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.
  

RadxConvert -f SEA20190917_073004 -outdir .

This will put the output in a directory for the date at the current level (‘.’).

Now we have a file called: cfrad.20190917_073005.718_to_20190917_073349.992_SEAPOL_v1_PISTON_RMAP_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 pyart
from CSU_RadarTools.csu_radartools import csu_kdp
from CSU_RadarTools.csu_radartools import csu_misc


%matplotlib inline