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Some ERA5 datasets do not appear in CDS, but they are accessible through CDS API, for example, ERA5 model level data and ERA5 monthly means archived at ECMWF's MARS archive. This implies, in some situations, accessing data not available on disks but archived in the tape library.

(warning)Please be aware that there is an additional queueing system for downloading data from the ECMWF's MARS archive - expect several hours to several days for submitted requests to complete at this time.

(warning)You will have to use ECMWF MARS keywords in the CDS API request AND follow the MARS efficiency rules to avoid disappointment with your data retrieval! You must also retrieve dataset 'reanalysis-era5-complete'.

We provide some examples of retrieval scripts but of course a wide range of scripts can be built. Under any circumstances, to make sure you are using the correct MARS keywords and values, we ask you to proceed as follows:

    1. Explore the ECMWF MARS ERA5 catalogue all the way to the parameters of interest.
    2. Use the "View MARS request" feature - this will help you build your own CDS API Python script to retrieve the data through the CDS API. A demonstration on how to use the ERA5 Catalogue is available as part of the video tutorial below:

      Multimedia
      nameFinal-ERA5.mp4
      width20%
      height20%
      Full Transcript (pdf)
    3. To retrieve MARS data efficiently (and get your data quicker!) you should retrieve all the data you need from one tape, then from the next tape, and so on. In most cases, this means retrieving all the data you need for one month, then for the next month, and so on. To find out what data is available on each tape, browse the ERA5 Catalogue and make your way until the bottom of the tree archive (where parameters are listed). Once you will have reached that level of the archive, what you see is what you can find on one single tape. See Retrieval efficiency page for more details.

    4. Transpose the MARS keywords into the CDS API script as shown on the examples below (please note that you need to give  'reanalysis-era5-complete ' as the dataset name in your script) :

      Expand
      titleExample 1: Download model level forecast ERA5 data (temperature) for a given area at a regular lat/lon grid in NetCDF format.


      Code Block
      #!/usr/bin/env python
      import cdsapi
      
      c = cdsapi.Client()
      c.retrieve('reanalysis-era5-complete', {     # do not change this!
          'class'   : 'ea',
          'expver'  : '1',
          'stream'  : 'oper',
          'type'    : 'fc',
          'step'    : '3/to/12/by/3',
          'param'   : '130.128',
          'levtype' : 'ml',
          'levelist': '1/10/50/100',
          'date'    : '2013-01-01',
          'time'    : '06/18',
          'area'    : '80/-50/-25/0', # North, West, South, East. Default: global
          'grid'    : '1.0/1.0', # Latitude/longitude grid: east-west (longitude) and north-south resolution (latitude). Default: reduced Gaussian grid
          'format'  : 'netcdf', # Default: grib
      }, 'temp-fc-ml.nc')
      




      title
      Expand
      titleExample 2: Download ERA5 model level analysis data (temperature) at the default reduced Gaussian grid in GRIB format.


      Code Block
      #!/usr/bin/env python
      import cdsapi
      
      c = cdsapi.Client()
      c.retrieve('reanalysis-era5-complete', {    # do not change this!
          'class'   : 'ea',
          'expver'  : '1',
          'stream'  : 'oper',
          'type'    : 'an',
          'param'   : '130.128',
          'levtype' : 'ml',
          'levelist': '1/10/50/100',
          'date'    : '2013-01-01',
          'time'    : '00/to/23/by/6',
      }, 'temp-an-ml.grib')
      Expand



      .#!/usr/bin/env python import cdsapi c = cdsapi.Client() c.retrieve('reanalysis-era5-complete', { # do not change this! 'class' : 'ea', 'expver' : '1', 'stream' : 'mnth', 'type' : 'an', 'param' : '130.128/157.128', 'levtype' : 'pl', 'levelist': '850', 'date' : '2017-01-01', 'time' : '00:00:00/03:00:00/06:00:00/09:00:00/12:00:00/15:00:00/18:00:00/21:00:00', }, 'monthly-mean-daily-mean-temp-an-sfc.grib')




      Hide content
      title


      Example 3: Download ERA5 monthly mean of daily means data (surface level 2m temperature) at the default reduced Gaussian grid in GRIB format.

      Code Block
      #!/usr/bin/env python
      import cdsapi
      
      c = cdsapi.Client()
      c.retrieve('reanalysis-era5-complete', {    # do not change this!
          'class'   : 'ea',
          'expver'  : '1',
          'stream'  : 'moda',
          'type'    : 'an',
          'param'   : '167.128',
          'levtype' : 'sfc',
          'date'    : '2018-01-01',
          'decade'  : '2010',
      }, 'monthly-mean-daily-mean-temp-an-sfc.grib')
      Expand




      Example 4: Download ERA5 synoptic monthly mean data (pressure level temperature and relative humidity) at the default reduced Gaussian grid in GRIB format
      Code Block


Content by Label
showLabelsfalse
max5
spacesCKB
showSpacefalse
sortmodified
reversetrue
typepage
cqllabel in ("cds","era5") and type = "page" and space = "CKB"
labelscds

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