...

• Locally (full control on the process)

GloFAS

...

Example script to crop and extract time series from different GloFAS products:

Code Block
language py title Area cropping: GloFAS Medium-range reforecast  collapse true
## === 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')

...

)

Code Block
language py title Area cropping: GloFAS Seasonal Forecast collapse true
## === 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')

Code Block
language py title Area cropping: GloFAS Seasonal Reforecast

...

collapse true
Code Block
language py
## === 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')

Code Block
language py title Extract timeseries: GloFAS Medium-range

...

Reforecast collapse true

The script shows how to retrieve the control reforecasts product from year 1999 to 2018, relative to the date 2019-01-03, for two station coordinates, one on the river network of the Thames and the other one on the Po river.  Code Block language py 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 # get date index MONTHSDAYS = get_monthsdays() if __name__ == '__main__': c = cdsapi.Client() # set station coordinates (lat,lon) COORDS = { "Thames":[51.35,-0.45], "Po":[44.85, 11.65] } # select all years YEARS = ['%d'%(y) for y in range(1999,2019)] # select all leadtime hours LEADTIMES = ['%d'%(l) for l in range(24,1128,24)] # loop over date index for md in MONTHSDAYS: month = md[0].lower() day = md[1] # loop over 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', 'area':station_point_coord, 'hyear': YEARS, 'hmonth': month , 'hday': day , 'leadtime_hour': LEADTIMES, }, f'glofas_reforecast_{station}_{month}_{day}.grib')

Plot retrieved data: Code Block language py import xarray as xr import numpy as np import matplotlib.pyplot as plt YEARS = range(1999,2019) # read dataset ds = xr.open_dataset("glofas_reforecast_Po_january_03.grib",engine="cfgrib") da = ds.isel(latitude=0,longitude=0).dis24 # plotting parameters n = len(YEARS) colors = plt.cm.jet(np.linspace(0,1,n)) ls = ["-","--",":"] # plot fig,ax = plt.subplots(1,1,figsize=(16,8)) steps = list(range(24,dsa.shape[1]*24+24,24)) for i,year in enumerate(YEARS): y = da.sel(time=f"{year}-01-03").values ax.plot(steps,y,label=f"{year}-01-03",color=colors[i],ls=ls[i%3]) plt.legend(bbox_to_anchor=(0.9,-0.1),ncol=7) ax.set_xlim([0,steps[-1]]) ax.set_xlabel("leadtime hours") ax.set_ylabel("m**3 s**-1") plt.title(f"Po river discharge at {float(ds.latitude.values),round(float(ds.longitude.values),2)}")

...