era5_hourly_reanalysis_single_levels_sa

ERA5 hourly estimates of variables on single levels

Load in Python

from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/atmosphere.yaml") ds = cat["era5_hourly_reanalysis_single_levels_sa"].to_dask()

Working with requester pays data

Several of the datasets within the cloud data catalog are contained in requester pays storage buckets. This means that a user requesting data must provide their own billing project (created and authenticated through Google Cloud Platform) to be billed for the charges associated with accessing a dataset. To set up an GCP billing project and use it for authentication in applications:

Metadata

url https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels
tags ['ocean', 'model', 'atmosphere']

Dataset Contents

Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • latitude: 721
    • longitude: 1440
    • time: 350640
    • latitude
      (latitude)
      float32
      90.0 89.75 89.5 ... -89.75 -90.0
      long_name :
      latitude
      units :
      degrees_north
      array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)
    • longitude
      (longitude)
      float32
      0.0 0.25 0.5 ... 359.5 359.75
      long_name :
      longitude
      units :
      degrees_east
      array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
             3.5975e+02], dtype=float32)
    • time
      (time)
      datetime64[ns]
      1979-01-01 ... 2018-12-31T23:00:00
      long_name :
      time
      array(['1979-01-01T00:00:00.000000000', '1979-01-01T01:00:00.000000000',
             '1979-01-01T02:00:00.000000000', ..., '2018-12-31T21:00:00.000000000',
             '2018-12-31T22:00:00.000000000', '2018-12-31T23:00:00.000000000'],
            dtype='datetime64[ns]')
    • asn
      (time, latitude, longitude)
      float32
      dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>
      long_name :
      Snow albedo
      units :
      (0 - 1)
      Array Chunk
      Bytes 1.46 TB 128.74 MB
      Shape (350640, 721, 1440) (31, 721, 1440)
      Count 11312 Tasks 11311 Chunks
      Type float32 numpy.ndarray
      1440 721 350640
    • d2m
      (time, latitude, longitude)
      float32
      dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>
      long_name :
      2 metre dewpoint temperature
      units :
      K
      Array Chunk
      Bytes 1.46 TB 128.74 MB
      Shape (350640, 721, 1440) (31, 721, 1440)
      Count 11312 Tasks 11311 Chunks
      Type float32 numpy.ndarray