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Calibrating radar wind profiler reflectivity factor using surface disdrometer observations

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 inflated noise power estimates during convective rain events that cause SNR to be biased low. 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 by approximately 3 to 4 dB yr−1 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. The calibrated moments are available on the DOE ARM data archive, and the Python processing code is available on public repositories.

54 ENVIRONMENTAL SCIENCES↗

Calibrated Radar Wind Profiler (RWP) Moments

The SGP Central Facility (C1) radar wind profiler (RWP) was calibrated using nearby surface disdrometer observations. Between 2011 and 2019, the SGP C1 RWP operated in two modes. The vertically pointing mode (named the precipitation mode) transmitted a short and long pulse length to have two different range resolutions and the beam-swinging mode (named the wind mode) transmitted one pulse length into three different beam directions. The precipitation-mode observations were available and calibrated from April 2011 through mid-August 2019. The wind-mode observations were available and calibrated between April 2014 and March 2019. The RWP spectra were processed to account for Nyquist velocity aliasing and coherent integration filtering effects before calculating the spectrum moments. During intense precipitation events, the calculated signal-to-noise ratio (SNR) is biased low due to signal power being distributed across the velocity spectrum such that some signal power is erroneously included in the noise level estimate, causing the noise level to be biased high. To correct for the low SNR bias, a new noise level is estimated using observations without precipitation and the SNR is increased accordingly. The adjusted SNR was converted to radar reflectivity factor and then calibrated against a nearby surface disdrometer. The calibration methodology is fully described in: Williams, CR, J Barrio, PE Johnston, P Muradyan, and S Giangrande. 2023. “Calibrating radar wind profiler reflectivity factor using surface disdrometer observations.” Atmospheric Measurement Techniques, https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1405/

54 ENVIRONMENTAL SCIENCES↗

CSAPR2 cell-tracking data collected during TRACER

One of the challenges of analyzing convective cell properties is quick evolution of the individual convective cells. While the operational radar data provide great a data set to analyze the evolution of radar observables of convective precipitation clouds statistically, previous studies also suggested that, because of the quick evolution of cell life cycle, conventional radar volume scan strategies taking ~5-7 minutes might not capture the detailed evolution. The TRACER campaign deployed CSAPR2, which performed frequent update of RHI and sector PPI scans to track convective cells every < 2 minutes guided by a new cell-tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. The submitted data files are CSAPR2 data in CfRadial format collected during the TRACER field campaign from June to September 2020. The data files include processed radar variables including: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification.

54 ENVIRONMENTAL SCIENCES↗