Process-Based Imprints on Ice-Phase Precipitation Evolution: Quantitative Outcomes and Implications for Radar Remote Sensing

Abstract

Precipitating ice crystals evolve through distinct processes that induce microphysical growth and decay. Process efficacy is governed by the physical attributes of the particles themselves and by ambient properties which are highly varied in winter cyclones, and consequently affect surface precipitation rates and accumulations. Estimates of critical particle attributes are advanced by the ability of air- and spaceborne radars to diagnose dominant microphysical processes, thus evoking a need to quantify process-based imprints on surface accumulations and measurements of radar reflectivity and Doppler velocity. We investigate these imprints from particle-based numerical modeling simulations constrained and validated by measurements collected within prominent winter cyclones over the Olympic Mountains and northeastern United States. These simulations revealed that evolution of particle mass and fall velocity within mixed-phase clouds is dominated by riming with sensitivity to the amount of supercooled liquid water present. Riming is further dependent on efficiencies of concurrent processes, such as aggregation or sublimation. Process-based effects were not readily discernible in simulated reflectivity profiles, but riming expressed a unique density-modulated imprint on Doppler velocity. These findings advance the diagnosis of riming and contextualize discrimination of concurrent microphysical processes from remote sensing radar, which has implications for quantitative constraints of retrieved particle attributes.