How to work with ERA5 single levels on Earth Data Hub: climatological analysis of temperature in GermanyĀ¶
Earth Data Hub (EDH) offers an innovative and super-efficient way to access earth related data. 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.
In order to access datasets on Earth Data Hub 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. 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: ā¤µ
PAT = "your-personal-access-token"
#e.g. PAT="edh_pat_44bbb7e9192a4c6bb47ddf07d07564eee5d17de8dfc48f7118f88e3bc4a4157f8fe2403f5aa0a2d53441b6922ea9a33a"
We will use it later when accessing the dataset.
What you will learn:Ā¶
- how to access and preview the dataset
- select and reduce the data
- plot the results
In this notebook we set two goals:
Our first goal is to compute the 2 metre temperature anomaly for the month of October 2023, in the Germany area, against the 1991-2020 reference period.
Our second goal is to compute the 2 metre temperature climatology (monthly means and standard deviations) in Berlin for the same reference period and compare it with the monthly averages of 2023.
Working with EDH dataĀ¶
Datasets on EDH are typically 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 and preview
- data selection
- (optional) data download
- (optional) data reduction
- further operations and visualization
Xarray and Dask work together following a lazy principle. This means 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 reduce the amount of data that needs to be downloaded before the computation takes place, e.g., when the .compute()
or .plot()
methods are called.
1. Data access and previewĀ¶
To preview the data, only the dataset metadata must be downloaded. Xarray does this automatically when you access a Zarr dataset:
import xarray as xr
ds = 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}},
)
ds
<xarray.Dataset> Size: 254TB Dimensions: (valid_time: 736344, 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 ... 2023-12-... 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: 2024-08-12T15:43 GRIB to CDM+CF via cfgrib-0.9.1... institution: European Centre for Medium-Range Weather Forecasts
- valid_time: 736344
- latitude: 721
- longitude: 1440
- entireAtmosphere()float32...
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
[1 values with dtype=float32]
- latitude(latitude)float6490.0 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)float640.0 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 :
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[1 values with dtype=int64]
- surface()float64...
- long_name :
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- units :
- 1
[1 values with dtype=float64]
- valid_time(valid_time)datetime64[ns]1940-01-01 ... 2023-12-31T23:00:00
array(['1940-01-01T00:00:00.000000000', '1940-01-01T01:00:00.000000000', '1940-01-01T02:00:00.000000000', ..., '2023-12-31T21:00:00.000000000', '2023-12-31T22:00:00.000000000', '2023-12-31T23:00:00.000000000'], dtype='datetime64[ns]')
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- 90.0
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- 0.0
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- 359.75
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- 3.4028234663852886e+38
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- Near IR albedo for diffuse radiation (climatological)
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- standard_name :
- unknown
- units :
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Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - alnip(valid_time, latitude, longitude)float32dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
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- GRIB_units :
- m s**-1
- last_restart_dim_updated :
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- long_name :
- 100 metre V wind component
- standard_name :
- unknown
- units :
- m s**-1
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - v10n(valid_time, latitude, longitude)float32dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
- GRIB_NV :
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- 0.0
- GRIB_longitudeOfLastGridPointInDegrees :
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- 3.4028234663852886e+38
- GRIB_name :
- 10 metre v-component of neutral wind
- GRIB_numberOfPoints :
- 1038240
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- v10n
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- GRIB_units :
- m s**-1
- last_restart_dim_updated :
- 736344
- long_name :
- 10 metre v-component of neutral wind
- standard_name :
- unknown
- units :
- m s**-1
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - viiwd(valid_time, latitude, longitude)float32dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
- GRIB_NV :
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- 0.0
- GRIB_longitudeOfLastGridPointInDegrees :
- 359.75
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- 3.4028234663852886e+38
- GRIB_name :
- Vertical integral of divergence of cloud frozen water flux
- GRIB_numberOfPoints :
- 1038240
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- entireAtmosphere
- GRIB_units :
- kg m**-2 s**-1
- last_restart_dim_updated :
- 736344
- long_name :
- Vertical integral of divergence of cloud frozen water flux
- standard_name :
- unknown
- units :
- kg m**-2 s**-1
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - viiwe(valid_time, latitude, longitude)float32dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
- GRIB_NV :
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- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
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- GRIB_latitudeOfLastGridPointInDegrees :
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- 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
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- viiwe
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- 0
- GRIB_typeOfLevel :
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- GRIB_units :
- kg m**-1 s**-1
- last_restart_dim_updated :
- 736344
- long_name :
- Vertical integral of eastward cloud frozen water flux
- standard_name :
- unknown
- units :
- kg m**-1 s**-1
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - viiwn(valid_time, latitude, longitude)float32dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
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- GRIB_cfName :
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- GRIB_cfVarName :
- viiwn
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- Latitude/Longitude Grid
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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 :
- Vertical integral of northward cloud frozen water flux
- GRIB_numberOfPoints :
- 1038240
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- 162091
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- viiwn
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- 0
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- entireAtmosphere
- GRIB_units :
- kg m**-1 s**-1
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- 736344
- long_name :
- Vertical integral of northward cloud frozen water flux
- standard_name :
- unknown
- units :
- kg m**-1 s**-1
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - vilwd(valid_time, latitude, longitude)float32dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
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- unknown
- GRIB_cfVarName :
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- an
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
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- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.25
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
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- 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
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- 162079
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- vilwd
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- instant
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- 1
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- entireAtmosphere
- GRIB_units :
- kg m**-2 s**-1
- last_restart_dim_updated :
- 736344
- long_name :
- Vertical integral of divergence of cloud liquid water flux
- standard_name :
- unknown
- units :
- kg m**-2 s**-1
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - vilwe(valid_time, latitude, longitude)float32dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
- GRIB_NV :
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- unknown
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- vilwe
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- GRIB_gridDefinitionDescription :
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- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.25
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 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
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- 3.4028234663852886e+38
- GRIB_name :
- Vertical integral of eastward cloud liquid water flux
- GRIB_numberOfPoints :
- 1038240
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- 162088
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- vilwe
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- 1
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- 0
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- entireAtmosphere
- GRIB_units :
- kg m**-1 s**-1
- last_restart_dim_updated :
- 736344
- long_name :
- Vertical integral of eastward cloud liquid water flux
- standard_name :
- unknown
- units :
- kg m**-1 s**-1
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - vilwn(valid_time, latitude, longitude)float32dask.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
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- 162089
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- vilwn
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 1
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- entireAtmosphere
- GRIB_units :
- kg m**-1 s**-1
- last_restart_dim_updated :
- 736344
- long_name :
- Vertical integral of northward cloud liquid water flux
- standard_name :
- unknown
- units :
- kg m**-1 s**-1
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray - z(valid_time, latitude, longitude)float32dask.array<chunksize=(4320, 64, 64), meta=np.ndarray>
- GRIB_NV :
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- geopotential
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- z
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- Latitude/Longitude Grid
- GRIB_gridType :
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- 0
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- 0.25
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- 0
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- 0
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- 90.0
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- -90.0
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- 0.0
- GRIB_longitudeOfLastGridPointInDegrees :
- 359.75
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- Geopotential
- GRIB_numberOfPoints :
- 1038240
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- z
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 1
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- m**2 s**-2
- long_name :
- Geopotential
- standard_name :
- geopotential
- units :
- m**2 s**-2
Array Chunk Bytes 2.78 TiB 67.50 MiB Shape (736344, 721, 1440) (4320, 64, 64) Dask graph 47196 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
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))
- longitudePandasIndex
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_timePandasIndex
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', ... '2023-12-31 14:00:00', '2023-12-31 15:00:00', '2023-12-31 16:00:00', '2023-12-31 17:00:00', '2023-12-31 18:00:00', '2023-12-31 19:00:00', '2023-12-31 20:00:00', '2023-12-31 21:00:00', '2023-12-31 22:00:00', '2023-12-31 23:00:00'], dtype='datetime64[ns]', name='valid_time', length=736344, freq=None))
- Conventions :
- CF-1.7
- GRIB_centre :
- ecmf
- GRIB_centreDescription :
- European Centre for Medium-Range Weather Forecasts
- GRIB_edition :
- 1
- GRIB_subCentre :
- 0
- history :
- 2024-08-12T15:43 GRIB to CDM+CF via cfgrib-0.9.14.0/ecCodes-2.36.0 with {"source": ".xarray-ecmwf-cache/ce711b0c7052464feb62eace9fb327fd.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}
- institution :
- European Centre for Medium-Range Weather Forecasts
ā At this point, no data has been downloaded yet, nor loaded in memory.
Average 2 metre temperature in Germany, October 2023Ā¶
2. Data selectionĀ¶
First, we perform a geographical selection corresponding to the Germany area. This greatly reduces the amount of data that will be downloaded from EDH. Also, we convert the temperature to Ā°C
.
t2m = ds.t2m.astype("float32") - 273.15
t2m.attrs["units"] = "C"
t2m_germany_area = t2m.sel(**{"latitude": slice(55, 47), "longitude": slice(5, 16)})
t2m_germany_area
<xarray.DataArray 't2m' (valid_time: 736344, latitude: 33, longitude: 45)> Size: 4GB dask.array<getitem, shape=(736344, 33, 45), dtype=float32, chunksize=(4320, 33, 44), chunktype=numpy.ndarray> Coordinates: entireAtmosphere float32 4B ... * latitude (latitude) float64 264B 55.0 54.75 54.5 ... 47.25 47.0 * longitude (longitude) float64 360B 5.0 5.25 5.5 ... 15.5 15.75 16.0 number int64 8B ... surface float64 8B ... * valid_time (valid_time) datetime64[ns] 6MB 1940-01-01 ... 2023-12-... Attributes: units: C
- valid_time: 736344
- latitude: 33
- longitude: 45
- dask.array<chunksize=(4320, 33, 44), meta=np.ndarray>
Array Chunk Bytes 4.07 GiB 23.93 MiB Shape (736344, 33, 45) (4320, 33, 44) Dask graph 342 chunks in 4 graph layers Data type float32 numpy.ndarray - entireAtmosphere()float32...
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
[1 values with dtype=float32]
- latitude(latitude)float6455.0 54.75 54.5 ... 47.5 47.25 47.0
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([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. ])
- longitude(longitude)float645.0 5.25 5.5 ... 15.5 15.75 16.0
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([ 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. ])
- 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 ... 2023-12-31T23:00:00
array(['1940-01-01T00:00:00.000000000', '1940-01-01T01:00:00.000000000', '1940-01-01T02:00:00.000000000', ..., '2023-12-31T21:00:00.000000000', '2023-12-31T22:00:00.000000000', '2023-12-31T23:00:00.000000000'], dtype='datetime64[ns]')
- latitudePandasIndex
PandasIndex(Index([ 55.0, 54.75, 54.5, 54.25, 54.0, 53.75, 53.5, 53.25, 53.0, 52.75, 52.5, 52.25, 52.0, 51.75, 51.5, 51.25, 51.0, 50.75, 50.5, 50.25, 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, 47.5, 47.25, 47.0], dtype='float64', name='latitude'))
- longitudePandasIndex
PandasIndex(Index([ 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0], dtype='float64', name='longitude'))
- valid_timePandasIndex
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', ... '2023-12-31 14:00:00', '2023-12-31 15:00:00', '2023-12-31 16:00:00', '2023-12-31 17:00:00', '2023-12-31 18:00:00', '2023-12-31 19:00:00', '2023-12-31 20:00:00', '2023-12-31 21:00:00', '2023-12-31 22:00:00', '2023-12-31 23:00:00'], dtype='datetime64[ns]', name='valid_time', length=736344, freq=None))
- units :
- C
t2m_germany_area
<xarray.DataArray 't2m' (valid_time: 736344, latitude: 33, longitude: 45)> Size: 4GB dask.array<getitem, shape=(736344, 33, 45), dtype=float32, chunksize=(4320, 33, 44), chunktype=numpy.ndarray> Coordinates: entireAtmosphere float32 4B ... * latitude (latitude) float64 264B 55.0 54.75 54.5 ... 47.25 47.0 * longitude (longitude) float64 360B 5.0 5.25 5.5 ... 15.5 15.75 16.0 number int64 8B ... surface float64 8B ... * valid_time (valid_time) datetime64[ns] 6MB 1940-01-01 ... 2023-12-... Attributes: units: C
- valid_time: 736344
- latitude: 33
- longitude: 45
- dask.array<chunksize=(4320, 33, 44), meta=np.ndarray>
Array Chunk Bytes 4.07 GiB 23.93 MiB Shape (736344, 33, 45) (4320, 33, 44) Dask graph 342 chunks in 4 graph layers Data type float32 numpy.ndarray - entireAtmosphere()float32...
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
[1 values with dtype=float32]
- latitude(latitude)float6455.0 54.75 54.5 ... 47.5 47.25 47.0
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([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. ])
- longitude(longitude)float645.0 5.25 5.5 ... 15.5 15.75 16.0
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([ 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. ])
- 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 ... 2023-12-31T23:00:00
array(['1940-01-01T00:00:00.000000000', '1940-01-01T01:00:00.000000000', '1940-01-01T02:00:00.000000000', ..., '2023-12-31T21:00:00.000000000', '2023-12-31T22:00:00.000000000', '2023-12-31T23:00:00.000000000'], dtype='datetime64[ns]')
- latitudePandasIndex
PandasIndex(Index([ 55.0, 54.75, 54.5, 54.25, 54.0, 53.75, 53.5, 53.25, 53.0, 52.75, 52.5, 52.25, 52.0, 51.75, 51.5, 51.25, 51.0, 50.75, 50.5, 50.25, 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, 47.5, 47.25, 47.0], dtype='float64', name='latitude'))
- longitudePandasIndex
PandasIndex(Index([ 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0], dtype='float64', name='longitude'))
- valid_timePandasIndex
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', ... '2023-12-31 14:00:00', '2023-12-31 15:00:00', '2023-12-31 16:00:00', '2023-12-31 17:00:00', '2023-12-31 18:00:00', '2023-12-31 19:00:00', '2023-12-31 20:00:00', '2023-12-31 21:00:00', '2023-12-31 22:00:00', '2023-12-31 23:00:00'], dtype='datetime64[ns]', name='valid_time', length=736344, freq=None))
- units :
- C
Second, we further select the October 2023 month. This is, again, a lazy operation:
t2m_germany_area_october_2023 = t2m_germany_area.sel(valid_time="2023-10")
t2m_germany_area_october_2023
<xarray.DataArray 't2m' (valid_time: 744, latitude: 33, longitude: 45)> Size: 4MB dask.array<getitem, shape=(744, 33, 45), dtype=float32, chunksize=(480, 33, 44), chunktype=numpy.ndarray> Coordinates: entireAtmosphere float32 4B ... * latitude (latitude) float64 264B 55.0 54.75 54.5 ... 47.25 47.0 * longitude (longitude) float64 360B 5.0 5.25 5.5 ... 15.5 15.75 16.0 number int64 8B ... surface float64 8B ... * valid_time (valid_time) datetime64[ns] 6kB 2023-10-01 ... 2023-10-... Attributes: units: C
- valid_time: 744
- latitude: 33
- longitude: 45
- dask.array<chunksize=(264, 33, 44), meta=np.ndarray>
Array Chunk Bytes 4.21 MiB 2.66 MiB Shape (744, 33, 45) (480, 33, 44) Dask graph 4 chunks in 5 graph layers Data type float32 numpy.ndarray - entireAtmosphere()float32...
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
[1 values with dtype=float32]
- latitude(latitude)float6455.0 54.75 54.5 ... 47.5 47.25 47.0
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([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. ])
- longitude(longitude)float645.0 5.25 5.5 ... 15.5 15.75 16.0
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([ 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. ])
- 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]2023-10-01 ... 2023-10-31T23:00:00
array(['2023-10-01T00:00:00.000000000', '2023-10-01T01:00:00.000000000', '2023-10-01T02:00:00.000000000', ..., '2023-10-31T21:00:00.000000000', '2023-10-31T22:00:00.000000000', '2023-10-31T23:00:00.000000000'], dtype='datetime64[ns]')
- latitudePandasIndex
PandasIndex(Index([ 55.0, 54.75, 54.5, 54.25, 54.0, 53.75, 53.5, 53.25, 53.0, 52.75, 52.5, 52.25, 52.0, 51.75, 51.5, 51.25, 51.0, 50.75, 50.5, 50.25, 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, 47.5, 47.25, 47.0], dtype='float64', name='latitude'))
- longitudePandasIndex
PandasIndex(Index([ 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0], dtype='float64', name='longitude'))
- valid_timePandasIndex
PandasIndex(DatetimeIndex(['2023-10-01 00:00:00', '2023-10-01 01:00:00', '2023-10-01 02:00:00', '2023-10-01 03:00:00', '2023-10-01 04:00:00', '2023-10-01 05:00:00', '2023-10-01 06:00:00', '2023-10-01 07:00:00', '2023-10-01 08:00:00', '2023-10-01 09:00:00', ... '2023-10-31 14:00:00', '2023-10-31 15:00:00', '2023-10-31 16:00:00', '2023-10-31 17:00:00', '2023-10-31 18:00:00', '2023-10-31 19:00:00', '2023-10-31 20:00:00', '2023-10-31 21:00:00', '2023-10-31 22:00:00', '2023-10-31 23:00:00'], dtype='datetime64[ns]', name='valid_time', length=744, freq=None))
- units :
- C
3. Data downloadĀ¶
At this point the selection is small enough to call .compute()
on it. This will trigger the download of data from EDH and load it in memory.
We can measure the time it takes:
%%time
t2m_germany_area_october_2023 = t2m_germany_area_october_2023.compute()
CPU times: user 818 ms, sys: 513 ms, total: 1.33 s Wall time: 2.16 s
The data was very small, this didn't take long!
4. Data reductionĀ¶
Now that the data is loaded in memory, we can easily compute the october 2023 monthly mean:
t2m_germany_area_october_2023_monthly_mean = t2m_germany_area_october_2023.mean(dim="valid_time")
t2m_germany_area_october_2023_monthly_mean
<xarray.DataArray 't2m' (latitude: 33, longitude: 45)> Size: 6kB array([[13.325453 , 13.389966 , 13.424911 , ..., 12.257616 , 12.284496 , 12.304321 ], [13.614086 , 13.664152 , 13.679943 , ..., 12.537507 , 12.4921465, 12.512643 ], [13.858366 , 13.903055 , 13.91448 , ..., 12.575477 , 12.505922 , 12.191089 ], ..., [13.5314245, 14.038129 , 14.431263 , ..., 10.0140505, 10.73885 , 11.996536 ], [14.110707 , 14.629511 , 15.010548 , ..., 11.550653 , 12.272733 , 12.732729 ], [14.843212 , 15.05759 , 15.260543 , ..., 13.064035 , 13.677259 , 13.871476 ]], dtype=float32) Coordinates: entireAtmosphere float32 4B 0.0 * latitude (latitude) float64 264B 55.0 54.75 54.5 ... 47.25 47.0 * longitude (longitude) float64 360B 5.0 5.25 5.5 ... 15.5 15.75 16.0 number int64 8B 0 surface float64 8B 0.0
- latitude: 33
- longitude: 45
- 13.33 13.39 13.42 13.47 13.5 13.52 ... 11.06 12.27 13.06 13.68 13.87
array([[13.325453 , 13.389966 , 13.424911 , ..., 12.257616 , 12.284496 , 12.304321 ], [13.614086 , 13.664152 , 13.679943 , ..., 12.537507 , 12.4921465, 12.512643 ], [13.858366 , 13.903055 , 13.91448 , ..., 12.575477 , 12.505922 , 12.191089 ], ..., [13.5314245, 14.038129 , 14.431263 , ..., 10.0140505, 10.73885 , 11.996536 ], [14.110707 , 14.629511 , 15.010548 , ..., 11.550653 , 12.272733 , 12.732729 ], [14.843212 , 15.05759 , 15.260543 , ..., 13.064035 , 13.677259 , 13.871476 ]], dtype=float32)
- entireAtmosphere()float320.0
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
array(0., dtype=float32)
- latitude(latitude)float6455.0 54.75 54.5 ... 47.5 47.25 47.0
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([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. ])
- longitude(longitude)float645.0 5.25 5.5 ... 15.5 15.75 16.0
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([ 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. ])
- number()int640
- long_name :
- ensemble member numerical id
- standard_name :
- realization
- units :
- 1
array(0)
- surface()float640.0
- long_name :
- original GRIB coordinate for key: level(surface)
- units :
- 1
array(0.)
- latitudePandasIndex
PandasIndex(Index([ 55.0, 54.75, 54.5, 54.25, 54.0, 53.75, 53.5, 53.25, 53.0, 52.75, 52.5, 52.25, 52.0, 51.75, 51.5, 51.25, 51.0, 50.75, 50.5, 50.25, 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, 47.5, 47.25, 47.0], dtype='float64', name='latitude'))
- longitudePandasIndex
PandasIndex(Index([ 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0], dtype='float64', name='longitude'))
5. VisualizationĀ¶
Finally, we can plot the october 2023 montly mean on a map:
import display
import matplotlib.pyplot as plt
from cartopy import crs
display.map(
t2m_germany_area_october_2023_monthly_mean,
projection=crs.Miller(),
vmax=None,
cmap="YlOrRd",
title="Mean Surface Temperature, Oct 2023"
);
2 metre temperature anomaly in Germany, October 2023Ā¶
We want to compute the 2 metre temperature anomaly for the month of October 2023 against the 1991-2020 reference period, once again in Germany. The same considerations done before apply here.
We fist select the relevant months in the reference period:
t2m_germany_area_octobers_1991_2020 = t2m_germany_area.sel(valid_time=t2m_germany_area["valid_time.month"] == 10).sel(valid_time=slice("1991", "2020"))
t2m_germany_area_octobers_1991_2020
<xarray.DataArray 't2m' (valid_time: 22320, latitude: 33, longitude: 45)> Size: 133MB dask.array<getitem, shape=(22320, 33, 45), dtype=float32, chunksize=(744, 33, 44), chunktype=numpy.ndarray> Coordinates: entireAtmosphere float32 4B ... * latitude (latitude) float64 264B 55.0 54.75 54.5 ... 47.25 47.0 * longitude (longitude) float64 360B 5.0 5.25 5.5 ... 15.5 15.75 16.0 number int64 8B ... surface float64 8B ... * valid_time (valid_time) datetime64[ns] 179kB 1991-10-01 ... 2020-1... Attributes: units: C
- valid_time: 22320
- latitude: 33
- longitude: 45
- dask.array<chunksize=(744, 33, 44), meta=np.ndarray>
Array Chunk Bytes 126.44 MiB 4.12 MiB Shape (22320, 33, 45) (744, 33, 44) Dask graph 62 chunks in 6 graph layers Data type float32 numpy.ndarray - entireAtmosphere()float32...
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
[1 values with dtype=float32]
- latitude(latitude)float6455.0 54.75 54.5 ... 47.5 47.25 47.0
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([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. ])
- longitude(longitude)float645.0 5.25 5.5 ... 15.5 15.75 16.0
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([ 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. ])
- 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]1991-10-01 ... 2020-10-31T23:00:00
array(['1991-10-01T00:00:00.000000000', '1991-10-01T01:00:00.000000000', '1991-10-01T02:00:00.000000000', ..., '2020-10-31T21:00:00.000000000', '2020-10-31T22:00:00.000000000', '2020-10-31T23:00:00.000000000'], dtype='datetime64[ns]')
- latitudePandasIndex
PandasIndex(Index([ 55.0, 54.75, 54.5, 54.25, 54.0, 53.75, 53.5, 53.25, 53.0, 52.75, 52.5, 52.25, 52.0, 51.75, 51.5, 51.25, 51.0, 50.75, 50.5, 50.25, 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, 47.5, 47.25, 47.0], dtype='float64', name='latitude'))
- longitudePandasIndex
PandasIndex(Index([ 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0], dtype='float64', name='longitude'))
- valid_timePandasIndex
PandasIndex(DatetimeIndex(['1991-10-01 00:00:00', '1991-10-01 01:00:00', '1991-10-01 02:00:00', '1991-10-01 03:00:00', '1991-10-01 04:00:00', '1991-10-01 05:00:00', '1991-10-01 06:00:00', '1991-10-01 07:00:00', '1991-10-01 08:00:00', '1991-10-01 09:00:00', ... '2020-10-31 14:00:00', '2020-10-31 15:00:00', '2020-10-31 16:00:00', '2020-10-31 17:00:00', '2020-10-31 18:00:00', '2020-10-31 19:00:00', '2020-10-31 20:00:00', '2020-10-31 21:00:00', '2020-10-31 22:00:00', '2020-10-31 23:00:00'], dtype='datetime64[ns]', name='valid_time', length=22320, freq=None))
- units :
- C
This is small enought to be computed in reasonable time:
%%time
t2m_germany_area_octobers_1991_2020 = t2m_germany_area_octobers_1991_2020.compute()
CPU times: user 12.9 s, sys: 8.81 s, total: 21.7 s Wall time: 19.5 s
Now that the data is loaded in memory we can esily compute the 1991-2020 octobers mean:
t2m_germany_area_octobers_1991_2020_mean = t2m_germany_area_octobers_1991_2020.mean(dim="valid_time")
And finally the anomaly:
anomaly = t2m_germany_area_october_2023_monthly_mean - t2m_germany_area_octobers_1991_2020_mean
anomaly
<xarray.DataArray 't2m' (latitude: 33, longitude: 45)> Size: 6kB array([[0.94561386, 0.9775305 , 0.9818058 , ..., 1.1062622 , 1.1509447 , 1.18295 ], [1.058692 , 1.0717936 , 1.0788851 , ..., 1.3152046 , 1.3094378 , 1.3461571 ], [1.1612654 , 1.169033 , 1.1861801 , ..., 1.4496317 , 1.4415836 , 1.3800087 ], ..., [2.637679 , 2.6654654 , 2.8324194 , ..., 3.3936133 , 3.5344958 , 3.6796694 ], [2.7071152 , 2.7365713 , 2.8895226 , ..., 3.2909975 , 3.426055 , 3.5746298 ], [2.8477697 , 2.828455 , 2.91681 , ..., 3.4293985 , 3.4998322 , 3.638671 ]], dtype=float32) Coordinates: entireAtmosphere float32 4B 0.0 * latitude (latitude) float64 264B 55.0 54.75 54.5 ... 47.25 47.0 * longitude (longitude) float64 360B 5.0 5.25 5.5 ... 15.5 15.75 16.0 number int64 8B 0 surface float64 8B 0.0
- latitude: 33
- longitude: 45
- 0.9456 0.9775 0.9818 0.9838 0.9637 ... 3.38 3.372 3.429 3.5 3.639
array([[0.94561386, 0.9775305 , 0.9818058 , ..., 1.1062622 , 1.1509447 , 1.18295 ], [1.058692 , 1.0717936 , 1.0788851 , ..., 1.3152046 , 1.3094378 , 1.3461571 ], [1.1612654 , 1.169033 , 1.1861801 , ..., 1.4496317 , 1.4415836 , 1.3800087 ], ..., [2.637679 , 2.6654654 , 2.8324194 , ..., 3.3936133 , 3.5344958 , 3.6796694 ], [2.7071152 , 2.7365713 , 2.8895226 , ..., 3.2909975 , 3.426055 , 3.5746298 ], [2.8477697 , 2.828455 , 2.91681 , ..., 3.4293985 , 3.4998322 , 3.638671 ]], dtype=float32)
- entireAtmosphere()float320.0
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
array(0., dtype=float32)
- latitude(latitude)float6455.0 54.75 54.5 ... 47.5 47.25 47.0
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([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. ])
- longitude(longitude)float645.0 5.25 5.5 ... 15.5 15.75 16.0
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([ 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. ])
- number()int640
- long_name :
- ensemble member numerical id
- standard_name :
- realization
- units :
- 1
array(0)
- surface()float640.0
- long_name :
- original GRIB coordinate for key: level(surface)
- units :
- 1
array(0.)
- latitudePandasIndex
PandasIndex(Index([ 55.0, 54.75, 54.5, 54.25, 54.0, 53.75, 53.5, 53.25, 53.0, 52.75, 52.5, 52.25, 52.0, 51.75, 51.5, 51.25, 51.0, 50.75, 50.5, 50.25, 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, 47.5, 47.25, 47.0], dtype='float64', name='latitude'))
- longitudePandasIndex
PandasIndex(Index([ 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0], dtype='float64', name='longitude'))
We can plot the anomaly on a map:
display.map(
anomaly,
vmax=None,
projection=crs.Miller(),
cmap="YlOrRd",
title="Mean Surface Temperature anomaly (ref 1991-2020), Oct 2013"
);
2 metre temperature climatology (1991-2020) in Berlin vs 2023 montly meanĀ¶
The power of EDH is better showned when working with timeseries. We will now show how fast it is to compute the 2 metre temperature climatology (montly mean and standard deviation) in Berlin, over the reference period 1991-2020, and compare it with the 2023 monthly means.
With legacy data distributon systems you would need to dowload the entire world temperature, for the reference time period, in order to extract the Berlin data. Thanks to earth data hub this is not needed anymore! you only need to download the relevant chunks.
Here, we select the closet data to Berlin:
%%time
t2m_Berlin = t2m.sel(**{"latitude": 52.5, "longitude": 13.4}, method="nearest")
t2m_Berlin
CPU times: user 2.78 ms, sys: 1.39 ms, total: 4.17 ms Wall time: 8.99 ms
<xarray.DataArray 't2m' (valid_time: 736344)> Size: 3MB dask.array<getitem, shape=(736344,), dtype=float32, chunksize=(4320,), chunktype=numpy.ndarray> Coordinates: entireAtmosphere float32 4B ... latitude float64 8B 52.5 longitude float64 8B 13.5 number int64 8B ... surface float64 8B ... * valid_time (valid_time) datetime64[ns] 6MB 1940-01-01 ... 2023-12-... Attributes: units: C
- valid_time: 736344
- dask.array<chunksize=(4320,), meta=np.ndarray>
Array Chunk Bytes 2.81 MiB 16.88 kiB Shape (736344,) (4320,) Dask graph 171 chunks in 4 graph layers Data type float32 numpy.ndarray - entireAtmosphere()float32...
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
[1 values with dtype=float32]
- latitude()float6452.5
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array(52.5)
- longitude()float6413.5
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array(13.5)
- 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 ... 2023-12-31T23:00:00
array(['1940-01-01T00:00:00.000000000', '1940-01-01T01:00:00.000000000', '1940-01-01T02:00:00.000000000', ..., '2023-12-31T21:00:00.000000000', '2023-12-31T22:00:00.000000000', '2023-12-31T23:00:00.000000000'], dtype='datetime64[ns]')
- valid_timePandasIndex
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', ... '2023-12-31 14:00:00', '2023-12-31 15:00:00', '2023-12-31 16:00:00', '2023-12-31 17:00:00', '2023-12-31 18:00:00', '2023-12-31 19:00:00', '2023-12-31 20:00:00', '2023-12-31 21:00:00', '2023-12-31 22:00:00', '2023-12-31 23:00:00'], dtype='datetime64[ns]', name='valid_time', length=736344, freq=None))
- units :
- C
This is already small enought to be computed:
%%time
t2m_Berlin = t2m_Berlin.compute()
CPU times: user 34.4 s, sys: 23.8 s, total: 58.2 s Wall time: 53.9 s
Now that the data is loaded in memory we can easily compute the climatology for the reference period 1991-2020:
t2m_Berlin_climatology_mean = t2m_Berlin.sel(valid_time=slice("1991", "2020")).groupby("valid_time.month").mean(dim="valid_time")
t2m_Berlin_climatology_std = t2m_Berlin.sel(valid_time=slice("1991", "2020")).groupby("valid_time.month").std(dim="valid_time")
We also compute the monthly means for the year 2023:
t2m_Berlin_2023_monthly_means = t2m_Berlin.sel(valid_time="2023").resample(valid_time="1ME").mean(dim="valid_time")
t2m_Berlin_2023_monthly_means
<xarray.DataArray 't2m' (valid_time: 12)> Size: 48B array([ 4.1796923, 3.010704 , 5.551641 , 7.8618503, 13.801259 , 19.066559 , 19.860262 , 19.305855 , 18.504393 , 11.931021 , 5.609091 , 3.6396573], dtype=float32) Coordinates: entireAtmosphere float32 4B 0.0 latitude float64 8B 52.5 longitude float64 8B 13.5 number int64 8B 0 surface float64 8B 0.0 * valid_time (valid_time) datetime64[ns] 96B 2023-01-31 ... 2023-12-31 Attributes: units: C
- valid_time: 12
- 4.18 3.011 5.552 7.862 13.8 19.07 19.86 19.31 18.5 11.93 5.609 3.64
array([ 4.1796923, 3.010704 , 5.551641 , 7.8618503, 13.801259 , 19.066559 , 19.860262 , 19.305855 , 18.504393 , 11.931021 , 5.609091 , 3.6396573], dtype=float32)
- entireAtmosphere()float320.0
- long_name :
- original GRIB coordinate for key: level(entireAtmosphere)
- units :
- 1
array(0., dtype=float32)
- latitude()float6452.5
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array(52.5)
- longitude()float6413.5
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array(13.5)
- number()int640
- long_name :
- ensemble member numerical id
- standard_name :
- realization
- units :
- 1
array(0)
- surface()float640.0
- long_name :
- original GRIB coordinate for key: level(surface)
- units :
- 1
array(0.)
- valid_time(valid_time)datetime64[ns]2023-01-31 ... 2023-12-31
array(['2023-01-31T00:00:00.000000000', '2023-02-28T00:00:00.000000000', '2023-03-31T00:00:00.000000000', '2023-04-30T00:00:00.000000000', '2023-05-31T00:00:00.000000000', '2023-06-30T00:00:00.000000000', '2023-07-31T00:00:00.000000000', '2023-08-31T00:00:00.000000000', '2023-09-30T00:00:00.000000000', '2023-10-31T00:00:00.000000000', '2023-11-30T00:00:00.000000000', '2023-12-31T00:00:00.000000000'], dtype='datetime64[ns]')
- valid_timePandasIndex
PandasIndex(DatetimeIndex(['2023-01-31', '2023-02-28', '2023-03-31', '2023-04-30', '2023-05-31', '2023-06-30', '2023-07-31', '2023-08-31', '2023-09-30', '2023-10-31', '2023-11-30', '2023-12-31'], dtype='datetime64[ns]', name='valid_time', freq='ME'))
- units :
- C
We can finally plot the climatology in Berlin for the 1991-2020 refrence period against the 2023 montly means:
plt.figure(figsize=(10, 5))
t2m_Berlin_climatology_mean.plot(label="Mean", color="#3498db")
plt.errorbar(
t2m_Berlin_climatology_mean.month,
t2m_Berlin_climatology_mean,
yerr=t2m_Berlin_climatology_std,
fmt="o",
label="Standard Deviation",
color="#a9a9a9"
)
for month in range (1, 11):
t2m_point = t2m_Berlin_2023_monthly_means.sel(valid_time=t2m_Berlin_2023_monthly_means["valid_time.month"]==month)
label = None
if month == 1:
label = "2023"
plt.scatter(month, t2m_point, color="#ff6600", label=label)
plt.title("Surface Temperature climatology in Berlin (DE), 1991-2020")
plt.xticks(t2m_Berlin_climatology_mean.month)
plt.xlabel("Month")
plt.ylabel("Surface Temperature [C]")
plt.legend()
plt.grid(alpha=0.3)
plt.show()