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| Table of Contents |
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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') |
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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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') |
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") |
...
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') |
Time series extraction:
We are going to extract EFAS reforecast's timeseries at locations defined by latitude and longitude coordinates from a tiny subset of the GRDC dataset.
...
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grdc,time,y,x,latitude,longitude,valid_time,dis06 6118010,2007-03-04,2827500.0,3372500.0,47.82327782578349,-2.7316459165737292,2007-03-04 00:00:00,9.68457 6118010,2007-03-04,2827500.0,3372500.0,47.82327782578349,-2.7316459165737292,2007-03-04 06:00:00,9.5390625 6118010,2007-03-04,2827500.0,3372500.0,47.82327782578349,-2.7316459165737292,2007-03-04 12:00:00,9.783691 6118010,2007-03-04,2827500.0,3372500.0,47.82327782578349,-2.7316459165737292,2007-03-04 18:00:00,9.83252 6118010,2007-03-07,2827500.0,3372500.0,47.82327782578349,-2.7316459165737292,2007-03-07 00:00:00,11.904785 6118010,2007-03-07,2827500.0,3372500.0,47.82327782578349,-2.7316459165737292,2007-03-07 06:00:00,12.73584 6118010,2007-03-07,2827500.0,3372500.0,47.82327782578349,-2.7316459165737292,2007-03-07 12:00:00,12.832031 6118010,2007-03-07,2827500.0,3372500.0,47.82327782578349,-2.7316459165737292,2007-03-07 18:00:00,13.336914 6118015,2007-03-04,2842500.0,3402500.0,48.001715448932764,-2.3682021184138047,2007-03-04 00:00:00,11.508301 6118015,2007-03-04,2842500.0,3402500.0,48.001715448932764,-2.3682021184138047,2007-03-04 06:00:00,11.17334 6118015,2007-03-04,2842500.0,3402500.0,48.001715448932764,-2.3682021184138047,2007-03-04 12:00:00,11.09082 6118015,2007-03-04,2842500.0,3402500.0,48.001715448932764,-2.3682021184138047,2007-03-04 18:00:00,11.20752 6118015,2007-03-07,2842500.0,3402500.0,48.001715448932764,-2.3682021184138047,2007-03-07 00:00:00,14.920898 6118015,2007-03-07,2842500.0,3402500.0,48.001715448932764,-2.3682021184138047,2007-03-07 06:00:00,15.956055 6118015,2007-03-07,2842500.0,3402500.0,48.001715448932764,-2.3682021184138047,2007-03-07 12:00:00,16.127441 6118015,2007-03-07,2842500.0,3402500.0,48.001715448932764,-2.3682021184138047,2007-03-07 18:00:00,15.900879 6118020,2007-03-04,2802500.0,3447500.0,47.71290346585623,-1.6892419697226784,2007-03-04 00:00:00,13.782227 |
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") |
...