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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 Medium-range reforecast (with example area subset)
GloFAS Seasonal Forecast (with example area subset)
## === 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')
GloFAS Seasonal Reforecast (with example area subset)
## === 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')
GloFAS Medium-range reforecast (download time series)
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. 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: 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)}")
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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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