Difference between revisions of "Csu radartools tutorial"
From Lrose Wiki
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*First convert the data to cfradial data to be more easily transferable to other programs.<br/> | *First convert the data to cfradial data to be more easily transferable to other programs.<br/> | ||
We can do this from a command line, but we can also do it from python with the os command.<br/> | We can do this from a command line, but we can also do it from python with the os command.<br/> | ||
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<code> | <code> | ||
mydir = '/Users/bdolan/scratch/LROSE/SEA-POL/ppi/'<br/> | mydir = '/Users/bdolan/scratch/LROSE/SEA-POL/ppi/'<br/> | ||
os.system(f'RadxConvert -f {mydir}SEA20190917_071005 -outdir {mydir}cfrad')</code></br> | os.system(f'RadxConvert -f {mydir}SEA20190917_071005 -outdir {mydir}cfrad')</code></br> | ||
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This will put the output in a directory for the date in directory 'cfrad'.<br/> | This will put the output in a directory for the date in directory 'cfrad'.<br/> | ||
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Now we have a file called:<br/> | Now we have a file called:<br/> | ||
cfrad.20190917_071006.545_to_20190917_071353.149_SEAPOL_SUR.nc<br/> | cfrad.20190917_071006.545_to_20190917_071353.149_SEAPOL_SUR.nc<br/> | ||
| − | + | <br/> | |
There are a lot of variables in this file, but the ones of interest are: | There are a lot of variables in this file, but the ones of interest are: | ||
DBZ: Reflectivity | DBZ: Reflectivity | ||
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</br> | </br> | ||
*Next do some quality control using PyART and CSU_Radartools.</br> | *Next do some quality control using PyART and CSU_Radartools.</br> | ||
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<code lang="python" line> | <code lang="python" line> | ||
import numpy as np</br> | import numpy as np</br> | ||
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from CSU_RadarTools.csu_radartools import csu_misc</br> | from CSU_RadarTools.csu_radartools import csu_misc</br> | ||
</code> | </code> | ||
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Revision as of 17:50, 18 August 2021
Contents
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
Scientific Background
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 pyart
from CSU_RadarTools.csu_radartools import csu_kdp
from CSU_RadarTools.csu_radartools import csu_misc