Csu radartools tutorial

Overview

---

This tutorial will walk through the steps to read and convert a raw radar file, quality control the data (removing unwanted artifacts and echoes, unfold radial velocity and calculate Kdp), then grid the data, and finally process the gridded data to end up with hydrometeor identification, precipitation rates, and DSD information. This is generally focused on surface polarimetric X, C, or S-band radar. We will apply a series of corrections

• Unfold radial velocity
• Calculate Kdp
• Apply thresholds to remove non-meteorological data
• Calculate the attenuation and differential attenuation
• Despeckle and remove 2nd trip

Modules available from GitHub

---

• https://github.com/ARM-DOE/pyart
• https://github.com/CSU-Radarmet/CSU_RadarTools

---

Step-by-step Instructions

---

The Example file is from the C-band polarimetric SEA-POL radar. The raw data are in sigmet native format:

SEA20190917_073004

• First convert the data to cfradial data to be more easily transferable to other programs.
  

We can do this from a command line, but we can also do it from python with the os command.

mydir = '/Users/bdolan/scratch/LROSE/SEA-POL/ppi/'
os.system(f'RadxConvert -f {mydir}SEA20190917_071005 -outdir {mydir}cfrad')

This will put the output in a directory for the date in directory 'cfrad'.

Now we have a file called:
cfrad.20190917_071006.545_to_20190917_071353.149_SEAPOL_SUR.nc

There are a lot of variables in this file, but the ones of interest are: DBZ: Reflectivity ZDR: Differential Reflectivity’ UNKNOWN_ID_82: Correlation Coefficient VEL: Radial velocity SQI: Signal Quality Index PHIDP: Differential phase SNR: Signal to noise ratio

• Next do some quality control using PyART and CSU_Radartools.
  

import numpy as np
from copy import deepcopy
import os
import pyart
from CSU_RadarTools.csu_radartools import csu_kdp
from CSU_RadarTools.csu_radartools import csu_misc

Special handling for SEA-POL:
Blanked sector over the wheelhouse: we will look for large blocks of azimuthal jumps to mask them.
For some reason the correlation coefficient was not correct but can be calculated from the UNKNOWN field

1. 1. 1. These are PECULIARS TO THE SEA-POL DATA. YMMV
         
2. Get the difference between each azimuth and look for jumps larger than 30º. If so, mask the data so it plots correctly.
   

az_diff = np.diff(radar.azimuth['data'])
jumps = np.where(np.abs(az_diff) >= 30.0)[0]
if len(jumps):

   for f in radar.fields.keys():

       for j in jumps:

           radar.fields[f]['data'][j].mask = True

           radar.fields[f]['data'][j+1].mask = True

1. Now correct the Untitled field to transform into the correlation coefficient.
   

ccorr = deepcopy(radar.fields['UNKNOWN_ID_82']['data'])
ccorr[ccorr<0] += 65535
ccorr = (ccorr-1)/65533.0
radar.add_field_like('RHOHV','CC',ccorr)