海洋技术专业rainfall2atbd.pdfVIP

EOS/AMSR-E RAINFALL Algorithm Theoretical Basis Document Christian Kummerow and Ralph Ferraro December 2006 The AMSR-E Level 2 rainfall algorithm in rooted in a Bayesian retrieval scheme over oceans and a regression of scattering signals to surface rainfall over land (Wilheit, et al., 2003). The ocean retrieval relies primarily on the emission signal from the rain drops themselves while the land retrieval relies solely on the scattering of high frequency (e.g., 89 GHz) radiation from precipitation sized-ice particles at and above the freezing level. The two approaches are needed due to the vastly different surface emissivities and the resulting differences in the sensor information content over ocean and land, respectively. In order to build a consistent algorithm framework, prior to the launch of the Aqua satellite, the land portion of the algorithm was converted from its original regression form (i.e., Grody, 1991 and updated by Ferraro, 1997) to a Bayesian framework, but in such a fashion as to currently reproduce the original regression equations. Typical reflectivities for ocean and land conditions are shown in Fig. 1. The low reflectivity (large emissivity) over land limits the use of emission signals of the precipitation and forces the land algorithm to exploit only the scattering signals. Figure 1: Reflectivity of typical ocean and land surfaces as a function of incidence angle for horizontal and vertical polarizations. 1 In an atmosphere that absorbs and/or scatters microwave radiation, the radiance is governed by the Equation of Radiative Transfer (RTE) (Chandrashekar, 1960) which may be organized as: dR (θ ,φ ) A + S