Nonlinear statistical retrievals of ice content and rain rate from passive microwave observations of a simulated convective storm

A numerical simulator for analysis of multispectral passive microwave mapping and retrieval is described. This simulator allows evaluation and optimization of satellite-based cloud and precipitation parameter retrieval algorithms. It contains three major components: the forward radiative transfer model, the sensor observation model, and the parameter retrieval algorithm. Simulated spaceborne observations of an oceanic tropical squall sampled at five stages in time are demonstrated for a simplified version of the proposed Earth Observation System (EOS) Multifrequency Imaging Microwave Radiometer (MIMR). The simulator uses a nonlinear statistical retrieval algorithm consisting of a Karhunen-Loeve (KL) transform, a projection operator, a nonlinear inverse mapping and a linear minimum mean-square error estimator. Retrievals of rain rate and integrated ice content are performed for each evolutionary frame at both full spatial resolution (1.5 km) and the degraded spatial resolution of a MIMR-class system. Results are presented for both KL-based and brightness temperature-based retrieval algorithms. It is found that the KL-based algorithm has a reduced complexity and performs better than the brightness temperature-based algorithm for degraded resolution imagery, especially for rain rate retrievals. In addition, rain rate retrievals are more affected by low image resolution than are integrated ice content retrievals. Retrieval accuracy of both rain and integrated ice is also found to depend on the evolutionary stage of the storm