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There are many situations where a user is only interested in a subset of the dataset spatial domain.
For example, when comparing modelled river flow against observations, it is reasonable to being able to extract the timeseries at those point coordinates rather than dealing with many GB of data.
Similarly, when the focus is on a specific catchment it is likely that you want only that part of the spatial domain.
In summary, there two operations that are very popular on CEMS-Flood datasets:
- Area cropping
- Time series extraction
There are different ways to perform those operations:
- From the CDS API (less data is downloaded)
- Locally (full control on the process)
GloFAS
Example script to crop and extract time series from different GloFAS products:
GloFAS Medium-range reforecast (download time series for an event)
This script shows how to retrieve a point time series reforecast on the river Thames for a single forecast reference time, specifically the 11th of July 2007. 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') | In July 2007 a series of flooding events hit the UK, in particular in some areas of the upper Thames catchment up to 120 mm of rain fell between 19th and 20th of July. Plot retrieved data: import xarray as xr import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdates from datetime import datetime,timedelta forecast_reference_time = datetime(2017,7,11) # read perturbed ensembles (dataType:pf) ds = xr.open_dataset("glofas_reforecast_Thames_july_11.grib",engine="cfgrib",backend_kwargs={'filter_by_keys':{'dataType': 'pf'}}) da = ds.isel(latitude=0,longitude=0).dis24 # read control (dataType:cf) cds = xr.open_dataset("glofas_reforecast_Thames_july_11.grib",engine="cfgrib",backend_kwargs={'filter_by_keys':{'dataType': 'cf'}}) cda = cds.isel(latitude=0,longitude=0).dis24 start = forecast_reference_time end = start + timedelta(45) time = pd.date_range(start,end) # plotting parameters ls = ["-","--",":"] locator = mdates.AutoDateLocator(minticks=2, maxticks=46) formatter = mdates.DateFormatter("%b-%d") # plot fig,ax = plt.subplots(1,1,figsize=(16,8)) ax.plot(time,cda.values,label=f"control",color="red") for i,number in enumerate(range(0,10)): y = da.isel(number=number).values ax.plot(time,y,label=f"ensemble {number}",color=colors[i],ls=ls[i%3]) plt.legend(bbox_to_anchor=(1,1),ncol=2) ax.axvspan(datetime(2017,7,19), datetime(2017,7,21), alpha=0.3, color='blue') # highlight event ax.set_xlabel("date") ax.set_ylabel("m**3 s**-1") ax.xaxis.set_major_locator(locator) ax.xaxis.set_major_formatter(formatter) ax.set_xlim([time[0],time[-1]]) plt.annotate("peak\nrainfall\nevent",(datetime(2017,7,19,3),85),rotation=0) plt.xticks(rotation=70) plt.title(f"Forecast reference time: 11-07-2007 - Thames river discharge at {float(ds.latitude.values),round(float(ds.longitude.values-360),2)}") |
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