A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors

Abstract

Numerical weather prediction centers increasingly make use of cloudy and rainy microwave radiances. Currently, the high microwave frequencies are simulated using simplified assumptions regarding the radiative properties of frozen hydrometeors. In particular, one single particle shape is often used for all precipitating frozen particles, all over the globe, and for all cloud types. In this paper, a multi-SSP (single scattering properties) approach for 1D Bayesian inversions is examined. Two experiments were set up: (1) one with three SSPs and (2) one with the previous SSPs plus one which leads to very cold brightness temperature distributions. For that purpose, we used observations from the GPM Microwave Imager radiometer over 2 months period and forecasts from the Météo-France convective scale AROME model. The results showed that mixtures of SSP are chosen by the inversion method for meteorological conditions with low scattering and that a single particle is chosen for those with high scattering to perform the inversions. Despite the fact that no specific weather scenes were found to be associated with a particular SSP the most efficient scattering particles can be favored for some of them.