A nonlinear multispectral statistical CLEAN-based precipitation parameter-retrieval algorithm

An iterative algorithm incorporating CLEAN(1) deconvolution concepts for pr ecipitation parameter retrieval using passive microwave imagery is presente d. The CLEAN algorithm was originally designed to deconvolve single-channel radio astronomy images. In order to use CLEAN to retrieve precipitation pa rameters from multispectral passive-microwave imagery, extensions of the al gorithm to accommodate nonlinear, multispectral, and statistical data mere designed and implemented. The primary advantage of the nonlinear multispect ral statistical (NMS) CLEAN retrieval algorithm relative to existing algori thms is the use of high-resolution (high-frequency) imagery to guide the re trievals of precipitation parameters from lower resolution (low-frequency) imagery. The NMS-CLEAN retrieval algorithm was used to estimate rain rate (RR) and i ntegrated ice content (IIC) using simulated imagery of oceanic convection a s would be observed from six channels of the proposed Advanced Microwave-Sc anning Radiometer, Both the accuracy and structural detail of the retrieved rain rate mere improved relative to the retrievals from a single-step, non linear, statistical algorithm. Reduced error and improved spatial resolutio n of a more minor magnitude was also seen in the integrated ice-content ret rievals. This study also showed that spatially-simple storm structures resu lted in better performance of the NMS-CLEAN retrieval algorithm.