Preprints
https://doi.org/10.5194/egusphere-2022-1405
https://doi.org/10.5194/egusphere-2022-1405
 
10 Jan 2023
10 Jan 2023
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Calibrating Radar Wind Profiler Reflectivity Factor using Surface Disdrometer Observations

Christopher R. Williams1, Joshua Barrio1, Paul E. Johnston2, Paytsar Muradyan3, and Scott E. Giangrande4 Christopher R. Williams et al.
  • 1Smead Aerospace Engineering Sciences Department, University of Colorado, Boulder, 80303, USA
  • 2Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, 80316, USA
  • 3Argonne National Laboratory, Lemont, Illinois, 60439, USA
  • 4Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11793, USA

Abstract. This study uses surface disdrometer reflectivity factor estimates to calibrate the vertical and off-vertical pointing radar beams produced by an Ultra High Frequency (UHF) band radar wind profiler (RWP) deployed at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) Central Facility in northern Oklahoma from April 2011 through July 2019. The methodology consists of five steps. First, the recorded Doppler velocity power spectra are adjusted to account for Nyquist velocity aliasing and coherent integration filtering effects. Second, the spectrum moments are calculated. The third step increases the signal-to-noise ratio (SNR) due to signal power leakage during the Fast Fourier Transform (FFT) calculation, which can exceed 20 dB during convective rain events. The fourth step determines the RWP calibration constant for one radar beam (called the “reference” beam) by comparing uncalibrated RWP reflectivity factors at 500 m above the ground to 1-min resolution surface disdrometer reflectivity factors. The last step uses the calibrated reference beam reflectivity factor to calibrate the other radar beams during precipitation. There are two key findings. The RWP sensitivity decreased approximately 3-to-4 dB/year as the hardware aged. This drift was slow enough that the reference calibration constant can be estimated over 3-month intervals using episodic rain events. Calibrated moments are available on the DOE ARM data archive and Python processing code is available on a public GitHub repository.

Christopher R. Williams et al.

Status: open (until 15 Feb 2023)

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Christopher R. Williams et al.

Model code and software

RWP Python Moment Christopher Williams https://github.com/ChristopherRWilliams/RWP_Python_moments/

Christopher R. Williams et al.

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Short summary
This study uses surface disdrometer observations to calibrate 8-years of 915 MHz radar wind profiler deployed in the central United States in northern Oklahoma. This study had two key findings. First, the radar wind profiler sensitivity decreased approximately 3-to-4 dB/year as the hardware aged. Second, this drift was slow enough that calibration can be performed using 3-month intervals. Calibrated radar wind profiler observations and Python processing code are available on public repositories.