Estimating the Uncertainty in Passive-Microwave Rain Retrievals

Abstract

Current passive-microwave rain-retrieval methods are largely based on databases built offline using cloud models. Since the vertical distribution of hydrometeors within the cloud has a large impact on upwelling brightness temperatures, a forward radiative transfer model can associate microwave radiances with different rain scenarios. Once such a database is available, to estimate the rain from measured brightness temperatures, one would look for the rain scenarios in the database whose associated radiances are closest to the measurements. To understand the uncertainties in this process, the authors have restricted their attention to tropical ocean cases and analyzed the marginal and joint distributions of the radiances observed by the Tropical Rainfall Measuring Mission (TRMM) satellite?s passive-microwave imager and of those in the databases used in the TRMM passive rain retrieval. The authors also calculated the covariances of the rain profiles and brightness temperatures in the TRMM passive-microwave database and derived a simple parametric model for the conditional variance, given measured radiances. These results are used to characterize the uncertainty inherent in the passive-microwave retrieval.