...
| Code Block | ||||||
|---|---|---|---|---|---|---|
| ||||||
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')
|
Local machine
EFAS
CDS API
to update once cropping works....
Local machine
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.
| Warning | ||
|---|---|---|
| ||
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. |
CDS API
to update once cropping works....
Local machine
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.
| Info | ||||
|---|---|---|---|---|
| ||||
EFAS data's x | ||||
| Alert | ||||
| ||||
| Info | ||||
| ||||
EFAS data's x and y coordinates 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. |
...
| Code Block | ||||
|---|---|---|---|---|
| ||||
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
|
...
.0,47.71290346585623,-1.6892419697226784,2007-03-04 00:00:00,13.782227
|
Cropping to a bounding box.
| Code Block | ||||||
|---|---|---|---|---|---|---|
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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("mars_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") |