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

Radar Precipitation Estimates interpolated onto LfULG Air Quality Measurement / Monitoring Stations <strong>Processing Steps</strong> 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 &amp; standard deviation) are provided for a 5 x 5 km**2 cutout <strong>Data Description</strong> 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 <strong>Example for Data Input with Python</strong> <pre><code class="language-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 )</code></pre>