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Table of Contents
maxLevel1
Introduction

The Climate Data Store (CDS) API is the service to allow users to request data from CDS datasets via a python script. These scripts use a number of keywords which vary from dataset to dataset, usually following the sections of the CDS download form.

...

If a keyword which is not allowed is used, the resulting output file could be corrupted or incomplete or an error could occurred when running the script.

ERA5 family datasets

Datasets name

Keywords


Product typevariableyearmonthdaytimeformat**

grid*


areapressure_levelExample script
ERA5 hourly data on single levels from 1979 to present:
'reanalysis-era5-single-levels'
'reanalysis''total_precipitation''2019''01''01'00:00''netcdf'[1.0,1.0][N,W,S,E]


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-single-levels',
    {
        'product_type': 'reanalysis',
        'variable': 'total_precipitation',
        'year': '2019',
        'month': '01',
        'day': '01',
        'time': '00:00',
        'format': 'netcdf',
		'grid':[1.0, 1.0],
        'area': [
            45, 0, 43,
            12,
        ],
    },
    'download.nc')


ERA5 monthly averaged data on single levels from 1979 to present:

'reanalysis-era5-single-levels-monthly-means'
'monthly_averaged_reanalysis''total_precipitation''2019''01'
'00:00''netcdf'[1.0,1.0][N,W,S,E]


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-single-levels-monthly-means',
    {
        'product_type': 'monthly_averaged_reanalysis',
        'variable': 'total_precipitation',
        'year': '2019',
        'month': '01',
        'time': '00:00',
        'format': 'netcdf',
        'area': [
            45, 0, 43,
            11,
        ],
    },
    'download.nc')


ERA5 hourly data on pressure levels from 1979 to present:

'reanalysis-era5-pressure-levels'
'reanalysis''temperature''2019''01''01'00:00''netcdf'[1.0,1.0][N,W,S,E]'850'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-pressure-levels',
    {
        'product_type':'reanalysis',
        'variable': 'temperature',
        'pressure_level': [
            '1', '2', '3',
            '5', '7', '10',
            '20', '30', '50',
            '70', '100', '125',
            '150', '175', '200',
            '225', '250', '300',
            '350', '400', '450',
            '500', '550', '600',
            '650', '700', '750',
            '775', '800', '825',
            '850', '875', '900',
            '925', '950', '975',
            '1000',
        ],
        'year': '2019',
        'month': '01',
        'day': '01',
        'time': '00:00',
        'format': 'netcdf',
        'area': [
            45, 0, 43,
            11,
        ],
    },
    'download.nc')


ERA5 monthly averaged data on pressure levels from 1979 to present:

'reanalysis-era5-pressure-levels-monthly-means'
'monthly_averaged_reanalysis''relative humidity''2019''01'
'00:00''netcdf'[1.0,1.0][N,W,S,E]'850'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-pressure-levels-monthly-means',
    {
        'product_type': 'monthly_averaged_reanalysis',
        'variable': 'relative_humidity',
        'pressure_level': '850',
        'year': '2019',
        'month': '01',
        'time': '00:00',
        'format': 'netcdf',
    },
    'download.nc')


ERA5-Land hourly data from 2001 to present:

'reanalysis-era5-land'

'total_precipitation''2019''01''01'00:00''netcdf'[1.0,1.0][N,W,S,E]


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-land',
    {
        'variable': 'total_precipitation',
        'year': '2019',
        'month': '01',
        'day': '01',
        'time': '00:00',
        'format': 'netcdf',
        'area': [
            45, 0, 43,
            11,
        ],
    },
    'download.nc')


ERA5-Land monthly averaged data from 2001 to present:

'reanalysis-era5-land-monthly-means'
'monthly_averaged_reanalysis''total_precipitation''2019''01'
'00:00''grib'[1.0,1.0][N,W,S,E]


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-land-monthly-means',
    {
        'format': 'grib',
        'product_type': 'monthly_averaged_reanalysis',
        'variable': '2m_temperature',
        'year': '2019',
        'month': '01',
        'time': '00:00',
    },
    'download.grib')


...

** If 'netcdf' selected, the keyword 'grid' must be included.


Datasets name

Keywords


class

date*

levelist*

levtype

param

stream

time

type

grid

area

format**

Example script

reanalysis-era5-complete:

https://apps.ecmwf.int/data-catalogues/era5/?class=ea

'ea'

'2020-01-01'

'1'

'ml'

'155'

'oper'

'00:00'

'an'

[1.0,1.0]

[N,W,S,E]

'grib' or 'netcdf'


Code Block
collapsetrue
#!/usr/bin/env python
import cdsapi
c = cdsapi.Client()
c.retrieve('reanalysis-era5-complete', {
    'class': 'ea',
    'date': '2020-01-01',
    'expver': '1',
    'levelist': '1/to/137',
    'levtype': 'ml',
    'param': '155',
    'stream': 'oper',
    'time': '00:00:00',
    'type': 'an',
	'format': 'grib'
}, 'output.grib')


*The syntax '/to/' could be use for intervals.
** If 'netcdf' selected, the keyword 'grid' must be included.

Seasonal forecasts datasets

Datasets name

Keywords


Product typevariableyearmonthdaytimeformatgridareapressure_leveloriginating_centresystemleadtime_hourleadtime_monthExample script

Seasonal forecast daily data on single levels from 2017 to present:

'seasonal-original-single-levels'

'2m_temperature'

'2019''10''01'00:00''grib'[1.0,1.0][N,W,S,E]
'ukmo''142''6'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'seasonal-original-single-levels',
    {
        'originating_centre': 'ukmo',
        'system': '14',
        'variable': '2m_temperature',
        'year': '2019',
        'month': '10',
        'day': '01',
        'leadtime_hour': '6',
        'format': 'grib',
    },
    'download.grib')


Seasonal forecast anomalies on single levels from 2017 to present:

'seasonal-postprocessed-single-levels'
'ensemble_mean''10m_u_component_of_wind_anomaly''2017''09'

'grib'[1.0,1.0][N,W,S,E]
'ecmwf''4'
'1'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'seasonal-postprocessed-single-levels',
    {
        'format': 'grib',
        'originating_centre': 'ecmwf',
        'system': '4',
        'product_type': 'ensemble_mean',
        'year': '2017',
        'variable': '10m_u_component_of_wind_anomaly',
        'month': '09',
        'leadtime_month': '1',
    },
    'download.grib')


Seasonal forecast monthly statistics on single levels from 2017 to present:

'seasonal-monthly-single-levels'
'monthly_mean''10m_u_component_of_wind''1993''09'

'grib'[1.0,1.0][N,W,S,E]
'ecmwf''5'
'1'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'seasonal-monthly-single-levels',
    {
        'originating_centre': 'ecmwf',
        'system': '5',
        'variable': '10m_u_component_of_wind',
        'product_type': 'monthly_mean',
        'year': '1993',
        'month': '09',
        'leadtime_month': '1',
        'format': 'grib',
    },
    'download.grib')


Seasonal forecast daily data on pressure levels from 2017 to present:

'seasonal-original-pressure-levels'

'v_component_of_wind'

'2018''4''01'00:00''grib'[1.0,1.0][N,W,S,E]'10''ukmo''12''12'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'seasonal-original-pressure-levels',
    {
        'format': 'grib',
        'originating_centre': 'ukmo',
        'system': '12',
        'variable': 'v_component_of_wind',
        'pressure_level': '10',
        'year': '2018',
        'month': '04',
        'day': '01',
        'leadtime_hour': '12',
    },
    'download.grib')


Seasonal forecast anomalies on pressure levels from 2017 to present:

'seasonal-postprocessed-pressure-levels'
'ensemble_mean''geopotential_anomaly''2018''11'

'grib'[1.0,1.0][N,W,S,E]'200''cmcc''3'
'2'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'seasonal-postprocessed-pressure-levels',
    {
        'format': 'grib',
        'originating_centre': 'cmcc',
        'system': '3',
        'variable': 'geopotential_anomaly',
        'pressure_level': '200',
        'product_type': 'ensemble_mean',
        'year': '2018',
        'month': '11',
        'leadtime_month': '2',
    },
    'download.grib')


Seasonal forecast monthly statistics on pressure levels from 2017 to present:

'seasonal-monthly-pressure-levels'

'monthly_mean'

'geopotential''2017''09'


[1.0,1.0][N,W,S,E]'10'

'meteo_france'

'5'
'2'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'seasonal-monthly-pressure-levels',
    {
        'format': 'grib',
        'originating_centre': 'meteo_france',
        'system': '5',
        'variable': 'geopotential',
        'pressure_level': '10',
        'product_type': 'monthly_mean',
        'year': '2017',
        'month': '09',
        'leadtime_month': '2',
    },
    'download.grib')


UERRA datasets

Datasets name

Keywords


variableyearmonthdaytimeformatpressure_levelheight_levelsoil_leveloriginExample script

UERRA regional reanalysis for Europe on single levels from 1961 to present:

'reanalysis-uerra-europe-single-levels'
'total_precipitation''2018''12''30''06:00''grib'


'mescan_surfex'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-uerra-europe-single-levels',
    {
        'origin': 'mescan_surfex',
        'variable': 'total_precipitation',
        'year': '2018',
        'month': '12',
        'day': '30',
        'time': '06:00',
        'format': 'grib',
    },
    'download.grib')


UERRA regional reanalysis for Europe on height levels from 1961 to present:

'reanalysis-uerra-europe-height-levels'
'pressure''1961''01''01''00:00''grib'
'15_m'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-uerra-europe-height-levels',
    {
        'format': 'grib',
        'variable': 'pressure',
        'height_level': '15_m',
        'year': '1961',
        'month': '01',
        'day': '01',
        'time': '00:00',
    },
    'download.grib')


UERRA regional reanalysis for Europe on pressure levels from 1961 to present:

'reanalysis-uerra-europe-pressure-levels'
'geopotential''1967'

'10'

'07''00:00''grib''10'



Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-uerra-europe-pressure-levels',
    {
        'format': 'grib',
        'variable': 'geopotential',
        'pressure_level': '10',
        'year': '1967',
        'month': '10',
        'day': '07',
        'time': '00:00',
    },
    'download.grib')


UERRA regional reanalysis for Europe on soil levels from 1961 to present:

'reanalysis-uerra-europe-soil-levels'
'soil_temperature'1968''08'02''06:00''netcdf'

'2''uerra_harmonie'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-uerra-europe-soil-levels',
    {
        'format': 'netcdf',
        'origin': 'uerra_harmonie',
        'variable': 'soil_temperature',
        'soil_level': '2',
        'year': '1968',
        'month': '08',
        'day': '02',
        'time': '06:00',
    },
    'download.nc')



Datasets name

Keywords


classdatasetdateexpverlevtypeoriginparamstreamtimetypetargetExample script
Complete UERRA regional reanalysis for Europe from 1961 to present'ur''uerra''2017-11-01/to/2017-11-30''prod''sfc''eswi'

'260242'

'oper''00:00:00''an''output.grib'


Code Block
languagepy
collapsetrue
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
server = ECMWFDataServer()
server.retrieve({
    "class": "ur",
    "dataset": "uerra",
    "date": "2017-11-01/to/2017-11-30",
    "expver": "prod",
    "levtype": "sfc",
    "origin": "eswi",
    "param": "260242",
    "stream": "oper",
    "time": "00:00:00",
    "type": "an",
    "target": "output",
})


260242 corresponds to 2 metre relative humidity. You can check the parameter ID from the Parameter Database.

CORDEX regional climate model dataset

Datasets name

Keywords


variablestart yearend yearformatexperimentensemble_membersimulation_versiontemporal_resolutiongcm_modelrcm_modelExample script

CORDEX regional climate model data on single levels for Europe:

'projections-cordex-single-levels'
'mean_precipitation_flux''2006''2008''zip''evaluation''r1i1p1''v1''daily_mean''era_interim''cclm4_8_17'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'projections-cordex-single-levels',
    {
        'experiment': 'evaluation',
        'variable': 'mean_precipitation_flux',
        'gcm_model': 'era_interim',
        'rcm_model': 'cclm4_8_17',
        'ensemble_member': 'r1i1p1',
        'format': 'zip',
        'temporal_resolution': 'daily_mean',
        'simulation_version': 'v1',
        'start_year': '2006',
        'end_year': '2008',
    },
    'download.zip')


CMIP5 climate projections datasets

Datasets name

Keywords


variableformatexperimentensemble_memberperiodmodelExample script

CMIP5 daily data on single levels:

'projections-cmip5-daily-single-levels'

'2m_temperature'

'zip''historical'

'r1i1p1'

'19750101-19991231'

'access1_3'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'projections-cmip5-daily-single-levels',
    {
        'ensemble_member': 'r1i1p1',
        'format': 'zip',
        'experiment': 'historical',
        'variable': '2m_temperature',
        'model': 'access1_3',
        'period': '19750101-19991231',
    },
    'download.zip')


CMIP5 monthly data on single levels:

'projections-cmip5-monthly-single-levels'
'10m_wind_speed''zip''amip''r1i1p1''197901-200812''cnrm_cm5'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'projections-cmip5-monthly-single-levels',
    {
        'ensemble_member': 'r1i1p1',
        'format': 'zip',
        'experiment': 'amip',
        'variable': '10m_wind_speed',
        'model': 'cnrm_cm5',
        'period': '197901-200812',
    },
    'download.zip')


CMIP5 daily data on pressure levels:

'projections-cmip5-daily-pressure-levels'
'u_component_of_wind''zip''historical''r1i1p1''20050101-20051231''access1_0'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'projections-cmip5-daily-pressure-levels',
    {
        'ensemble_member': 'r1i1p1',
        'format': 'zip',
        'experiment': 'historical',
        'variable': 'u_component_of_wind',
        'model': 'access1_0',
        'period': '20050101-20051231',
    },
    'download.zip')


CMIP5 monthly data on pressure levels:

'projections-cmip5-monthly-pressure-levels'
'u_component_of_wind''tgz''rcp_2_6''r3i1p1''200601-210012''ipsl_cm5a_lr'


Code Block
languagepy
collapsetrue
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'projections-cmip5-monthly-pressure-levels',
    {
        'ensemble_member': 'r3i1p1',
        'format': 'tgz',
        'experiment': 'rcp_2_6',
        'variable': 'u_component_of_wind',
        'model': 'ipsl_cm5a_lr',
        'period': '200601-210012',
    },
    'download.tar.gz')



Content by Label
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