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- Explore the ECMWF MARS ERA5 catalogue all the way to the parameters of interest.
- 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:
Full Transcript (pdf)Multimedia name Final-ERA5.mp4 width 20% height 20% 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.
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 title Example 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')Expand title Example 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 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 title 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 #!/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')
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