Using Doppler Spectra from a Vertically Pointing Polarimetric Weather Radar to Characterize Precipitation Processes

Abstract:

Polarimetric Doppler weather radars provide a wealth of information about precipitation type and amount for operational weather services. While the analysis of weather radar data generally focuses on radar scans performed at slant viewing angles to cover a large area with few radar systems, vertically pointing birdbath scans, i.e., radar observations confined to a narrow cone above the radar site, have been used successfully for monitoring the calibration of individual radar systems. This study explores the potential for using Doppler spectra from such birdbath scans to characterize precipitation processes.

Birdbath scans are collected with the C-band research weather radar of the German Meteorological Service located just north of the Bavarian Alps. Similar to some cloud radars with Doppler capability, Doppler spectra are saved as output from the weather radar birdbath scans, indicating the signal power as a discrete function of hydrometeor fall velocity. Doppler spectra are recorded at a velocity resolution of several cm per s and at a sufficiently high sensitivity to probe the full cloud layer up to the (optical) cloud top.

Features in the Doppler spectra, such as fingerprints of particle growth mechanisms, can be identified and related to airborne in situ measurements of microphysical cloud processes, weather-model output data, and quasi-vertical radar profiles from slant-viewing polarimetric radar observations, thereby gaining insight into precipitation processes in combination with atmospheric dynamics. The Doppler spectra reveal in great detail, e.g., clear differences between snow and rain, a distinct transition from snow to rain in the melting layer, or the simultaneous occurrence of different precipitation processes. By separating the meteorological signal from clutter and background, the main spectral mode and potential side modes (reflecting different precipitation regimes) can be identified. Then, characteristic properties of all modes can be quantified (e.g. spectral moments of individual modes and multimodal properties that indicate mode separation and mixing) toward an automated analysis of the Doppler spectra, combining classical signal-processing and AI data-minining techniques, to enhance the identification of precipitation types and processes for nowcasting methods.