Difference between revisions of "Csu radartools tutorial"
From Lrose Wiki
(This is an example tutorial for quality controlling and processing a raw radar file using LROSE (RadX), CSU-Radartools, and PyART) |
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=== '''Overview'''=== | === '''Overview'''=== | ||
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| − | + | 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. | |
| − | This | + | We will apply a series of corrections |
* Unfold radial velocity | * Unfold radial velocity | ||
* Calculate Kdp | * Calculate Kdp | ||
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* Calculate the attenuation and differential attenuation | * Calculate the attenuation and differential attenuation | ||
* Despeckle and remove 2nd trip | * Despeckle and remove 2nd trip | ||
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=== '''Required Modules available from GitHub'''=== | === '''Required Modules available from GitHub'''=== | ||
Revision as of 20:33, 2 August 2021
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
Required Modules available from GitHub
First Step
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