The CDS API is a Python service that enables access to CEMS-Flood data on the CDS. It is ideal for users that retrieve large volumes of data or need to automate tasks. This page collects a number of scripts that can work as blueprints for more user-specific requests.
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Instructions about the installation and set-up of the CDS API can be found in How to use the CDS API. |
A user will indicate the data they wish to download by using the radio buttons on the 'Data Download' tab of their chosen dataset on the CDS. After a selection is made on the form, to generate the API request click the 'Show API request' button. This will show the python code to be used to download the data of the bottom of the form.
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You should copy the content of the script into a python file (ex: retrieve_<dataset>.py) and then launch it from a terminal:
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Table of Contents
API script examples:
The following are some examples of API scripts to download the various CEMS-Floods datasets from the CDS.
EFAS Medium-range climatology
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## === retrieve EFAS Medium-Range Climatology ===
import cdsapi
if __name__ == '__main__':
c = cdsapi.Client()
VARIABLES = [
'river_discharge_in_the_last_6_hours', 'snow_depth_water_equivalent',
]
YEARS = ['%02d'%(mn) for mn in range(1991,2022)]
MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']
DAYS = ['%02d'%(mn) for mn in range(1,32)]
for variable in VARIABLES:
for year in YEARS:
c.retrieve(
'efas-historical',
{
'system_version': 'version_4_0',
'variable': variable,
'model_levels': 'surface_level',
'hyear': '1991',
'hmonth': MONTHS,
'hday': DAYS,
'time': '00:00',
'format': 'grib',
},
f'efas_historical_{variable}_{year}.grib')
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EFAS Medium-range forecast
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## === retrieve EFAS Medium-Range Forecast ===
import cdsapi
import datetime
def compute_dates_range(start_date,end_date,loop_days=True):
start_date = datetime.date(*[int(x) for x in start_date.split('-')])
end_date = datetime.date(*[int(x) for x in end_date.split('-')])
ndays = (end_date - start_date).days + 1
dates = []
for d in range(ndays):
dates.append(start_date + datetime.timedelta(d))
if not loop_days:
dates = [i for i in dates if i.day == 1]
else:
pass
return dates
if __name__ == '__main__':
# start the client
c = cdsapi.Client()
# user inputs
START_DATE = '2020-10-14' # first date with available data
END_DATE = '2021-02-28'
LEADTIMES = [str(lt) for lt in range(0,372,6)]
# loop over dates and save to disk
dates = compute_dates_range(START_DATE,END_DATE)
for date in dates:
year = date.strftime('%Y')
month = date.strftime('%m')
day = date.strftime('%d')
print(f"RETRIEVING: {year}-{month}-{day}")
c.retrieve('efas-forecast',
{
'format': 'grib',
'originating_centre':'ecmwf',
'product_type':'ensemble_perturbed_forecasts',
'variable': 'river_discharge_in_the_last_6_hours',
'model_levels': 'surface_level',
'year': year,
'month': month,
'day': day,
'leadtime_hour':LEADTIMES,
'time': '12:00',
},
f'efas_forecast_{year}_{month}_{day}.grib') |
GloFAS Medium-range climatology
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## === retrieve GloFAS Medium-Range Climatology ===
import cdsapi
if __name__ == '__main__':
c = cdsapi.Client()
YEARS = ['%02d'%(mn) for mn in range(1979,2021)]
MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']
DAYS = ['%02d'%(mn) for mn in range(1,32)]
for year in YEARS:
c.retrieve(
'cems-glofas-historical',
{
'system_version':'version_2_1',
'product_type': 'consolidated',
'hydrological_model': 'htessel_lisflood',
'variable': 'river_discharge_in_the_last_24_hours',
'hyear': year,,
'hmonth': MONTHS,
'hday': DAYS,,
'format': 'grib',
},
f'glofas_historical_{year}.grib') |
GloFAS Medium-range forecast
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## === retrieve GloFAS Medium-Range Forecast ===
import cdsapi
import datetime
import warnings
def compute_dates_range(start_date,end_date,loop_days=True):
start_date = datetime.date(*[int(x) for x in start_date.split('-')])
end_date = datetime.date(*[int(x) for x in end_date.split('-')])
ndays = (end_date - start_date).days + 1
dates = []
for d in range(ndays):
dates.append(start_date + datetime.timedelta(d))
if not loop_days:
dates = [i for i in dates if i.day == 1]
else:
pass
return dates
if __name__ == '__main__':
# start the client
c = cdsapi.Client()
# user inputs
START_DATE = '2019-11-05' # first date with available data
END_DATE = '2021-03-15'
LEADTIMES = [str(lt) for lt in range(24,744,24)]
# loop over dates and save to disk
dates = compute_dates_range(START_DATE,END_DATE)
for date in dates:
year = date.strftime('%Y')
month = date.strftime('%m')
day = date.strftime('%d')
print(f"RETRIEVING: {year}-{month}-{day}")
c.retrieve(
'cems-glofas-forecast',
{
'format': 'grib',
'system_version':'operational',
'hydrological_model': 'htessel_lisflood',
'product_type':'ensemble_perturbed_forecasts',
'variable': 'river_discharge_in_the_last_24_hours',
'year': year,
'month': month,
'day': day,
'leadtime_hour':LEADTIMES
},
f'glofas_forecast_{year}_{month}_{day}.grib') |
GloFAS Medium-range reforecast
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## === retrieve GloFAS Medium-Range Reforecast ===
## === subset India, Pakistan, Nepal and Bangladesh region ===
import cdsapi
from datetime import datetime, timedelta
def get_monthsdays():
start, end = datetime(2019, 1, 1), datetime(2019, 12, 31)
days = [start + timedelta(days=i) for i in range((end - start).days + 1)]
monthday = [d.strftime("%B-%d").split("-") for d in days if d.weekday() in [0,3] ]
return monthday
MONTHSDAYS = get_monthsdays()
if __name__ == '__main__':
c = cdsapi.Client()
# user inputs
BBOX = [40.05 ,59.95, 4.95, 95.05] # North West South East
YEARS = ['%d'%(y) for y in range(1999,2019)]
LEADTIMES = ['%d'%(l) for l in range(24,1128,24)]
# submit request
for md in MONTHSDAYS:
month = md[0].lower()
day = md[1]
c.retrieve(
'cems-glofas-reforecast',
{
'system_version': 'version_2_2',
'variable': 'river_discharge_in_the_last_24_hours',
'format': 'grib',
'hydrological_model': 'htessel_lisflood',
'product_type': 'control_reforecast',
'area': BBOX,# < - subset
'hyear': YEARS,
'hmonth': month ,
'hday': day ,
'leadtime_hour': LEADTIMES,
},
f'glofas_reforecast_{month}_{day}.grib') |
GloFAS Seasonal forecast
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## === retrieve GloFAS Seasonal Forecast ===
## === subset South America/Amazon region ===
import cdsapi
if __name__ == '__main__':
c = cdsapi.Client()
YEARS = ['%d'%(y) for y in range(2020,2022)]
MONTHS = ['%02d'%(m) for m in range(1,13)]
LEADTIMES = ['%d'%(l) for l in range(24,2976,24)]
for year in YEARS:
for month in MONTHS:
c.retrieve(
'cems-glofas-seasonal',
{
'variable': 'river_discharge_in_the_last_24_hours',
'format': 'grib',
'year': year,
'month': '12' if year == '2020' else month,
'leadtime_hour': LEADTIMES,
'area': [ 10.95, -90.95, -30.95, -29.95 ]
},
f'glofas_seasonal_{year}_{month}.grib') |
GloFAS Seasonal reforecast
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## === retrieve GloFAS Seasonal Reforecast ===
## === subset South America/Amazon region ===
import cdsapi
if __name__ == '__main__':
c = cdsapi.Client()
YEARS = ['%d'%(y) for y in range(1981,2021)]
MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']
LEADTIMES = ['%d'%(l) for l in range(24,2976,24)]
for year in YEARS:
for month in MONTHS:
c.retrieve(
'cems-glofas-seasonal-reforecast',
{
'system_version': 'version_2_2',
'variable':'river_discharge_in_the_last_24_hours',
'format':'grib',
'hydrological_model':'htessel_lisflood',
'hyear': year,
'hmonth': month,
'leadtime_hour': LEADTIMES,
'area': [ 10.95, -90.95, -30.95, -29.95 ]
},
f'glofas_seasonal_reforecast_{year}_{month}.grib') |