Improved Passive Microwave Retrievals of Precipitation from Space Using Sparse Approximation
This presentation discusses a new passive microwave precipitation retrieval algorithm--the shrunken locally linear embedding algorithm for retrieval of precipitation (ShARP). This algorithm relies on a sparsity promoting regularization technique and makes use of two joint dictionaries of coincident rainfall profiles and their corresponding upwelling spectral brightness temperatures. A sequential detection estimation strategy is adopted, which assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently smooth manifolds with analogous local geometry. The detection step employs a nearest neighbor classification rule, whereas the estimation scheme is equipped with a constrained shrinkage estimator to ensure the stability of retrieval and some physical consistency. The algorithm is examined using coincident observations of the radar and the passive microwave imager on board the TRMM satellite. We focus on a radiometrically complex region--covering the Tibetan highlands, Himalayas, and Ganges-Brahmaputra-Meghna River basins--that is unique in terms of its land surface radiometric properties and precipitation types. Promising results are presented using ShARP over snow-covered surfaces and near coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12 product. It is demonstrated that ShARP can significantly reduce the rainfall overestimation due to the background snow-cover contaminations and markedly improve detection and retrieval of rainfall in the vicinity of coastlines.