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

The objective of this paper is to evaluate the potential of a Bayesian inve rsion algorithm using microwave multisensor data for the retrieval of surfa ce rainfall rate and cloud parameters. The retrieval scheme is based on the maximum a pasteriori probability (MAP) method, extended to the use of both spaceborne passive and active microwave data. The MAP technique for precip itation profiling is also proposed to approach the problem of the radar-swa th synthetic broadening; that is, the capability to exploit the combined in formation also where only radiometric data are available. In order to show an application to airborne data, two case studies are selected within the t ropical ocean-global atmosphere coupled ocean-atmosphere response experimen t (TOGA-COARE). They refer to a stratiform storm region and an intense squa ll line of two mesoscale convective systems, which occurred over ocean on F ebruary 20 and 22, 1993, respectively. The estimated rainfall rates and col umnar hydrometeor contents derived from the proposed algorithms are compare d to each other and to radar estimates based on reflectivity-rainrate (Z-R) relationships. Results in terms of reflectivity profiles and upwelling bri ghtness temperatures, reconstructed from the estimated cloud structures, ar e also discussed. A database of combined measurements acquired at nadir dur ing various TOGA-COARE Eights, is used for applying the radarswath syntheti c broadening technique in the case of along-track radar-failure countermeas ure, A simulated test of the latter technique is performed using the case s tudies of February 20 and 22, 1993.