Vertical wind and drop size distribution retrieval with the CloudCube G-band Doppler radar

Abstract

Macrophysical properties of clouds are influenced by underlying microphysical processes. In practice, there is often an observational gap in bridging the two. For example, our current understanding of aerosol-cloud interaction and cloud-climate feedback is hindered by a lack of robust measurements of the distribution of drop sizes within clouds, especially for the smallest drop sizes. Doppler radar measurements have proven useful in estimating rainfall drop size distributions (DSDs) but face an intermediate challenge of requiring a correction for the presence of vertical air motion. Recent advances in millimeter-wave technology have made radar measurements at increasingly smaller wavelengths possible, allowing for analysis of particle-size-dependent scattering effects to derive estimates of vertical winds and thereby DSDs. This work demonstrates a method of deriving range-resolved DSDs using Doppler spectra at 238 GHz measured by the CloudCube ground-based G-band atmospheric Doppler radar. The observations utilized are of marine boundary layer clouds during March and April 2023 in La Jolla, CA, USA, taken as part of CloudCube's participation in the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) campaign. This method first identifies notches in the velocity spectra and compares them to the theoretical notch velocities predicted by size-dependent backscattering and terminal velocity models to estimate the range-dependent vertical wind. After removing the vertical wind, binned DSDs are retrieved from the zero-wind spectrum. Bulk properties of the precipitation are then derived, including the number concentration, liquid water content, characteristic drop size, and precipitation rate. For the case study presented here, calculated bulk properties are found to be relatively invariant to the forward-model assumptions made in the estimation of the full DSD retrieval. Validation of this method on larger volumes of data would make such retrievals useful tools in assessing physical models of drizzle.