Bayesian estimation of precipitating cloud parameters from combined measurements of spaceborne microwave radiometer and radar

Abstract

The objective of this paper is to evaluate the potential of a Bayesian inversion algorithm using microwave multisensor data for the retrieval of surface rainfall rate and cloud parameters. The retrieval scheme is based on the maximum a posteriori probability (MAP) method, extended for the use of both spaceborne passive and active microwave data. The MAP technique for precipitation profiling is also proposed to approach the problem of the radar-swath synthetic broadening; that is, the capability to exploit the combined information also where only radiometric data are available. In order to show an application to airborne data, two case studies are selected within the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). They refer to a stratiform storm region and an intense squall line of two mesoscale convective systems, which occurred over the ocean on February 20 and 22, 1993, respectively. The estimated rainfall rates and columnar hydrometeor contents derived from the proposed algorithms are compared to each other and to radar estimates based on reflectivity-rainrate (Z-R) relationships. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are also discussed. A database of combined measurements acquired at nadir during various TOGA-COARE flights, is used for applying the radar-swath synthetic broadening technique in the case of an along-track radar-failure countermeasure. A simulated test of the latter technique is performed using the case studies of February 20 and 22, 1993.