Use of Passive Microwave Observations in a Radar Rainfall-Profiling Algorithm

A physically based methodology to incorporate passive microwave observations in a “rain-profiling algorithm” is developed for space- or airborne radars at frequencies exhibiting attenuation. The rain-profiling algorithm deploys a formulation for reflectivity attenuation correction that is mathematically equivalent to that of Hitschfeld and Bordan. In this formulation, the reflectivity–hydrometeor content (or rainfall rate) and reflectivity–attenuation relationships are expressed as a function of one variable in the drop size distribution parameterization, namely, the multiplicative factor in a normalized gamma distribution. The multiplicative factor parameter, mean cloud water content, and one parameter describing the precipitation phase are estimated in a Bayesian framework. This involves the minimization of differences between the 10-, 19-, 37-, and 85-GHz brightness temperature values predicted by a plane-parallel multilayer radiative transfer model and those observed by space- or airborne radiometers. A variational approach is devised to perform the minimization. The methodology is first tested using data simulated using a cloud model and is subsequently applied to coincident airborne brightness temperature and radar profile observations originating in the Kwajalein Experiment of the Tropical Rainfall Measuring Mission (TRMM). Results suggest improvements in rain estimation induced by the inclusion of the brightness temperature information in the retrieval framework if consistent modeling and quantification of errors are performed. Recommendations regarding the application of the method to TRMM satellite observations are formulated based on the findings of the study.