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- Locally (full control on the process)
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GloFAS
CDS API
Time series extraction:
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import cdsapi from datetime import datetime, timedelta def get_monthsdays(start =[2019,1,1],end=[2019,12,31]): # reforecast time index start, end = datetime(*start),datetime(*end) 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 if __name__ == '__main__': c = cdsapi.Client() # station coordinates (lat,lon) COORDS = { "Thames":[51.35,-0.45] } # select date index corresponding to the event MONTHSDAYS = get_monthsdays(start =[2019,7,11],end=[2019,7,11]) YEAR = '2007' LEADTIMES = ['%d'%(l) for l in range(24,1128,24)] # loop over date index (just 1 in this case) for md in MONTHSDAYS: month = md[0].lower() day = md[1] # loop over station coordinates for station in COORDS: station_point_coord = COORDS[station]*2 # coordinates input for the area keyword 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','ensemble_perturbed_reforecasts'], 'area':station_point_coord, 'hyear': YEAR, 'hmonth': month , 'hday': day , 'leadtime_hour': LEADTIMES, }, f'glofas_reforecast_{station}_{month}_{day}.grib') |
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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') |
Local machine
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import cdsapi c = cdsapi.Client() c.retrieve( 'cems-glofas-historical', { 'variable': 'river_discharge_in_the_last_24_hours', 'format': 'grib', 'hydrological_model': 'lisflood', 'product_type': 'intermediate', 'hyear': '2021', 'hmonth': 'january', 'hday': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', ], 'system_version': 'version_3_1', }, 'glofas_historical.grib') |
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When transforming from lat/lon (source coordinates) to projected LAEA (target coordinates), you need to consider that the number of decimal places of the source coordinates affects the target coordinates precision: An interval of 0.001 degrees corresponds to about 100 metres in LAEA. An interval of 0.00001 degrees corresponds to about 1 metre in LAEA. |
CDS API
to update once cropping works....
Time series extraction:
Area cropping:
Local machine
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import cdsapi c = cdsapi.Client() c.retrieve( 'efas-reforecast', { 'format': 'grib', 'product_type': 'ensemble_perturbed_reforecasts', 'variable': 'river_discharge_in_the_last_6_hours', 'model_levels': 'surface_level', 'hyear': '2007', 'hmonth': 'march', 'hday': [ '04', '07', ], 'leadtime_hour': [ '0', '12', '18', '6', ], }, 'efas_reforecast.grib') |
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