How to make use of Dask clusters

How to make use of Dask clusters for parallel computing¶

A DeepESDL example notebook¶

This notebook shows how to use Dask clusters to parallelize workflows. Parallelization is useful when running workflows that require more computing resources than are available locally (or here in your DeepESDL workspace). The methods presented in this notebook take care of setting up the cluster with a desired number of workers, installing the environment on the worker nodes, and performing the parallel computation. When a task is finished and you no longer need the resources, you shut down the cluster with a single line of code, and all resources are shut down.

If you are new to Dask, you can get an introduction here. Please also refer to the [DeepESDL documentation] (https://deepesdl.readthedocs.io/en/latest/guide/jupyterlab/) and visit the platform's website for further information!

Brockmann Consult, 2024

---

To run this notebook you need a python environment with:

• python=3.11
• xcube>=1.1.2
• coiled
• dask
• ipykernel

To create a custom environment, please refer to the documentation.

In order to work with the cluster service, the Dask processing cluster has to be added to your team from the NoR (Network of Resources) portal. The example notebook will only run if your team has already access to the service and a suitable environment is selected.

If your team has access, you have to execute the following line once in your terminal:
$ coiled login --account bc --token $COILED_TOKEN

You just need to do this on the very first usage.

In [1]:

from xcube.util.dask import new_cluster
from dask.distributed import Client
import os

from xcube.util.dask import new_cluster from dask.distributed import Client import os

In [2]:

# mandatory: Dask cluster service mirrors your currently selected environment and does not take the default environment
del os.environ['JUPYTER_IMAGE']

# mandatory: Dask cluster service mirrors your currently selected environment and does not take the default environment del os.environ['JUPYTER_IMAGE']

---

Set up Dask cluster¶

Before starting a cluster, think about a good cluster name as well as how many workers you expect to need for your task. The cluster name is helpful in case your Jupyter notebook crashes or you wish to connect to a running cluster from another notebook. It will then recognize that a cluster with the indicated name is already running and connect to it. 

The following cell creates a cluster with two worker nodes using the default instance type (spot instances). For additional settings, please refer to the xcube documentation.

Whenever you start a cluster, please make sure to shut it down with cluster.shutdown() once finished! Otherwise, your processing units will go unused.

In [3]:

# default instance type selected contains 4 cores
cluster = new_cluster(name='team_computing_monthly_means', n_workers=2)

# default instance type selected contains 4 cores cluster = new_cluster(name='team_computing_monthly_means', n_workers=2)

╭─────────────────────────────── Coiled Cluster ───────────────────────────────╮
│              https://cloud.coiled.io/clusters/592165?account=bc              │
╰──────────────────────────────────────────────────────────────────────────────╯
╭────────────── Overview ──────────────╮╭─────────── Configuration ────────────╮
│                                      ││                                      │
│ Name: team_computing_monthly_means   ││ Region: eu-central-1                 │
│                                      ││                                      │
│ Scheduler Status: started            ││ Scheduler: m6i.xlarge                │
│                                      ││                                      │
│ Dashboard:                           ││ Workers:   m6i.xlarge (2)            │
│ https://cluster-zppgt.dask.host?toke ││                                      │
│ n=gRqjkEMXnTB-t8k4                   ││ Workers Requested: 2                 │
│                                      ││                                      │
╰──────────────────────────────────────╯╰──────────────────────────────────────╯
╭───────────────────────── (2024/09/18 08:49:33 UTC) ──────────────────────────╮
│                                                                              │
│                              All workers ready.                              │
│                                                                              │
│                                                                              │
╰──────────────────────────────────────────────────────────────────────────────╯

Connect your newly started cluster to the Dask client:

In [4]:

# check cluster status
cluster.status

# check cluster status cluster.status

Out[4]:

<Status.running: 'running'>

In [5]:

client = Client(cluster)

client = Client(cluster)

To test the cluster, we will create monthly means for a variable from the Earth System Data Cube provided by DeepESDL. To get more inspiration, when Dask may be useful, check out the example section.

---

Access Data and create monthly means¶

In [6]:

from xcube.core.store import new_data_store
store = new_data_store("s3", root="deep-esdl-public", storage_options=dict(anon=True))
store.list_data_ids()

from xcube.core.store import new_data_store store = new_data_store("s3", root="deep-esdl-public", storage_options=dict(anon=True)) store.list_data_ids()

Out[6]:

['LC-1x2160x2160-1.0.0.levels',
 'SMOS-L2C-OS-20230101-20231231-1W-res0-1x1000x1000.levels',
 'SMOS-L2C-OS-20230101-20231231-1W-res0-53x120x120.zarr',
 'SMOS-L2C-OS-20230101-20231231-1W-res0.zarr',
 'SMOS-L2C-SM-20230101-20231231-1W-res0-1x1000x1000.levels',
 'SMOS-L2C-SM-20230101-20231231-1W-res0-53x120x120.zarr',
 'SMOS-L2C-SM-20230101-20231231-1W-res0.zarr',
 'SMOS-freezethaw-1x720x720-1.0.1.zarr',
 'SMOS-freezethaw-4267x10x10-1.0.1.zarr',
 'SeasFireCube_v3.zarr',
 'black-sea-1x1024x1024.levels',
 'black-sea-256x128x128.zarr',
 'esa-cci-permafrost-1x1151x1641-0.0.2.levels',
 'esdc-8d-0.25deg-1x720x1440-3.0.1.zarr',
 'esdc-8d-0.25deg-256x128x128-3.0.1.zarr',
 'extrAIM-merged-cube-1x86x179.zarr',
 'hydrology-1D-0.009deg-100x60x60-3.0.2.zarr',
 'hydrology-1D-0.009deg-1418x70x76-2.0.0.zarr',
 'hydrology-1D-0.009deg-1x1102x2415-2.0.0.levels',
 'hydrology-1D-0.009deg-1x1102x966-3.0.2.levels',
 'ocean-1M-9km-1x1080x1080-1.4.0.levels',
 'ocean-1M-9km-64x256x256-1.4.0.zarr',
 'polar-100m-1x2048x2048-1.0.1.zarr']

We select a cube, that contains 256 timestamps in one chunk.

In [7]:

dataset = store.open_data('esdc-8d-0.25deg-256x128x128-3.0.1.zarr')
dataset

dataset = store.open_data('esdc-8d-0.25deg-256x128x128-3.0.1.zarr') dataset

Out[7]:

<xarray.Dataset> Size: 353GB
Dimensions:                            (time: 1978, lat: 720, lon: 1440)
Coordinates:
  * lat                                (lat) float64 6kB -89.88 -89.62 ... 89.88
  * lon                                (lon) float64 12kB -179.9 ... 179.9
  * time                               (time) datetime64[ns] 16kB 1979-01-05 ...
Data variables: (12/42)
    aerosol_optical_thickness_550      (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    air_temperature_2m                 (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    bare_soil_evaporation              (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    burnt_area                         (time, lat, lon) float64 16GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    cot                                (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    cth                                (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    ...                                 ...
    sif_rtsif                          (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    sm                                 (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    snow_sublimation                   (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    surface_moisture                   (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    terrestrial_ecosystem_respiration  (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
    transpiration                      (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
Attributes: (12/23)
    Conventions:                CF-1.9
    acknowledgment:             All ESDC data providers are acknowledged insi...
    contributor_name:           ['University of Leipzig', 'Max Planck Institu...
    contributor_url:            ['https://www.uni-leipzig.de/', 'https://www....
    creator_name:               ['University of Leipzig', 'Brockmann Consult ...
    creator_url:                ['https://www.uni-leipzig.de/', 'https://www....
    ...                         ...
    publisher_url:              https://www.earthsystemdatalab.net/
    time_coverage_end:          2021-12-31T00:00:00.000000000
    time_coverage_start:        1979-01-05T00:00:00.000000000
    time_period:                8D
    time_period_reported_day:   5.0
    title:                      Earth System Data Cube (ESDC) v3.0.1

xarray.Dataset

• Dimensions:
  • time: 1978
  • lat: 720
  • lon: 1440
• Coordinates: (3)
  • lat
    (lat)
    float64
    -89.88 -89.62 ... 89.62 89.88

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])

  • lon
    (lon)
    float64
    -179.9 -179.6 ... 179.6 179.9

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])

  • time
    (time)
    datetime64[ns]
    1979-01-05 ... 2021-12-31

    long_name :

    time

    standard_name :

    time

    array(['1979-01-05T00:00:00.000000000', '1979-01-13T00:00:00.000000000',
           '1979-01-21T00:00:00.000000000', ..., '2021-12-15T00:00:00.000000000',
           '2021-12-23T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (42)
  • aerosol_optical_thickness_550
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ESA Aerosol Climate Change Initiative (Aerosol_cci)

    date_modified :

    2022-10-13 03:15:18.312024

    description :

    ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ensemble product), Version 2.6

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Aerosol Optical Thickness at 550 nm

    original_add_offset :

    0.0

    original_name :

    AOD550_mean

    original_scale_factor :

    1.0

    processing_steps :

    ['Loading data using the cciodp xcube data store', 'Resampling by 8-day mean', 'Upsampling to 0.25 degrees using nearest neighbor']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/amt-6-1919-2013']

    reported_day :

    5.0

    source :

    ['https://catalogue.ceda.ac.uk/uuid/c183044b88734442b6d37f5c4f6b0092']

    standard_name :

    atmosphere_optical_thickness_due_to_ambient_aerosol

    temporal_resolution :

    8D

    time_coverage_end :

    2012-04-10T00:00:00.000000000

    time_coverage_start :

    2002-05-21T00:00:00.000000000

    time_period :

    8D

    units :

    1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • air_temperature_2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Mean Air Temperature at 2 m

    original_add_offset :

    0.0

    original_name :

    t2m

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily mean', 'Converting to °C from K', 'Resampling by 8-day mean', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    mean_air_temperature_2m

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    °C

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bare_soil_evaporation
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Bare Soil Evaporation

    original_add_offset :

    0.0

    original_name :

    Eb

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    bare_soil_evaporation

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • burnt_area
    (time, lat, lon)
    float64
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.globalfiredata.org/

    date_modified :

    2022-10-13 14:55:35.002779

    description :

    Global Fire Emissions Database (GFED) 4 Monthly Burnt Area

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Monthly Burnt Area

    original_add_offset :

    0.0

    original_name :

    burnt_area

    original_scale_factor :

    0.01

    processing_steps :

    ['Merging hdf files', 'Resampling by 8-day nearest neighbor']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.1002/jgrg.20042']

    reported_day :

    5.0

    source :

    ['https://www.globalfiredata.org/']

    standard_name :

    burnt_area

    temporal_resolution :

    8D

    time_coverage_end :

    2016-12-30T00:00:00.000000000

    time_coverage_start :

    1995-06-06T00:00:00.000000000

    time_period :

    8D

    units :

    hectares

    Array Chunk Bytes 15.28 GiB 32.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float64 numpy.ndarray

  • cot
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ESA Cloud Climate Change Initiative (Cloud_cci)

    date_modified :

    2022-11-04 13:33:18.450458

    description :

    ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-TERRA monthly gridded cloud properties, version 2.0

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Cloud Optical Thickness

    original_add_offset :

    0.0

    original_name :

    cot

    original_scale_factor :

    1.0

    processing_steps :

    ['Loading data using the cciodp xcube data store', 'Resampling by 8-day nearest neighbor', 'Upsampling to 0.25 degrees using nearest neighbor']

    project :

    DeepESDL

    references :

    ['https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v2.0/DOIs/DOI_ESA_Cloud_cci_MODIS-Terra_v2.0_landingpage.html', 'https://dap.ceda.ac.uk/neodc/esacci/cloud/docs/DataSet_Desc_ESA_Cloud_cci_CC4CL_1.5.pdf']

    reported_day :

    5.0

    source :

    ['https://catalogue.ceda.ac.uk/uuid/f1ab07b5292f4813bd3090b51d270aa8']

    standard_name :

    atmosphere_optical_thickness_due_to_cloud

    temporal_resolution :

    8D

    time_coverage_end :

    2014-12-15T00:00:00.000000000

    time_coverage_start :

    2000-02-22T00:00:00.000000000

    time_period :

    8D

    units :

    1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cth
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ESA Cloud Climate Change Initiative (Cloud_cci)

    date_modified :

    2022-11-04 13:33:18.450458

    description :

    ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-TERRA monthly gridded cloud properties, version 2.0

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Cloud Top Height

    original_add_offset :

    0.0

    original_name :

    cth

    original_scale_factor :

    1.0

    processing_steps :

    ['Loading data using the cciodp xcube data store', 'Resampling by 8-day nearest neighbor', 'Upsampling to 0.25 degrees using nearest neighbor']

    project :

    DeepESDL

    references :

    ['https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v2.0/DOIs/DOI_ESA_Cloud_cci_MODIS-Terra_v2.0_landingpage.html', 'https://dap.ceda.ac.uk/neodc/esacci/cloud/docs/DataSet_Desc_ESA_Cloud_cci_CC4CL_1.5.pdf']

    reported_day :

    5.0

    source :

    ['https://catalogue.ceda.ac.uk/uuid/f1ab07b5292f4813bd3090b51d270aa8']

    standard_name :

    cloud_top_altitude

    temporal_resolution :

    8D

    time_coverage_end :

    2014-12-15T00:00:00.000000000

    time_coverage_start :

    2000-02-22T00:00:00.000000000

    time_period :

    8D

    units :

    km

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ctt
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ESA Cloud Climate Change Initiative (Cloud_cci)

    date_modified :

    2022-11-04 13:33:18.450458

    description :

    ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-TERRA monthly gridded cloud properties, version 2.0

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Cloud Top Temperature

    original_add_offset :

    0.0

    original_name :

    ctt

    original_scale_factor :

    1.0

    processing_steps :

    ['Loading data using the cciodp xcube data store', 'Resampling by 8-day nearest neighbor', 'Upsampling to 0.25 degrees using nearest neighbor']

    project :

    DeepESDL

    references :

    ['https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v2.0/DOIs/DOI_ESA_Cloud_cci_MODIS-Terra_v2.0_landingpage.html', 'https://dap.ceda.ac.uk/neodc/esacci/cloud/docs/DataSet_Desc_ESA_Cloud_cci_CC4CL_1.5.pdf']

    reported_day :

    5.0

    source :

    ['https://catalogue.ceda.ac.uk/uuid/f1ab07b5292f4813bd3090b51d270aa8']

    standard_name :

    air_temperature_at_cloud_top

    temporal_resolution :

    8D

    time_coverage_end :

    2014-12-15T00:00:00.000000000

    time_coverage_start :

    2000-02-22T00:00:00.000000000

    time_period :

    8D

    units :

    K

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • evaporation
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Actual Evaporation

    original_add_offset :

    0.0

    original_name :

    E

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    actual_evaporation

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • evaporation_era5
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Evaporation

    original_add_offset :

    0.0

    original_name :

    e

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily sum', 'Converting to mm from m', 'Resampling by 8-day mean', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    lwe_thickness_of_water_evaporation_amount

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • evaporative_stress
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Evaporative Stress

    original_add_offset :

    0.0

    original_name :

    S

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    evaporative_stress

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • gross_primary_productivity
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    FLUXCOM

    date_modified :

    2022-10-17 22:14:30.401506

    description :

    FLUXCOM

    geospatial_lat_max :

    89.87499928049999

    geospatial_lat_min :

    -89.8750000005

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87499856049996

    geospatial_lon_min :

    -179.8750000005

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Gross Primary Productivity

    original_add_offset :

    0.0

    original_name :

    GPP

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Downsampling to 0.25 deg with mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/bg-13-4291-2016', 'https://doi.org/10.1038/s41597-019-0076-8']

    reported_day :

    5.0

    source :

    ['https://www.fluxcom.org/']

    standard_name :

    gross_primary_productivity_of_carbon

    temporal_resolution :

    8D

    time_coverage_end :

    2020-12-30T00:00:00.000000000

    time_coverage_start :

    2001-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    g C m^-2 d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • interception_loss
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Interception Loss

    original_add_offset :

    0.0

    original_name :

    Ei

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    interception_loss

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • kndvi
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Kernel Normalized Difference Vegetation Index

    original_add_offset :

    0.0

    original_name :

    kNDVI

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.1126/sciadv.abc7447', 'https://github.com/awesome-spectral-indices/awesome-spectral-indices', 'https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://github.com/awesome-spectral-indices/spyndex', 'https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    kNDVI

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • latent_energy
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    FLUXCOM

    date_modified :

    2022-10-17 22:14:30.401506

    description :

    FLUXCOM

    geospatial_lat_max :

    89.87499928049999

    geospatial_lat_min :

    -89.8750000005

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87499856049996

    geospatial_lon_min :

    -179.8750000005

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Latent Energy

    original_add_offset :

    0.0

    original_name :

    LE

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Downsampling to 0.25 deg with mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/bg-13-4291-2016', 'https://doi.org/10.1038/s41597-019-0076-8']

    reported_day :

    5.0

    source :

    ['https://www.fluxcom.org/']

    standard_name :

    surface_upward_latent_heat_flux

    temporal_resolution :

    8D

    time_coverage_end :

    2020-12-30T00:00:00.000000000

    time_coverage_start :

    2001-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    MJ m^-2 d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • max_air_temperature_2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Maximum Air Temperature at 2 m

    original_add_offset :

    0.0

    original_name :

    t2m_max

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily max', 'Converting to °C from K', 'Resampling by 8-day max', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    max_air_temperature_2m

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    °C

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • min_air_temperature_2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Minimum Air Temperature at 2 m

    original_add_offset :

    0.0

    original_name :

    t2m_min

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily min', 'Converting to °C from K', 'Resampling by 8-day min', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    min_air_temperature_2m

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    °C

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nbar_blue
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    bandwidth :

    20.0

    bandwidth_units :

    nm

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Nadir BRDF Adjusted Reflectance of Band 3 (blue)

    original_add_offset :

    0.0

    original_name :

    Nadir_Reflectance_Band3

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    nadir_reflectance_band3

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    wavelength :

    469.0

    wavelength_units :

    nm

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nbar_green
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    bandwidth :

    20.0

    bandwidth_units :

    nm

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Nadir BRDF Adjusted Reflectance of Band 4 (green)

    original_add_offset :

    0.0

    original_name :

    Nadir_Reflectance_Band4

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    nadir_reflectance_band4

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    wavelength :

    555.0

    wavelength_units :

    nm

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nbar_nir
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    bandwidth :

    35.0

    bandwidth_units :

    nm

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Nadir BRDF Adjusted Reflectance of Band 2 (NIR)

    original_add_offset :

    0.0

    original_name :

    Nadir_Reflectance_Band2

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    nadir_reflectance_band2

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    wavelength :

    858.5

    wavelength_units :

    nm

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nbar_red
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    bandwidth :

    50.0

    bandwidth_units :

    nm

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Nadir BRDF Adjusted Reflectance of Band 1 (red)

    original_add_offset :

    0.0

    original_name :

    Nadir_Reflectance_Band1

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    nadir_reflectance_band1

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    wavelength :

    645.0

    wavelength_units :

    nm

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nbar_swir1
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    bandwidth :

    20.0

    bandwidth_units :

    nm

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Nadir BRDF Adjusted Reflectance of Band 5 (SWIR1)

    original_add_offset :

    0.0

    original_name :

    Nadir_Reflectance_Band5

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    nadir_reflectance_band5

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    wavelength :

    1240.0

    wavelength_units :

    nm

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nbar_swir2
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    bandwidth :

    24.0

    bandwidth_units :

    nm

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Nadir BRDF Adjusted Reflectance of Band 6 (SWIR2)

    original_add_offset :

    0.0

    original_name :

    Nadir_Reflectance_Band6

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    nadir_reflectance_band6

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    wavelength :

    1640.0

    wavelength_units :

    nm

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nbar_swir3
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    bandwidth :

    50.0

    bandwidth_units :

    nm

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Nadir BRDF Adjusted Reflectance of Band 7 (SWIR3)

    original_add_offset :

    0.0

    original_name :

    Nadir_Reflectance_Band7

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    nadir_reflectance_band7

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    wavelength :

    2130.0

    wavelength_units :

    nm

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ndvi
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Normalized Difference Vegetation Index

    original_add_offset :

    0.0

    original_name :

    NDVI

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://ntrs.nasa.gov/citations/19740022614', 'https://github.com/awesome-spectral-indices/awesome-spectral-indices', 'https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://github.com/awesome-spectral-indices/spyndex', 'https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    NDVI

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • net_ecosystem_exchange
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    FLUXCOM

    date_modified :

    2022-10-17 22:14:30.401506

    description :

    FLUXCOM

    geospatial_lat_max :

    89.87499928049999

    geospatial_lat_min :

    -89.8750000005

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87499856049996

    geospatial_lon_min :

    -179.8750000005

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Net Ecosystem Exchange

    original_add_offset :

    0.0

    original_name :

    NEE

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Downsampling to 0.25 deg with mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/bg-13-4291-2016', 'https://doi.org/10.1038/s41597-019-0076-8']

    reported_day :

    5.0

    source :

    ['https://www.fluxcom.org/']

    standard_name :

    net_primary_productivity_of_carbon

    temporal_resolution :

    8D

    time_coverage_end :

    2020-12-30T00:00:00.000000000

    time_coverage_start :

    2001-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    g C m^-2 d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • net_radiation
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    FLUXCOM

    date_modified :

    2022-10-17 22:14:30.401506

    description :

    FLUXCOM

    geospatial_lat_max :

    89.87499928049999

    geospatial_lat_min :

    -89.8750000005

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87499856049996

    geospatial_lon_min :

    -179.8750000005

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Net Radiation

    original_add_offset :

    0.0

    original_name :

    Rn

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Downsampling to 0.25 deg with mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/bg-13-4291-2016', 'https://doi.org/10.1038/s41597-019-0076-8']

    reported_day :

    5.0

    source :

    ['https://www.fluxcom.org/']

    standard_name :

    surface_net_radiation_flux

    temporal_resolution :

    8D

    time_coverage_end :

    2020-12-30T00:00:00.000000000

    time_coverage_start :

    2001-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    MJ m^-2 d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nirv
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://lpdaac.usgs.gov/products/mcd43c4v061/

    date_modified :

    2022-10-11 23:51:00.603768

    description :

    MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Reflectance Daily L3 Global 0.05 Deg CMG and Vegetation Indices

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.87499999998977

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000008183

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Near Infrared Reflectance of Vegetation

    original_add_offset :

    0.0

    original_name :

    NIRv

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging hdf files', 'Computing NDVI, NIRv, and kNDVI', 'resampling by 8-day mean', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation', 'Masking water using GLEAM as reference']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.1126/sciadv.1602244', 'https://github.com/awesome-spectral-indices/awesome-spectral-indices', 'https://doi.org/10.5067/MODIS/MCD43C4.061', 'https://www.umb.edu/spectralmass/terra_aqua_modis/v006']

    reported_day :

    5.0

    source :

    ['https://github.com/awesome-spectral-indices/spyndex', 'https://lpdaac.usgs.gov/products/mcd43c4v061/']

    standard_name :

    NIRv

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • open_water_evaporation
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Open-water Evaporation

    original_add_offset :

    0.0

    original_name :

    Ew

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    open_water_evaporation

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • potential_evaporation
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Potential Evaporation

    original_add_offset :

    0.0

    original_name :

    Ep

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    potential_evaporation

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • precipitation_era5
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Total Precipitation

    original_add_offset :

    0.0

    original_name :

    tp

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily sum', 'Converting to mm from m', 'Resampling by 8-day mean', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    total_precipitation

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • radiation_era5
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Surface Net Solar Radiation

    original_add_offset :

    0.0

    original_name :

    ssr

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily mean', 'Resampling by 8-day mean', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    surface_net_downward_shortwave_flux

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    J m^-2

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • root_moisture
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Root-zone Soil Moisture

    original_add_offset :

    0.0

    original_name :

    SMroot

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    root_zone_soil_moisture

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sensible_heat
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    FLUXCOM

    date_modified :

    2022-10-17 22:14:30.401506

    description :

    FLUXCOM

    geospatial_lat_max :

    89.87499928049999

    geospatial_lat_min :

    -89.8750000005

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87499856049996

    geospatial_lon_min :

    -179.8750000005

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Sensible Heat

    original_add_offset :

    0.0

    original_name :

    H

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Downsampling to 0.25 deg with mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/bg-13-4291-2016', 'https://doi.org/10.1038/s41597-019-0076-8']

    reported_day :

    5.0

    source :

    ['https://www.fluxcom.org/']

    standard_name :

    surface_upward_sensible_heat_flux

    temporal_resolution :

    8D

    time_coverage_end :

    2020-12-30T00:00:00.000000000

    time_coverage_start :

    2001-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    MJ m^-2 d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sif_gome2_jj
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://doi.org/10.5194/essd-12-1101-2020

    date_modified :

    2022-10-11 22:36:53.583022

    description :

    Spatially Downscaled Sun-Induced Fluorescence (JJ Method)

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000000003

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Downscaled Daily Corrected Sun-Induced Chlorophyll Fluorescence at 740 nm

    original_add_offset :

    0.0

    original_name :

    SIF

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/essd-12-1101-2020']

    reported_day :

    9.0

    source :

    ['https://data.jrc.ec.europa.eu/dataset/21935ffc-b797-4bee-94da-8fec85b3f9e1']

    standard_name :

    sif

    temporal_resolution :

    16D

    time_coverage_end :

    2018-10-04T00:00:00.000000000

    time_coverage_start :

    2007-01-21T00:00:00.000000000

    time_period :

    8D

    units :

    m W m^-2 sr^-1 nm^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sif_gome2_pk
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://doi.org/10.5194/essd-12-1101-2020

    date_modified :

    2022-10-11 22:43:08.258033

    description :

    Spatially Downscaled Sun-Induced Fluorescence (PK Method)

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000000003

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Downscaled Daily Corrected Sun-Induced Chlorophyll Fluorescence at 740 nm

    original_add_offset :

    0.0

    original_name :

    SIF

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Downsampling to 0.25 deg with mean', 'Interpolating NA with linear interpolation']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/essd-12-1101-2020']

    reported_day :

    9.0

    source :

    ['https://data.jrc.ec.europa.eu/dataset/21935ffc-b797-4bee-94da-8fec85b3f9e1']

    standard_name :

    sif

    temporal_resolution :

    16D

    time_coverage_end :

    2018-12-31T00:00:00.000000000

    time_coverage_start :

    2007-01-21T00:00:00.000000000

    time_period :

    8D

    units :

    m W m^-2 sr^-1 nm^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sif_gosif
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://doi.org/10.3390/rs11050517

    date_modified :

    2022-10-11 22:20:05.841847

    description :

    GOSIF Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data

    geospatial_lat_max :

    89.87499999999999

    geospatial_lat_min :

    -89.87500000000001

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000000003

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Sun-Induced Chlorophyll Fluorescence at 757 nm

    original_add_offset :

    0.0

    original_name :

    sif

    original_scale_factor :

    0.0001

    processing_steps :

    ['Merging tif files', 'Converting water bodies and snow covered areas to NaN', 'Applying original scale factor', 'Downsampling to 0.25 deg with mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.3390/rs11050517']

    reported_day :

    5.0

    source :

    ['https://globalecology.unh.edu/data.html']

    standard_name :

    sif

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    2000-03-01T00:00:00.000000000

    time_period :

    8D

    units :

    W m^-2 sr^-1 um^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sif_rtsif
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://doi.org/10.1038/s41597-022-01520-1

    date_modified :

    2022-10-12 14:26:02.972963

    description :

    Long-term Reconstructed TROPOMI Solar-Induced Fluorescence (RTSIF)

    geospatial_lat_max :

    89.87499999999999

    geospatial_lat_min :

    -89.87500000000001

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87500000000003

    geospatial_lon_min :

    -179.87499999999997

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Sun-Induced Chlorophyll Fluorescence at 740 nm

    original_add_offset :

    0.0

    original_name :

    sif

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging tif files', 'Downsampling to 0.25 deg with mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.1038/s41597-022-01520-1']

    reported_day :

    5.0

    source :

    ['https://figshare.com/articles/dataset/RTSIF_dataset/19336346/2']

    standard_name :

    sif

    temporal_resolution :

    8D

    time_coverage_end :

    2020-12-30T00:00:00.000000000

    time_coverage_start :

    2001-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    m W m^-2 sr^-1 um^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sm
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci)

    date_modified :

    2022-10-13 20:42:41.277132

    description :

    ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Volumetric Soil Moisture

    original_add_offset :

    0.0

    original_name :

    sm

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/soil_moisture/docs/v06.1/ESA_CCI_SM_RD_D2.1_v2_ATBD_v06.1_issue_1.1.pdf', 'https://doi.org/10.5194/essd-11-717-2019', 'https://doi.org/10.1016/j.rse.2017.07.001']

    reported_day :

    5.0

    source :

    ['https://catalogue.ceda.ac.uk/uuid/43d73291472444e6b9c2d2420dbad7d6']

    standard_name :

    volumetric_soil_moisture

    temporal_resolution :

    8D

    time_coverage_end :

    2020-12-30T00:00:00.000000000

    time_coverage_start :

    1979-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    m^3 m^-3

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snow_sublimation
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Snow Sublimation

    original_add_offset :

    0.0

    original_name :

    Es

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    snow_sublimation

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_moisture
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Surface Soil Moisture

    original_add_offset :

    0.0

    original_name :

    SMsurf

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    surface_soil_moisture

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • terrestrial_ecosystem_respiration
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    FLUXCOM

    date_modified :

    2022-10-17 22:14:30.401506

    description :

    FLUXCOM

    geospatial_lat_max :

    89.87499928049999

    geospatial_lat_min :

    -89.8750000005

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.87499856049996

    geospatial_lon_min :

    -179.8750000005

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Terrestrial Ecosystem Respiration

    original_add_offset :

    0.0

    original_name :

    TER

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Downsampling to 0.25 deg with mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/bg-13-4291-2016', 'https://doi.org/10.1038/s41597-019-0076-8']

    reported_day :

    5.0

    source :

    ['https://www.fluxcom.org/']

    standard_name :

    ecosystem_respiration_carbon_flux

    temporal_resolution :

    8D

    time_coverage_end :

    2020-12-30T00:00:00.000000000

    time_coverage_start :

    2001-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    g C m^-2 d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • transpiration
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    https://www.gleam.eu/

    date_modified :

    2022-10-11 16:47:36.234042

    description :

    Global Land Evaporation Amsterdam Model (GLEAM) v3.6a

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    -0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Transpiration

    original_add_offset :

    0.0

    original_name :

    Et

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'resampling by 8-day mean']

    project :

    DeepESDL

    references :

    ['https://doi.org/10.5194/gmd-10-1903-2017', 'https://doi.org/10.5194/hess-15-453-2011']

    reported_day :

    5.0

    source :

    ['GLEAM v3.6a', 'https://www.gleam.eu/']

    standard_name :

    transpiration

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-31T00:00:00.000000000

    time_coverage_start :

    1980-01-05T00:00:00.000000000

    time_period :

    8D

    units :

    mm d^-1

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
           -87.875, -87.625,
           ...
            87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
            89.625,  89.875],
          dtype='float64', name='lat', length=720))

  • lon
    PandasIndex

    PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
           -178.125, -177.875, -177.625,
           ...
            177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
            179.375,  179.625,  179.875],
          dtype='float64', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-05', '1979-01-13', '1979-01-21', '1979-01-29',
                   '1979-02-06', '1979-02-14', '1979-02-22', '1979-03-02',
                   '1979-03-10', '1979-03-18',
                   ...
                   '2021-10-20', '2021-10-28', '2021-11-05', '2021-11-13',
                   '2021-11-21', '2021-11-29', '2021-12-07', '2021-12-15',
                   '2021-12-23', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=1978, freq=None))

• Attributes: (23)

  Conventions :

  CF-1.9

  acknowledgment :

  All ESDC data providers are acknowledged inside each variable

  contributor_name :

  ['University of Leipzig', 'Max Planck Institute', 'Brockmann Consult GmbH']

  contributor_url :

  ['https://www.uni-leipzig.de/', 'https://www.mpg.de/en', 'https://www.brockmann-consult.de/']

  creator_name :

  ['University of Leipzig', 'Brockmann Consult GmbH']

  creator_url :

  ['https://www.uni-leipzig.de/', 'https://www.brockmann-consult.de/']

  date_modified :

  2022-11-25 23:13:03.350030

  geospatial_lat_max :

  89.875

  geospatial_lat_min :

  -89.875

  geospatial_lat_resolution :

  0.25

  geospatial_lon_max :

  179.875

  geospatial_lon_min :

  -179.875

  geospatial_lon_resolution :

  0.25

  id :

  esdc-8d-0.25deg-256x128x128-3.0.1

  license :

  Terms and conditions of the DeepESDL data distribution

  project :

  DeepESDL

  publisher_name :

  DeepESDL Team

  publisher_url :

  https://www.earthsystemdatalab.net/

  time_coverage_end :

  2021-12-31T00:00:00.000000000

  time_coverage_start :

  1979-01-05T00:00:00.000000000

  time_period :

  8D

  time_period_reported_day :

  5.0

  title :

  Earth System Data Cube (ESDC) v3.0.1

Select a variable, for which you would like to calculate monthly means. In this example, we select air_temperature_2m.

In [8]:

air_dataset = dataset[['air_temperature_2m']]
air_dataset

air_dataset = dataset[['air_temperature_2m']] air_dataset

Out[8]:

<xarray.Dataset> Size: 8GB
Dimensions:             (time: 1978, lat: 720, lon: 1440)
Coordinates:
  * lat                 (lat) float64 6kB -89.88 -89.62 -89.38 ... 89.62 89.88
  * lon                 (lon) float64 12kB -179.9 -179.6 -179.4 ... 179.6 179.9
  * time                (time) datetime64[ns] 16kB 1979-01-05 ... 2021-12-31
Data variables:
    air_temperature_2m  (time, lat, lon) float32 8GB dask.array<chunksize=(256, 128, 128), meta=np.ndarray>
Attributes: (12/23)
    Conventions:                CF-1.9
    acknowledgment:             All ESDC data providers are acknowledged insi...
    contributor_name:           ['University of Leipzig', 'Max Planck Institu...
    contributor_url:            ['https://www.uni-leipzig.de/', 'https://www....
    creator_name:               ['University of Leipzig', 'Brockmann Consult ...
    creator_url:                ['https://www.uni-leipzig.de/', 'https://www....
    ...                         ...
    publisher_url:              https://www.earthsystemdatalab.net/
    time_coverage_end:          2021-12-31T00:00:00.000000000
    time_coverage_start:        1979-01-05T00:00:00.000000000
    time_period:                8D
    time_period_reported_day:   5.0
    title:                      Earth System Data Cube (ESDC) v3.0.1

xarray.Dataset

• Dimensions:
  • time: 1978
  • lat: 720
  • lon: 1440
• Coordinates: (3)
  • lat
    (lat)
    float64
    -89.88 -89.62 ... 89.62 89.88

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])

  • lon
    (lon)
    float64
    -179.9 -179.6 ... 179.6 179.9

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])

  • time
    (time)
    datetime64[ns]
    1979-01-05 ... 2021-12-31

    long_name :

    time

    standard_name :

    time

    array(['1979-01-05T00:00:00.000000000', '1979-01-13T00:00:00.000000000',
           '1979-01-21T00:00:00.000000000', ..., '2021-12-15T00:00:00.000000000',
           '2021-12-23T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • air_temperature_2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(256, 128, 128), meta=np.ndarray>

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Mean Air Temperature at 2 m

    original_add_offset :

    0.0

    original_name :

    t2m

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily mean', 'Converting to °C from K', 'Resampling by 8-day mean', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    mean_air_temperature_2m

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    °C

    Array Chunk Bytes 7.64 GiB 16.00 MiB Shape (1978, 720, 1440) (256, 128, 128) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
           -87.875, -87.625,
           ...
            87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
            89.625,  89.875],
          dtype='float64', name='lat', length=720))

  • lon
    PandasIndex

    PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
           -178.125, -177.875, -177.625,
           ...
            177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
            179.375,  179.625,  179.875],
          dtype='float64', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-05', '1979-01-13', '1979-01-21', '1979-01-29',
                   '1979-02-06', '1979-02-14', '1979-02-22', '1979-03-02',
                   '1979-03-10', '1979-03-18',
                   ...
                   '2021-10-20', '2021-10-28', '2021-11-05', '2021-11-13',
                   '2021-11-21', '2021-11-29', '2021-12-07', '2021-12-15',
                   '2021-12-23', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=1978, freq=None))

• Attributes: (23)

  Conventions :

  CF-1.9

  acknowledgment :

  All ESDC data providers are acknowledged inside each variable

  contributor_name :

  ['University of Leipzig', 'Max Planck Institute', 'Brockmann Consult GmbH']

  contributor_url :

  ['https://www.uni-leipzig.de/', 'https://www.mpg.de/en', 'https://www.brockmann-consult.de/']

  creator_name :

  ['University of Leipzig', 'Brockmann Consult GmbH']

  creator_url :

  ['https://www.uni-leipzig.de/', 'https://www.brockmann-consult.de/']

  date_modified :

  2022-11-25 23:13:03.350030

  geospatial_lat_max :

  89.875

  geospatial_lat_min :

  -89.875

  geospatial_lat_resolution :

  0.25

  geospatial_lon_max :

  179.875

  geospatial_lon_min :

  -179.875

  geospatial_lon_resolution :

  0.25

  id :

  esdc-8d-0.25deg-256x128x128-3.0.1

  license :

  Terms and conditions of the DeepESDL data distribution

  project :

  DeepESDL

  publisher_name :

  DeepESDL Team

  publisher_url :

  https://www.earthsystemdatalab.net/

  time_coverage_end :

  2021-12-31T00:00:00.000000000

  time_coverage_start :

  1979-01-05T00:00:00.000000000

  time_period :

  8D

  time_period_reported_day :

  5.0

  title :

  Earth System Data Cube (ESDC) v3.0.1

Next, we will specify how to resample the dataset. It is important to note that this is a lazy operation. After executing the next cell, the air_dataset_monthly should only contain one value for each month in its metadata and structure, even though the data has not yet been resampled.

In [9]:

air_dataset_monthly = air_dataset.resample({'time':'1ME'}).mean()
air_dataset_monthly

air_dataset_monthly = air_dataset.resample({'time':'1ME'}).mean() air_dataset_monthly

Out[9]:

<xarray.Dataset> Size: 2GB
Dimensions:             (time: 516, lat: 720, lon: 1440)
Coordinates:
  * lat                 (lat) float64 6kB -89.88 -89.62 -89.38 ... 89.62 89.88
  * lon                 (lon) float64 12kB -179.9 -179.6 -179.4 ... 179.6 179.9
  * time                (time) datetime64[ns] 4kB 1979-01-31 ... 2021-12-31
Data variables:
    air_temperature_2m  (time, lat, lon) float32 2GB dask.array<chunksize=(1, 128, 128), meta=np.ndarray>
Attributes: (12/23)
    Conventions:                CF-1.9
    acknowledgment:             All ESDC data providers are acknowledged insi...
    contributor_name:           ['University of Leipzig', 'Max Planck Institu...
    contributor_url:            ['https://www.uni-leipzig.de/', 'https://www....
    creator_name:               ['University of Leipzig', 'Brockmann Consult ...
    creator_url:                ['https://www.uni-leipzig.de/', 'https://www....
    ...                         ...
    publisher_url:              https://www.earthsystemdatalab.net/
    time_coverage_end:          2021-12-31T00:00:00.000000000
    time_coverage_start:        1979-01-05T00:00:00.000000000
    time_period:                8D
    time_period_reported_day:   5.0
    title:                      Earth System Data Cube (ESDC) v3.0.1

xarray.Dataset

• Dimensions:
  • time: 516
  • lat: 720
  • lon: 1440
• Coordinates: (3)
  • lat
    (lat)
    float64
    -89.88 -89.62 ... 89.62 89.88

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])

  • lon
    (lon)
    float64
    -179.9 -179.6 ... 179.6 179.9

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])

  • time
    (time)
    datetime64[ns]
    1979-01-31 ... 2021-12-31

    long_name :

    time

    standard_name :

    time

    array(['1979-01-31T00:00:00.000000000', '1979-02-28T00:00:00.000000000',
           '1979-03-31T00:00:00.000000000', ..., '2021-10-31T00:00:00.000000000',
           '2021-11-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • air_temperature_2m
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 128, 128), meta=np.ndarray>

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Mean Air Temperature at 2 m

    original_add_offset :

    0.0

    original_name :

    t2m

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily mean', 'Converting to °C from K', 'Resampling by 8-day mean', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    mean_air_temperature_2m

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    °C

    Array Chunk Bytes 1.99 GiB 64.00 kiB Shape (516, 720, 1440) (1, 128, 128) Dask graph 37152 chunks in 2067 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
           -87.875, -87.625,
           ...
            87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
            89.625,  89.875],
          dtype='float64', name='lat', length=720))

  • lon
    PandasIndex

    PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
           -178.125, -177.875, -177.625,
           ...
            177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
            179.375,  179.625,  179.875],
          dtype='float64', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-31', '1979-02-28', '1979-03-31', '1979-04-30',
                   '1979-05-31', '1979-06-30', '1979-07-31', '1979-08-31',
                   '1979-09-30', '1979-10-31',
                   ...
                   '2021-03-31', '2021-04-30', '2021-05-31', '2021-06-30',
                   '2021-07-31', '2021-08-31', '2021-09-30', '2021-10-31',
                   '2021-11-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=516, freq='ME'))

• Attributes: (23)

  Conventions :

  CF-1.9

  acknowledgment :

  All ESDC data providers are acknowledged inside each variable

  contributor_name :

  ['University of Leipzig', 'Max Planck Institute', 'Brockmann Consult GmbH']

  contributor_url :

  ['https://www.uni-leipzig.de/', 'https://www.mpg.de/en', 'https://www.brockmann-consult.de/']

  creator_name :

  ['University of Leipzig', 'Brockmann Consult GmbH']

  creator_url :

  ['https://www.uni-leipzig.de/', 'https://www.brockmann-consult.de/']

  date_modified :

  2022-11-25 23:13:03.350030

  geospatial_lat_max :

  89.875

  geospatial_lat_min :

  -89.875

  geospatial_lat_resolution :

  0.25

  geospatial_lon_max :

  179.875

  geospatial_lon_min :

  -179.875

  geospatial_lon_resolution :

  0.25

  id :

  esdc-8d-0.25deg-256x128x128-3.0.1

  license :

  Terms and conditions of the DeepESDL data distribution

  project :

  DeepESDL

  publisher_name :

  DeepESDL Team

  publisher_url :

  https://www.earthsystemdatalab.net/

  time_coverage_end :

  2021-12-31T00:00:00.000000000

  time_coverage_start :

  1979-01-05T00:00:00.000000000

  time_period :

  8D

  time_period_reported_day :

  5.0

  title :

  Earth System Data Cube (ESDC) v3.0.1

Next, in order to use the data for further analysis, the monthly resampling needs to be run. The following command will take some time.

In [10]:

computed_air_dataset_monthly = air_dataset_monthly.compute()

computed_air_dataset_monthly = air_dataset_monthly.compute()

In [11]:

computed_air_dataset_monthly

computed_air_dataset_monthly

Out[11]:

<xarray.Dataset> Size: 2GB
Dimensions:             (time: 516, lat: 720, lon: 1440)
Coordinates:
  * lat                 (lat) float64 6kB -89.88 -89.62 -89.38 ... 89.62 89.88
  * lon                 (lon) float64 12kB -179.9 -179.6 -179.4 ... 179.6 179.9
  * time                (time) datetime64[ns] 4kB 1979-01-31 ... 2021-12-31
Data variables:
    air_temperature_2m  (time, lat, lon) float32 2GB -30.6 -30.6 ... -23.28
Attributes: (12/23)
    Conventions:                CF-1.9
    acknowledgment:             All ESDC data providers are acknowledged insi...
    contributor_name:           ['University of Leipzig', 'Max Planck Institu...
    contributor_url:            ['https://www.uni-leipzig.de/', 'https://www....
    creator_name:               ['University of Leipzig', 'Brockmann Consult ...
    creator_url:                ['https://www.uni-leipzig.de/', 'https://www....
    ...                         ...
    publisher_url:              https://www.earthsystemdatalab.net/
    time_coverage_end:          2021-12-31T00:00:00.000000000
    time_coverage_start:        1979-01-05T00:00:00.000000000
    time_period:                8D
    time_period_reported_day:   5.0
    title:                      Earth System Data Cube (ESDC) v3.0.1

xarray.Dataset

• Dimensions:
  • time: 516
  • lat: 720
  • lon: 1440
• Coordinates: (3)
  • lat
    (lat)
    float64
    -89.88 -89.62 ... 89.62 89.88

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])

  • lon
    (lon)
    float64
    -179.9 -179.6 ... 179.6 179.9

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])

  • time
    (time)
    datetime64[ns]
    1979-01-31 ... 2021-12-31

    long_name :

    time

    standard_name :

    time

    array(['1979-01-31T00:00:00.000000000', '1979-02-28T00:00:00.000000000',
           '1979-03-31T00:00:00.000000000', ..., '2021-10-31T00:00:00.000000000',
           '2021-11-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • air_temperature_2m
    (time, lat, lon)
    float32
    -30.6 -30.6 -30.6 ... -23.28 -23.28

    acknowledgment :

    ERA5 hourly data on single levels from 1959 to present

    date_modified :

    2022-11-04 15:41:36.233472

    description :

    ERA5 Reanalysis Products

    geospatial_lat_max :

    89.875

    geospatial_lat_min :

    -89.875

    geospatial_lat_resolution :

    0.25

    geospatial_lon_max :

    179.875

    geospatial_lon_min :

    -179.875

    geospatial_lon_resolution :

    0.25

    license :

    Terms and conditions of the DeepESDL data distribution

    long_name :

    Mean Air Temperature at 2 m

    original_add_offset :

    0.0

    original_name :

    t2m

    original_scale_factor :

    1.0

    processing_steps :

    ['Merging nc files', 'Resampling by daily mean', 'Converting to °C from K', 'Resampling by 8-day mean', 'Resampling to 0.25 degrees using bilinear interpolation']

    project :

    DeepESDL

    references :

    ['https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation']

    reported_day :

    5.0

    source :

    ['https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels']

    standard_name :

    mean_air_temperature_2m

    temporal_resolution :

    8D

    time_coverage_end :

    2021-12-27T00:00:00.000000000

    time_coverage_start :

    1979-01-01T00:00:00.000000000

    time_period :

    8D

    units :

    °C

    array([[[-30.604319 , -30.603416 , -30.602505 , ..., -30.607002 ,
             -30.606113 , -30.605225 ],
            [-30.997593 , -30.994892 , -30.992188 , ..., -31.00566  ,
             -31.00301  , -31.000326 ],
            [-31.401316 , -31.396795 , -31.392254 , ..., -31.415653 ,
             -31.410519 , -31.405838 ],
            ...,
            [-28.86119  , -28.862188 , -28.863615 , ..., -28.858183 ,
             -28.859447 , -28.86039  ],
            [-28.825958 , -28.826452 , -28.826956 , ..., -28.824509 ,
             -28.825    , -28.825487 ],
            [-28.634472 , -28.634634 , -28.63481  , ..., -28.633997 ,
             -28.634163 , -28.634321 ]],
    
           [[-38.47369  , -38.472347 , -38.471    , ..., -38.477657 ,
             -38.476353 , -38.475033 ],
            [-38.85001  , -38.845985 , -38.841953 , ..., -38.861927 ,
             -38.85798  , -38.854004 ],
            [-39.241528 , -39.234924 , -39.22851  , ..., -39.262314 ,
             -39.255016 , -39.24818  ],
    ...
            [-20.72065  , -20.718517 , -20.716475 , ..., -20.72755  ,
             -20.72508  , -20.722828 ],
            [-20.490475 , -20.489168 , -20.487873 , ..., -20.494385 ,
             -20.493073 , -20.491772 ],
            [-20.113615 , -20.113174 , -20.112738 , ..., -20.114912 ,
             -20.11448  , -20.114038 ]],
    
           [[-25.563875 , -25.562918 , -25.561962 , ..., -25.566689 ,
             -25.565746 , -25.564808 ],
            [-25.990168 , -25.987343 , -25.984467 , ..., -25.998623 ,
             -25.995815 , -25.992983 ],
            [-26.477713 , -26.473064 , -26.468483 , ..., -26.49173  ,
             -26.48712  , -26.482414 ],
            ...,
            [-23.76227  , -23.762304 , -23.762413 , ..., -23.76266  ,
             -23.76236  , -23.762276 ],
            [-23.610622 , -23.61064  , -23.610657 , ..., -23.610603 ,
             -23.61062  , -23.610626 ],
            [-23.282463 , -23.282469 , -23.282478 , ..., -23.282448 ,
             -23.282454 , -23.28246  ]]], dtype=float32)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
           -87.875, -87.625,
           ...
            87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
            89.625,  89.875],
          dtype='float64', name='lat', length=720))

  • lon
    PandasIndex

    PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
           -178.125, -177.875, -177.625,
           ...
            177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
            179.375,  179.625,  179.875],
          dtype='float64', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-31', '1979-02-28', '1979-03-31', '1979-04-30',
                   '1979-05-31', '1979-06-30', '1979-07-31', '1979-08-31',
                   '1979-09-30', '1979-10-31',
                   ...
                   '2021-03-31', '2021-04-30', '2021-05-31', '2021-06-30',
                   '2021-07-31', '2021-08-31', '2021-09-30', '2021-10-31',
                   '2021-11-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=516, freq='ME'))

• Attributes: (23)

  Conventions :

  CF-1.9

  acknowledgment :

  All ESDC data providers are acknowledged inside each variable

  contributor_name :

  ['University of Leipzig', 'Max Planck Institute', 'Brockmann Consult GmbH']

  contributor_url :

  ['https://www.uni-leipzig.de/', 'https://www.mpg.de/en', 'https://www.brockmann-consult.de/']

  creator_name :

  ['University of Leipzig', 'Brockmann Consult GmbH']

  creator_url :

  ['https://www.uni-leipzig.de/', 'https://www.brockmann-consult.de/']

  date_modified :

  2022-11-25 23:13:03.350030

  geospatial_lat_max :

  89.875

  geospatial_lat_min :

  -89.875

  geospatial_lat_resolution :

  0.25

  geospatial_lon_max :

  179.875

  geospatial_lon_min :

  -179.875

  geospatial_lon_resolution :

  0.25

  id :

  esdc-8d-0.25deg-256x128x128-3.0.1

  license :

  Terms and conditions of the DeepESDL data distribution

  project :

  DeepESDL

  publisher_name :

  DeepESDL Team

  publisher_url :

  https://www.earthsystemdatalab.net/

  time_coverage_end :

  2021-12-31T00:00:00.000000000

  time_coverage_start :

  1979-01-05T00:00:00.000000000

  time_period :

  8D

  time_period_reported_day :

  5.0

  title :

  Earth System Data Cube (ESDC) v3.0.1

---

Shut down Dask cluster¶

Once you are finished with your computations, make sure to shutdown the cluster to minimize costs.

In [12]:

cluster.shutdown()

cluster.shutdown()

Note, resampling by month, will always put the date for the month on the last day of the month per default.

In [13]:

computed_air_dataset_monthly.air_temperature_2m.isel(time=1).plot.imshow()

computed_air_dataset_monthly.air_temperature_2m.isel(time=1).plot.imshow()

Out[13]:

<matplotlib.image.AxesImage at 0x7f4d2265aed0>

If you wish to persist the dataset to your team storage, please checkout the example notebook 05 SAVE CUBE TO TEAM STORAGE.

In [ ]: