A physical-statistical approach to match spaceborne microwave radiometric retrieval of rainfall to Mediterranean climatology

A physical-statistical approach to retrieve precipitating cloud parameters from spaceborne microwave radiometric data is described. A Bayesian Maximum A posteriori Probability inversion scheme is trained by a plane-parallel radiative transfer model applied to statistically-generated cloud and precipitation structures. Initial microphysical a priori information on vertical structures of cloud parameters is derived from a mesoscale cloud-resolving model. In order to adapt simulations to the conditions of the area of interest, climatological constraints are derived on a monthly basis from available radiosounding profiles, raingauge network measurements, SSM/I large sets of data over the Mediterranean region, and co-located METEOSAT infrared measurements. Monthly average and variance maps of clear-air surface emissivity at SSM/I frequencies are also includeded using a METEOSAT-based cloud screening. A validation is carried out by comparing SSM/I estimates with rainrates measured by a raingauge network along the Tiber river basin in Italy during 1995.