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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 focusing on a specific catchment it is likely that you want to deal with only that part of the spatial domain.
In summary, there are two operations of data size reduction that are very popular on CEMS-Flood datasets, area cropping and time series extraction.
There are two ways to perform those operations:
- Remotely - Using the CDS API to perform the operation remotely on the CDS compute nodes and retrieve only the reduced data.
- Locally - Using the CDS API to retrieve the entire data and perform the operation locally.
This section provides scripts for both cases and for both CEMS-Flood products, GloFAS and EFAS.
For the exercise on extracting time series on the local machine, we are going to use the latitude and longitude coordinates from a tiny subset of the GRDC dataset. Copy the content of the box into an empty file named "GRDC.csv", the file should reside in your working folder.
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grdc_no,wmo_reg,sub_reg,river,station,country,lat,long,area,altitude 6118010,6,18,CLAIE,SAINT-JEAN-BREVELAY,FR,47.824831,-2.703308,137.0,99.98 6118015,6,18,YVEL,LOYAT (PONT D129),FR,47.993815,-2.368849,315.0,88.26 6118020,6,18,"ARON, RUISSEAU D'",GRAND-FOUGERAY (LA BERNADAISE),FR,47.71222,-1.690835,118.0,68.0 6118025,6,18,"CANUT, RUISSEAU DE",SAINT-JUST (LA RIVIERE COLOMBEL),FR,47.775309,-1.979609,37.0,80.22 6118030,6,18,AFF,PAIMPONT (PONT DU SECRET),FR,47.981631,-2.143762,30.2,119.01 6118050,6,18,COET-ORGAN,QUISTINIC (KERDEC),FR,47.904164,-3.201265,47.7,94.42 6118060,6,18,EVEL,GUENIN,FR,47.899928,-2.975167,316.0,95.16 6118070,6,18,STER-GOZ,BANNALEC (PONT MEYA),FR,47.906833,-3.752172,69.7,85.08 6118080,6,18,MOROS,CONCARNEAU (PONT D22),FR,47.882934,-3.875375,20 |
Then, retrieve the following datasets into the working folder.
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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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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') |
GloFAS
Remote processing
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') |
Area cropping:
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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') |
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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') |
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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 processing
Time series extraction:
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import xarray as xr import pandas as pd parameter = "dis24" ds = xr.open_dataset("glofas_historical.grib", engine="cfgrib",backend_kwargs={'time_dims':['time']}) df = pd.read_csv("GRDC.csv") total = len(df) rows = [] count = 0 for lon, lat, id in zip(df.long, df.lat, df.grdc_no): extracted = ds.sel(longitude=lon, latitude=lat, method="nearest")[parameter] df_temp = extracted.drop_vars(["surface"]).to_dataframe().reset_index() df_temp["grdc"] = str(id) df_temp = df_temp.set_index(["grdc", "time"]) rows.append(df_temp) count += 1 print(f"progress: {count/total*100} %") out = pd.concat(rows) out.to_csv("extracted.csv", index="grdc") |
Area cropping:
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import xarray as xr # Rhine's basin bounding box bbox = [50.972204, 46.296530, 5.450796, 11.871059] # N,S,W,E ds = xr.open_dataset("glofas_historical.grib", engine="cfgrib") ds_cropped = ds.sel( longitude=slice(bbox[2], bbox[3]), latitude=slice(bbox[0], bbox[1]) ) ds_cropped.to_netcdf("glofas_historical_cropped.nc") |
EFAS
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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. |
Remote processing
to update once cropping works....
Time series extraction:
Area cropping:
Local processing
Time series extraction:
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EFAS's x and y coordinates, when converted from GRIB to NetCDF, are not projected coordinates but matrix indexes (i, j), It is necessary to download the upstream area static file that contains the projected coordinates and replace it in EFAS. |
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import xarray as xr import pandas as pd from pyproj import Transformer,CRS parameter = "dis06" ds = xr.open_dataset("efas_reforecast.grib", engine="cfgrib") df = pd.read_csv("GRDC.csv") uparea = xr.open_dataset("ec_uparea4.0.nc") # the upstream area # replace x, y ds["x"] = uparea["x"] ds["y"] = uparea["y"] # define reprojection parameters laea_proj = CRS.from_proj4("+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs") transformer = Transformer.from_crs('epsg:4326', laea_proj, always_xy=True) total = len(df) rows = [] count = 0 for lon, lat, id in zip(df.long, df.lat, df.grdc_no): x1, y1 = transformer.transform(lon, lat) extracted = ds.sel(x=x1, y=y1, number = 1, method="nearest")[parameter] df_temp = extracted.drop_vars(["surface", "number"]).to_dataframe().reset_index() df_temp["grdc"] = str(id) df_temp = df_temp.drop("step", axis=1) df_temp = df_temp.set_index(["grdc","time"]) rows.append(df_temp) count += 1 print(f"progress: {count/total*100} %") out = pd.concat(rows) out.to_csv("extracted.csv", index="grdc") |
Area cropping:
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import xarray as xr from pyproj import Transformer, CRS import numpy as np # Rhine's basin bounding box bbox = [50.972204, 46.296530, 5.450796, 11.871059] # N,S,W,E ds = xr.open_dataset("efas_reforecast.grib", engine="cfgrib") uparea = xr.open_dataset("ec_uparea4.0.nc") # replace x, y ds["x"] = uparea["x"] ds["y"] = uparea["y"] # define reprojection parameters laea_proj = CRS.from_proj4( "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs" ) transformer = Transformer.from_crs("epsg:4326", laea_proj, always_xy=True) we = bbox[2:] ns = bbox[:2] we_xy, ns_xy = transformer.transform(we, ns) we_xy = [np.floor(we_xy[0]), np.ceil(we_xy[1])] ns_xy = [np.ceil(ns_xy[0]), np.floor(ns_xy[1])] ds_cropped = ds.sel( x=slice(we_xy[0], we_xy[1]), y=slice(ns_xy[0], ns_xy[1]) ) ds_cropped.to_netcdf("efas_forecast_cropped.nc") |