Abstract
An algorithm is presented for the detection of surface rainfall using passive microwave measurements by satellite radiometers. The technique consists of a two-stage approach to distinguish precipitation signatures from other effects: (1) Contributions from slowly varying parameters (surface type and state) are isolated by comparing observed brightness temperatures to those obtained from previous orbits only containing rain-free observations. (2) Effects of more dynamic parameters, i.e., surface temperature and moisture, are reduced by successive subtraction from the observations by means of principal component analysis. For this purpose, the general signatures of both temperature and moisture variations are deduced from radiative transfer simulations. The fundamentals of this approach are based on a methodology developed by CONNER and PETTY (1998). The technique is applied to TMI observations and compared to co-located measurements of TMI and PR as well as independent techniques over selected regions in Africa, North and South America and India, but less skill over South America. All techniques provide similar rainfall screening skill where our technique showed superior results over Africa, North America, and India. Based on HEIDKE skill scores as a function of rainfall and brightness temperature range, an efcient calibration tool to retrieve near-surface rainfall intensities is provided
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