Exploring mm-Wavelength Radar Capabilities for Raindrop Size Distribution Retrieval: Estimating Mass-weighted Mean Diameter from the Differential Backscatter Phase

Abstract

Accurate precipitation characterization relies on the estimation of raindrop size distribution (RDSD) from observations. While various techniques using cm-wavelength radars have been proposed for RDSD retrieval, the potential of mm-wavelength polarimetric radars, offering enhanced spatial and temporal resolution while capturing light to moderate rain, remains unexplored. This study focuses on retrieving the mass-weighted mean volume diameter (Dm) using a dual frequency cloud radar. Since the differential reflectivity (Zdr) is ineffective for Dm retrieval at 94 GHz, and simulations demonstrate a strong dependence of the differential backscatter phase (δco) on Dm, the estimation of δco takes precedence in this paper. Notably, δco remains unaffected by attenuation and polarimetric calibration. Addressing the initial requirement of disentangling backscattering and propagation effects at mm-wavelength, an automatic algorithm is proposed to detect Rayleigh plateaus in the spectral domain. Subsequently, a methodology for estimating δco and its associated error is presented. Leveraging simulation results, confidence intervals for Dm that align with δco confidence intervals are retrieved. The assessment of Dm and its confidence interval at 35 and 94 GHz is conducted employing disdrometer-derived Dm. The results demonstrate a comprehensive concordance within a margin of 0.2 mm, underscoring the cloud radar's efficacy in delineating nuanced variations in raindrop mean diameter versus altitude. The validation process encounters difficulties for Dm below 1 mm, as the disdrometer-derived Dm may exhibit an overestimation, while the cloud radar-derived Dm may exhibit an underestimation. The combination of 35 and 94 GHz serves to diminish the confidence interval associated with the retrieved Dm.