A <scp>1D</scp> Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors

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