Radar Precipitation Estimates (Radolan RW product) interpolated onto LfULG Stations Saxony

Radar Precipitation Estimates interpolated onto LfULG Air Quality Measurement / Monitoring Stations

Processing Steps

Radolan RW data were retrieved from DWD Climate Data Center: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/historical/bin/

Radolan RW data were interpolated onto a subset of LfULG Stations using the Nearest Neighbor method

statistics (average, maximum & standard deviation) are provided for a 5 x 5 km**2 cutout

Data Description

data are packed into TAR Archives for each station

individual data are stored as ASCII tables for each month and station (CSV with space as separator)

variable meaning is described in the header section of each file

Example for Data Input with Python

import numpy as np
import xarray as xr
import datetime

def read_rado_dat( filename ):

'''
Reads Radolan RW time series from ASCII files and returns `xarray` Dataset.

Parameters
----------
filename : str
input filename

Returns
-------
rr : xr.Dataset
time series data (rain rates in mm/h)
'''

print(f'.. open {filename}')
dat = np.genfromtxt( filename )
ndat = len(dat)
print(ndat)
time = []
for i in range( ndat ):
d = dat[i]
t = datetime.datetime(int( d[0] ),
int( d[1] ),
int( d[2] ),
int( d[3] ),
int( d[4] ))
time += [t,]

rr = xr.Dataset()
rr['time'] = time
rr['rr'] = xr.DataArray( data = dat[:, 7], dims = 'time', coords = {'time':time})
rr['rr_mean'] = xr.DataArray( data = dat[:, 8], dims = 'time', coords = {'time':time})
rr['rr_max'] = xr.DataArray( data = dat[:, 9], dims = 'time', coords = {'time':time})
rr['rr_std'] = xr.DataArray( data = dat[:, 10], dims = 'time', coords = {'time':time})

m = (rr != -999)

return rr.where( m )