Quality control of Doppler spectra from a vertically pointing, S-band profiling radar

Abstract

This study describes a novel combination of methods to remove spurious spectral peaks, or “spurs,” from Doppler spectra produced by a vertically pointing, S-band radar. The University of Massachusetts S-band frequency-modulated, continuous-wave radar (UMass FMCW) was deployed to monitor the growth of the CBL over northern Alabama during the VORTEX–Southeast field campaign in 2016. The Doppler spectra contained spurs caused by high-voltage switching power supplies in the traveling wave tube amplifier. In the original data processing scheme for this radar, a median filtering method was used to eliminate most of the spurs, but the largest ones persisted, which significantly degraded the quality of derived radar moments (e.g., reflectivity, Doppler velocity, and spectrum width) and hindered further analysis of these data (e.g., hydrometeor classification and boundary layer height tracking). Our technique for removing the spurs consists of three steps: (i) a Laplacian filter identifies and masks peaks in the spectra that are characteristic of the spurs in shape and amplitude, (ii) an in-painting method then fills in the masked area based on surrounding data, and (iii) the moments data (e.g., reflectivity, Doppler velocity, and spectrum width) are then recomputed using a coherent power technique. This combination of techniques was more effective than the median filter at removing the largest spurs from the Doppler spectra, and preserved more of the underlying Doppler spectral structure of the scatterers. Performance of both the median-filter and the in-painting methods are assessed through statistical analysis of the spectral power differences. Downstream products, such as boundary layer height detection, are more easily derived from the recomputed moments.

This work was funded by NOAA grants NA18OAR4590313 and NA19OAR4590209. Dr. Stanley H. Chan and Dr. Andrew Pazmany advised us on the in-painting and CP techniques, respectively. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the NOAA or the U.S. Department of Commerce. Dr. Rocadenbosch contributed the CBL detection algorithm via project PID2021-126436OB-C21 funded by Ministerio de Ciencia e Investigación (MCIN)/ Agencia Estatal de Investigación (AEI)/ 10.13039/501100011033 and EU project H2020 ATMO-ACCESS (GA-101008004).

Peer Reviewed

Postprint (author's final draft)