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e.g. Example scripts for EFAS and GloFAS
EFAS
Retrieve data
These exercises require EFAS model output parameters and auxiliary data. You can retrieve them using the CDS API, as described in the code block below:
model outputs
auxiliary data
This is available only when marsurl adaptor will be in production. For the moment I am using the datasets from the test stack.
Plot map discharge
Plot Discharge Timeseries
Plot Soil Wetness Index Map
Plot Snow depth water equivalent map
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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