wradlib Documentation - Bitbucket

wradlib Documentation, Release 0.1.1
2.5.2 Integrating and accumulating precipitation rates of several radar scans
To avoid reading each radar scan seperately, we first build an array of the timesteps we need, in order to scale the
solution for longer time intervals. Suppose we want to accumulate the precipitation between 18.8.2006 00:05 and
18.8.2006 02:05:
# the datetime library facilitates the work with timesteps
import datetime as dt
import numpy as np
# begin and end of the accumulation interval stripped to a datetime format
t_start = dt.datetime.strptime(’2006-08-18 00:05:00’, ’%Y-%m-%d %H:%M:%S’)
t_end = dt.datetime.strptime(’2006-08-18 02:05:00’, ’%Y-%m-%d %H:%M:%S’)
# empty list container for the resulting timesteps array
times = []
# temporal scan resolution in seconds as increment
t_scan_res = 300.
incr = dt.timedelta(seconds=t_scan_res)
curtime = t_start
while curtime >> print times
[2006-08-18 00:05:00 2006-08-18 00:10:00 2006-08-18 00:15:00
2006-08-18 00:20:00 2006-08-18 00:25:00 2006-08-18 00:30:00
2006-08-18 00:35:00 2006-08-18 00:40:00 2006-08-18 00:45:00
2006-08-18 00:50:00 2006-08-18 00:55:00 2006-08-18 01:00:00
2006-08-18 01:05:00 2006-08-18 01:10:00 2006-08-18 01:15:00
2006-08-18 01:20:00 2006-08-18 01:25:00 2006-08-18 01:30:00
2006-08-18 01:35:00 2006-08-18 01:40:00 2006-08-18 01:45:00
2006-08-18 01:50:00 2006-08-18 01:55:00 2006-08-18 02:00:00]
We create an empty 3-dimensional array (time, azimuth, elevation) for all the radar data:
data_dBZ = np.repeat(np.empty(46080), len(scans)).reshape(len(scans),360,128)
Now we can fill the empty array with radar data (Read and plot raw DWD radar data):
for i,time in enumerate(scans):
f = ’raa00-dx_10908-’ + time.strftime(’%Y%m%d%H%M’) + ’-fbg---bin’
data_dBZ[i,:,:] = wrl.io.readDX(datadir + f)[0]
and compute 5-minute-precipitation depths (Read and plot raw DWD radar data, Converting reflectivities):
data_R_depth = wrl.trafo.r2depth(wrl.zr.z2r(data_Z = wrl.trafo.idecibel(data_dBZ)), t_scan_res)
Before accumulating we finally have to build an array with the accumulated timesteps (e.g. hourly):
# list container for the array of accumulated time steps
accum_times = []
# accumulation interval (3600 = 1 hour)
accum_interval = 3600.
incr = dt.timedelta(seconds=accum_interval)
curtime = t_start
while curtime