25,  13.5 ,  13.75,  14.  ,  14.25,  14.5 ,
            14.75,  15.  ,  15.25,  15.5 ,  15.75,  16.  ,  16.25,  16.5 ,  16.75,
            17.  ,  17.25,  17.5 ,  17.75,  18.  ,  18.25,  18.5 ,  18.75,  19.  ,
            19.25,  19.5 ,  19.75,  20.  ,  20.25,  20.5 ,  20.75,  21.  ,  21.25,
            21.5 ,  21.75,  22.  ,  22.25,  22.5 ,  22.75,  23.  ,  23.25,  23.5 ,
            23.75,  24.  ,  24.25,  24.5 ,  24.75,  25.  ,  25.25,  25.5 ,  25.75,
            26.  ,  26.25,  26.5 ,  26.75,  27.  ,  27.25,  27.5 ,  27.75,  28.  ,
            28.25,  28.5 ,  28.75,  29.  ,  29.25,  29.5 ,  29.75,  30.  ,  30.25,
            30.5 ,  30.75,  31.  ,  31.25,  31.5 ,  31.75,  32.  ,  32.25,  32.5 ,
            32.75,  33.  ,  33.25,  33.5 ,  33.75,  34.  ,  34.25,  34.5 ,  34.75,
            35.  ])

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 71.0, 70.75,  70.5, 70.25,  70.0, 69.75,  69.5, 69.25,  69.0, 68.75,
           ...
           38.25,  38.0, 37.75,  37.5, 37.25,  37.0, 36.75,  36.5, 36.25,  36.0],
          dtype='float64', name='latitude', length=141))

  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['1950-01-01 00:00:00', '1950-01-01 01:00:00',
                   '1950-01-01 02:00:00', '1950-01-01 03:00:00',
                   '1950-01-01 04:00:00', '1950-01-01 05:00:00',
                   '1950-01-01 06:00:00', '1950-01-01 07:00:00',
                   '1950-01-01 08:00:00', '1950-01-01 09:00:00',
                   ...
                   '2020-12-31 14:00:00', '2020-12-31 15:00:00',
                   '2020-12-31 16:00:00', '2020-12-31 17:00:00',
                   '2020-12-31 18:00:00', '2020-12-31 19:00:00',
                   '2020-12-31 20:00:00', '2020-12-31 21:00:00',
                   '2020-12-31 22:00:00', '2020-12-31 23:00:00'],
                  dtype='datetime64[ns]', name='valid_time', length=622392, freq=None))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
           ...
           32.75,  33.0, 33.25,  33.5, 33.75,  34.0, 34.25,  34.5, 34.75,  35.0],
          dtype='float64', name='longitude', length=181))

• Attributes: (31)

  GRIB_NV :

  0

  GRIB_Nx :

  1440

  GRIB_Ny :

  721

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  t2m

  GRIB_dataType :

  an

  GRIB_gridDefinitionDescription :

  Latitude/Longitude Grid

  GRIB_gridType :

  regular_ll

  GRIB_iDirectionIncrementInDegrees :

  0.25

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.25

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  90.0

  GRIB_latitudeOfLastGridPointInDegrees :

  -90.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  0.0

  GRIB_longitudeOfLastGridPointInDegrees :

  359.75

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  2 metre temperature

  GRIB_numberOfPoints :

  1038240

  GRIB_paramId :

  167

  GRIB_shortName :

  2t

  GRIB_stepType :

  instant

  GRIB_stepUnits :

  1

  GRIB_totalNumber :

  0

  GRIB_typeOfLevel :

  surface

  GRIB_units :

  K

  last_restart_dim_updated :

  742824

  long_name :

  2 metre temperature

  standard_name :

  unknown

  units :

  °C

The ERA5 single levels dataset is way bigger than the population one. When we access a sample of the 2 metre temperature, xarray must download every chunk that includes a portion of the selected data.

To estimate the size of the download, we can use the costing.py module. This estimate must be done before we apply any reduction operation.

import costing

costing.estimate_download_size(t2m, t2m_europe_1950_2020)

estimated_needed_chunks: 1740
estimated_memory_size: 123.155 GB
estimated_download_size: 12.316 GB

This amount of data could be easily downloaded from Earth Data Hub, thanks to data compression. However, the result of the compute() wouldn't fit into memory. We thus perform a reduction operation before triggering the download and computation, which greatly reduces the size of the result that will be kept in memory.

We compute the annual average of the 2 metre temperature:

t2m_europe_yearly_mean = t2m_europe_1950_2020.resample(valid_time="YS").mean()
t2m_europe_yearly_mean

<xarray.DataArray 't2m' (valid_time: 71, latitude: 141, longitude: 181)> Size: 7MB
dask.array<transpose, shape=(71, 141, 181), dtype=float32, chunksize=(1, 64, 64), chunktype=numpy.ndarray>
Coordinates:
    entireAtmosphere  float32 4B ...
  * latitude          (latitude) float64 1kB 71.0 70.75 70.5 ... 36.5 36.25 36.0
    number            int64 8B ...
    surface           float64 8B ...
  * longitude         (longitude) float64 1kB -10.0 -9.75 -9.5 ... 34.75 35.0
  * valid_time        (valid_time) datetime64[ns] 568B 1950-01-01 ... 2020-01-01
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  1440
    GRIB_Ny:                                  721
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           t2m
    GRIB_dataType:                            an
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               K
    last_restart_dim_updated:                 742824
    long_name:                                2 metre temperature
    standard_name:                            unknown
    units:                                    °C

xarray.DataArray

't2m'

• valid_time: 71
• latitude: 141
• longitude: 181

• dask.array<chunksize=(1, 52, 24), meta=np.ndarray>

  Array Chunk Bytes 6.91 MiB 16.00 kiB Shape (71, 141, 181) (1, 64, 64) Dask graph 852 chunks in 157 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • entireAtmosphere
    ()
    float32
    ...

    long_name :

    original GRIB coordinate for key: level(entireAtmosphere)

    units :

    1

    [1 values with dtype=float32]

  • latitude
    (latitude)
    float64
    71.0 70.75 70.5 ... 36.5 36.25 36.0

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    array([71.  , 70.75, 70.5 , 70.25, 70.  , 69.75, 69.5 , 69.25, 69.  , 68.75,
           68.5 , 68.25, 68.  , 67.75, 67.5 , 67.25, 67.  , 66.75, 66.5 , 66.25,
           66.  , 65.75, 65.5 , 65.25, 65.  , 64.75, 64.5 , 64.25, 64.  , 63.75,
           63.5 , 63.25, 63.  , 62.75, 62.5 , 62.25, 62.  , 61.75, 61.5 , 61.25,
           61.  , 60.75, 60.5 , 60.25, 60.  , 59.75, 59.5 , 59.25, 59.  , 58.75,
           58.5 , 58.25, 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.  , 49.75, 49.5 , 49.25, 49.  , 48.75,
           48.5 , 48.25, 48.  , 47.75, 47.5 , 47.25, 47.  , 46.75, 46.5 , 46.25,
           46.  , 45.75, 45.5 , 45.25, 45.  , 44.75, 44.5 , 44.25, 44.  , 43.75,
           43.5 , 43.25, 43.  , 42.75, 42.5 , 42.25, 42.  , 41.75, 41.5 , 41.25,
           41.  , 40.75, 40.5 , 40.25, 40.  , 39.75, 39.5 , 39.25, 39.  , 38.75,
           38.5 , 38.25, 38.  , 37.75, 37.5 , 37.25, 37.  , 36.75, 36.5 , 36.25,
           36.  ])

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    [1 values with dtype=int64]

  • 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 ... 34.75 35.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    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.  ,   2.25,   2.5 ,   2.75,   3.  ,   3.25,
             3.5 ,   3.75,   4.  ,   4.25,   4.5 ,   4.75,   5.  ,   5.25,   5.5 ,
             5.75,   6.  ,   6.25,   6.5 ,   6.75,   7.  ,   7.25,   7.5 ,   7.75,
             8.  ,   8.25,   8.5 ,   8.75,   9.  ,   9.25,   9.5 ,   9.75,  10.  ,
            10.25,  10.5 ,  10.75,  11.  ,  11.25,  11.5 ,  11.75,  12.  ,  12.25,
            12.5 ,  12.75,  13.  ,  13.25,  13.5 ,  13.75,  14.  ,  14.25,  14.5 ,
            14.75,  15.  ,  15.25,  15.5 ,  15.75,  16.  ,  16.25,  16.5 ,  16.75,
            17.  ,  17.25,  17.5 ,  17.75,  18.  ,  18.25,  18.5 ,  18.75,  19.  ,
            19.25,  19.5 ,  19.75,  20.  ,  20.25,  20.5 ,  20.75,  21.  ,  21.25,
            21.5 ,  21.75,  22.  ,  22.25,  22.5 ,  22.75,  23.  ,  23.25,  23.5 ,
            23.75,  24.  ,  24.25,  24.5 ,  24.75,  25.  ,  25.25,  25.5 ,  25.75,
            26.  ,  26.25,  26.5 ,  26.75,  27.  ,  27.25,  27.5 ,  27.75,  28.  ,
            28.25,  28.5 ,  28.75,  29.  ,  29.25,  29.5 ,  29.75,  30.  ,  30.25,
            30.5 ,  30.75,  31.  ,  31.25,  31.5 ,  31.75,  32.  ,  32.25,  32.5 ,
            32.75,  33.  ,  33.25,  33.5 ,  33.75,  34.  ,  34.25,  34.5 ,  34.75,
            35.  ])

  • valid_time
    (valid_time)
    datetime64[ns]
    1950-01-01 ... 2020-01-01

    array(['1950-01-01T00:00:00.000000000', '1951-01-01T00:00:00.000000000',
           '1952-01-01T00:00:00.000000000', '1953-01-01T00:00:00.000000000',
           '1954-01-01T00:00:00.000000000', '1955-01-01T00:00:00.000000000',
           '1956-01-01T00:00:00.000000000', '1957-01-01T00:00:00.000000000',
           '1958-01-01T00:00:00.000000000', '1959-01-01T00:00:00.000000000',
           '1960-01-01T00:00:00.000000000', '1961-01-01T00:00:00.000000000',
           '1962-01-01T00:00:00.000000000', '1963-01-01T00:00:00.000000000',
           '1964-01-01T00:00:00.000000000', '1965-01-01T00:00:00.000000000',
           '1966-01-01T00:00:00.000000000', '1967-01-01T00:00:00.000000000',
           '1968-01-01T00:00:00.000000000', '1969-01-01T00:00:00.000000000',
           '1970-01-01T00:00:00.000000000', '1971-01-01T00:00:00.000000000',
           '1972-01-01T00:00:00.000000000', '1973-01-01T00:00:00.000000000',
           '1974-01-01T00:00:00.000000000', '1975-01-01T00:00:00.000000000',
           '1976-01-01T00:00:00.000000000', '1977-01-01T00:00:00.000000000',
           '1978-01-01T00:00:00.000000000', '1979-01-01T00:00:00.000000000',
           '1980-01-01T00:00:00.000000000', '1981-01-01T00:00:00.000000000',
           '1982-01-01T00:00:00.000000000', '1983-01-01T00:00:00.000000000',
           '1984-01-01T00:00:00.000000000', '1985-01-01T00:00:00.000000000',
           '1986-01-01T00:00:00.000000000', '1987-01-01T00:00:00.000000000',
           '1988-01-01T00:00:00.000000000', '1989-01-01T00:00:00.000000000',
           '1990-01-01T00:00:00.000000000', '1991-01-01T00:00:00.000000000',
           '1992-01-01T00:00:00.000000000', '1993-01-01T00:00:00.000000000',
           '1994-01-01T00:00:00.000000000', '1995-01-01T00:00:00.000000000',
           '1996-01-01T00:00:00.000000000', '1997-01-01T00:00:00.000000000',
           '1998-01-01T00:00:00.000000000', '1999-01-01T00:00:00.000000000',
           '2000-01-01T00:00:00.000000000', '2001-01-01T00:00:00.000000000',
           '2002-01-01T00:00:00.000000000', '2003-01-01T00:00:00.000000000',
           '2004-01-01T00:00:00.000000000', '2005-01-01T00:00:00.000000000',
           '2006-01-01T00:00:00.000000000', '2007-01-01T00:00:00.000000000',
           '2008-01-01T00:00:00.000000000', '2009-01-01T00:00:00.000000000',
           '2010-01-01T00:00:00.000000000', '2011-01-01T00:00:00.000000000',
           '2012-01-01T00:00:00.000000000', '2013-01-01T00:00:00.000000000',
           '2014-01-01T00:00:00.000000000', '2015-01-01T00:00:00.000000000',
           '2016-01-01T00:00:00.000000000', '2017-01-01T00:00:00.000000000',
           '2018-01-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000',
           '2020-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 71.0, 70.75,  70.5, 70.25,  70.0, 69.75,  69.5, 69.25,  69.0, 68.75,
           ...
           38.25,  38.0, 37.75,  37.5, 37.25,  37.0, 36.75,  36.5, 36.25,  36.0],
          dtype='float64', name='latitude', length=141))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
           ...
           32.75,  33.0, 33.25,  33.5, 33.75,  34.0, 34.25,  34.5, 34.75,  35.0],
          dtype='float64', name='longitude', length=181))

  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['1950-01-01', '1951-01-01', '1952-01-01', '1953-01-01',
                   '1954-01-01', '1955-01-01', '1956-01-01', '1957-01-01',
                   '1958-01-01', '1959-01-01', '1960-01-01', '1961-01-01',
                   '1962-01-01', '1963-01-01', '1964-01-01', '1965-01-01',
                   '1966-01-01', '1967-01-01', '1968-01-01', '1969-01-01',
                   '1970-01-01', '1971-01-01', '1972-01-01', '1973-01-01',
                   '1974-01-01', '1975-01-01', '1976-01-01', '1977-01-01',
                   '1978-01-01', '1979-01-01', '1980-01-01', '1981-01-01',
                   '1982-01-01', '1983-01-01', '1984-01-01', '1985-01-01',
                   '1986-01-01', '1987-01-01', '1988-01-01', '1989-01-01',
                   '1990-01-01', '1991-01-01', '1992-01-01', '1993-01-01',
                   '1994-01-01', '1995-01-01', '1996-01-01', '1997-01-01',
                   '1998-01-01', '1999-01-01', '2000-01-01', '2001-01-01',
                   '2002-01-01', '2003-01-01', '2004-01-01', '2005-01-01',
                   '2006-01-01', '2007-01-01', '2008-01-01', '2009-01-01',
                   '2010-01-01', '2011-01-01', '2012-01-01', '2013-01-01',
                   '2014-01-01', '2015-01-01', '2016-01-01', '2017-01-01',
                   '2018-01-01', '2019-01-01', '2020-01-01'],
                  dtype='datetime64[ns]', name='valid_time', freq='YS-JAN'))

• Attributes: (31)

  GRIB_NV :

  0

  GRIB_Nx :

  1440

  GRIB_Ny :

  721

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  t2m

  GRIB_dataType :

  an

  GRIB_gridDefinitionDescription :

  Latitude/Longitude Grid

  GRIB_gridType :

  regular_ll

  GRIB_iDirectionIncrementInDegrees :

  0.25

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.25

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  90.0

  GRIB_latitudeOfLastGridPointInDegrees :

  -90.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  0.0

  GRIB_longitudeOfLastGridPointInDegrees :

  359.75

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  2 metre temperature

  GRIB_numberOfPoints :

  1038240

  GRIB_paramId :

  167

  GRIB_shortName :

  2t

  GRIB_stepType :

  instant

  GRIB_stepUnits :

  1

  GRIB_totalNumber :

  0

  GRIB_typeOfLevel :

  surface

  GRIB_units :

  K

  last_restart_dim_updated :

  742824

  long_name :

  2 metre temperature

  standard_name :

  unknown

  units :

  °C

At this point, the resulting dataset small enough to call compute() on it. Remember to assign the return of the compute() function to a new variable, so that the data is kept in memory.

We can measure the time it takes:

%%time

t2m_europe_yearly_mean_computed = t2m_europe_yearly_mean.compute()

CPU times: user 19min 36s, sys: 5min 31s, total: 25min 7s
Wall time: 8min 59s

Area-weighted 2 metre temperature vs population-weighted 2 metre temperature

To compute the area-weighted 2 metre temperature trend in Europe, we need to spatially average the temperature data using the areas of the latitude-longitude grid cells as weights:

import numpy as np

weights = np.cos(np.deg2rad(t2m_europe_yearly_mean_computed.latitude))
weights.name = "weights"

t2m_europe_yearly_mean_area_weighted = t2m_europe_yearly_mean_computed.weighted(weights).mean(("longitude", "latitude"))
t2m_europe_yearly_mean_area_weighted

<xarray.DataArray 't2m' (valid_time: 71)> Size: 568B
array([ 9.73143245,  9.54077568,  9.30266422,  9.65744633,  9.14167472,
        9.19113411,  8.39374605,  9.56572026,  9.29620918,  9.78678239,
        9.63911762,  9.99801575,  9.05815576,  8.92954205,  9.29800933,
        8.8260963 ,  9.29124349,  9.59479709,  9.21513626,  8.96015133,
        9.25737186,  9.33365114,  9.48211979,  9.33031631,  9.75456766,
        9.86438628,  9.00002637,  9.37511701,  8.84580254,  9.11709609,
        8.87549476,  9.29087939,  9.65754524,  9.7386082 ,  9.44506196,
        8.77296716,  9.2368825 ,  8.87974788,  9.69230407, 10.37366402,
       10.38624748,  9.59619789,  9.82628151,  9.38199912, 10.123129  ,
        9.89640215,  9.20763148,  9.88151595,  9.81225687, 10.29441204,
       10.41770865, 10.06531496, 10.3884738 , 10.31476037, 10.12192511,
       10.08604738, 10.41532098, 10.57369915, 10.46482174, 10.37075119,
        9.66206254, 10.51831596, 10.11687097, 10.28761378, 10.97883414,
       10.86744654, 10.65587192, 10.60293839, 10.90643005, 10.8906737 ,
       11.19913625])
Coordinates:
    entireAtmosphere  float32 4B 0.0
    number            int64 8B 0
    surface           float64 8B 0.0
  * valid_time        (valid_time) datetime64[ns] 568B 1950-01-01 ... 2020-01-01
Attributes: (12/31)
    GRIB_NV:                                  0
    GRIB_Nx:                                  1440
    GRIB_Ny:                                  721
    GRIB_cfName:                              unknown
    GRIB_cfVarName:                           t2m
    GRIB_dataType:                            an
    ...                                       ...
    GRIB_typeOfLevel:                         surface
    GRIB_units:                               K
    last_restart_dim_updated:                 742824
    long_name:                                2 metre temperature
    standard_name:                            unknown
    units:                                    °C

xarray.DataArray

't2m'

• valid_time: 71

• 9.731 9.541 9.303 9.657 9.142 9.191 ... 10.66 10.6 10.91 10.89 11.2

  array([ 9.73143245,  9.54077568,  9.30266422,  9.65744633,  9.14167472,
          9.19113411,  8.39374605,  9.56572026,  9.29620918,  9.78678239,
          9.63911762,  9.99801575,  9.05815576,  8.92954205,  9.29800933,
          8.8260963 ,  9.29124349,  9.59479709,  9.21513626,  8.96015133,
          9.25737186,  9.33365114,  9.48211979,  9.33031631,  9.75456766,
          9.86438628,  9.00002637,  9.37511701,  8.84580254,  9.11709609,
          8.87549476,  9.29087939,  9.65754524,  9.7386082 ,  9.44506196,
          8.77296716,  9.2368825 ,  8.87974788,  9.69230407, 10.37366402,
         10.38624748,  9.59619789,  9.82628151,  9.38199912, 10.123129  ,
          9.89640215,  9.20763148,  9.88151595,  9.81225687, 10.29441204,
         10.41770865, 10.06531496, 10.3884738 , 10.31476037, 10.12192511,
         10.08604738, 10.41532098, 10.57369915, 10.46482174, 10.37075119,
          9.66206254, 10.51831596, 10.11687097, 10.28761378, 10.97883414,
         10.86744654, 10.65587192, 10.60293839, 10.90643005, 10.8906737 ,
         11.19913625])

• Coordinates: (4)
  • entireAtmosphere
    ()
    float32
    0.0

    long_name :

    original GRIB coordinate for key: level(entireAtmosphere)

    units :

    1

    array(0., dtype=float32)

  • number
    ()
    int64
    0

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    array(0)

  • surface
    ()
    float64
    0.0

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    array(0.)

  • valid_time
    (valid_time)
    datetime64[ns]
    1950-01-01 ... 2020-01-01

    array(['1950-01-01T00:00:00.000000000', '1951-01-01T00:00:00.000000000',
           '1952-01-01T00:00:00.000000000', '1953-01-01T00:00:00.000000000',
           '1954-01-01T00:00:00.000000000', '1955-01-01T00:00:00.000000000',
           '1956-01-01T00:00:00.000000000', '1957-01-01T00:00:00.000000000',
           '1958-01-01T00:00:00.000000000', '1959-01-01T00:00:00.000000000',
           '1960-01-01T00:00:00.000000000', '1961-01-01T00:00:00.000000000',
           '1962-01-01T00:00:00.000000000', '1963-01-01T00:00:00.000000000',
           '1964-01-01T00:00:00.000000000', '1965-01-01T00:00:00.000000000',
           '1966-01-01T00:00:00.000000000', '1967-01-01T00:00:00.000000000',
           '1968-01-01T00:00:00.000000000', '1969-01-01T00:00:00.000000000',
           '1970-01-01T00:00:00.000000000', '1971-01-01T00:00:00.000000000',
           '1972-01-01T00:00:00.000000000', '1973-01-01T00:00:00.000000000',
           '1974-01-01T00:00:00.000000000', '1975-01-01T00:00:00.000000000',
           '1976-01-01T00:00:00.000000000', '1977-01-01T00:00:00.000000000',
           '1978-01-01T00:00:00.000000000', '1979-01-01T00:00:00.000000000',
           '1980-01-01T00:00:00.000000000', '1981-01-01T00:00:00.000000000',
           '1982-01-01T00:00:00.000000000', '1983-01-01T00:00:00.000000000',
           '1984-01-01T00:00:00.000000000', '1985-01-01T00:00:00.000000000',
           '1986-01-01T00:00:00.000000000', '1987-01-01T00:00:00.000000000',
           '1988-01-01T00:00:00.000000000', '1989-01-01T00:00:00.000000000',
           '1990-01-01T00:00:00.000000000', '1991-01-01T00:00:00.000000000',
           '1992-01-01T00:00:00.000000000', '1993-01-01T00:00:00.000000000',
           '1994-01-01T00:00:00.000000000', '1995-01-01T00:00:00.000000000',
           '1996-01-01T00:00:00.000000000', '1997-01-01T00:00:00.000000000',
           '1998-01-01T00:00:00.000000000', '1999-01-01T00:00:00.000000000',
           '2000-01-01T00:00:00.000000000', '2001-01-01T00:00:00.000000000',
           '2002-01-01T00:00:00.000000000', '2003-01-01T00:00:00.000000000',
           '2004-01-01T00:00:00.000000000', '2005-01-01T00:00:00.000000000',
           '2006-01-01T00:00:00.000000000', '2007-01-01T00:00:00.000000000',
           '2008-01-01T00:00:00.000000000', '2009-01-01T00:00:00.000000000',
           '2010-01-01T00:00:00.000000000', '2011-01-01T00:00:00.000000000',
           '2012-01-01T00:00:00.000000000', '2013-01-01T00:00:00.000000000',
           '2014-01-01T00:00:00.000000000', '2015-01-01T00:00:00.000000000',
           '2016-01-01T00:00:00.000000000', '2017-01-01T00:00:00.000000000',
           '2018-01-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000',
           '2020-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (1)
  • valid_time
    PandasIndex

    PandasIndex(DatetimeIndex(['1950-01-01', '1951-01-01', '1952-01-01', '1953-01-01',
                   '1954-01-01', '1955-01-01', '1956-01-01', '1957-01-01',
                   '1958-01-01', '1959-01-01', '1960-01-01', '1961-01-01',
                   '1962-01-01', '1963-01-01', '1964-01-01', '1965-01-01',
                   '1966-01-01', '1967-01-01', '1968-01-01', '1969-01-01',
                   '1970-01-01', '1971-01-01', '1972-01-01', '1973-01-01',
                   '1974-01-01', '1975-01-01', '1976-01-01', '1977-01-01',
                   '1978-01-01', '1979-01-01', '1980-01-01', '1981-01-01',
                   '1982-01-01', '1983-01-01', '1984-01-01', '1985-01-01',
                   '1986-01-01', '1987-01-01', '1988-01-01', '1989-01-01',
                   '1990-01-01', '1991-01-01', '1992-01-01', '1993-01-01',
                   '1994-01-01', '1995-01-01', '1996-01-01', '1997-01-01',
                   '1998-01-01', '1999-01-01', '2000-01-01', '2001-01-01',
                   '2002-01-01', '2003-01-01', '2004-01-01', '2005-01-01',
                   '2006-01-01', '2007-01-01', '2008-01-01', '2009-01-01',
                   '2010-01-01', '2011-01-01', '2012-01-01', '2013-01-01',
                   '2014-01-01', '2015-01-01', '2016-01-01', '2017-01-01',
                   '2018-01-01', '2019-01-01', '2020-01-01'],
                  dtype='datetime64[ns]', name='valid_time', freq='YS-JAN'))

• Attributes: (31)

  GRIB_NV :

  0

  GRIB_Nx :

  1440

  GRIB_Ny :

  721

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  t2m

  GRIB_dataType :

  an

  GRIB_gridDefinitionDescription :

  Latitude/Longitude Grid

  GRIB_gridType :

  regular_ll

  GRIB_iDirectionIncrementInDegrees :

  0.25

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.25

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  90.0

  GRIB_latitudeOfLastGridPointInDegrees :

  -90.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  0.0

  GRIB_longitudeOfLastGridPointInDegrees :

  359.75

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  2 metre temperature

  GRIB_numberOfPoints :

  1038240

  GRIB_paramId :

  167

  GRIB_shortName :

  2t

  GRIB_stepType :

  instant

  GRIB_stepUnits :

  1

  GRIB_totalNumber :

  0

  GRIB_typeOfLevel :

  surface

  GRIB_units :

  K

  last_restart_dim_updated :

  742824

  long_name :

  2 metre temperature

  standard_name :

  unknown

  units :

  °C

To compute the population-weighted 2 metre temperature we will follow the same logic, but this time we will use the population data as weight. To make the population data and temperature data compatible we need to transform the valid_time coordinate of the temperature data to the yearcoordinate of the population data so that the two data array can be easily multiplied:

# shorten the names and replace the valid_time coordiate
temperature = t2m_europe_yearly_mean_computed.assign_coords({"year" : t2m_europe_yearly_mean_computed.valid_time.dt.year}).swap_dims({"valid_time": "year"}).drop_vars("valid_time")
population = europe_population_bands_total_computed

# compute the population wheighted temperature
t2m_europe_yearly_mean_population_weighted = (temperature * population).sum(dim=["latitude", "longitude"]) / population.sum(dim=["latitude", "longitude"])

Finally, we can compare the area-weighted temperature with the population-weighted temperature in the same graph. The first one is the average temperature experienced by the territory, while the second represents the average temperature experienced by people. Notice that the latter is higher than the fisrt.

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.plot(t2m_europe_yearly_mean_area_weighted.valid_time.dt.year, t2m_europe_yearly_mean_area_weighted, marker='o', label='Area-Weighted Average Temperature')
plt.plot(t2m_europe_yearly_mean_population_weighted.year, t2m_europe_yearly_mean_population_weighted, marker='o', label='Population-Weighted Average Temperature')
plt.legend()
plt.title("Europe area Average Temperature (1950-2020)", fontsize=14)
plt.xlabel("Year", fontsize=12)
plt.ylabel("Average Temperature", fontsize=12)
plt.grid(True)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

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