Gm. Skofronick-jackson et Aj. Gasiewski, A nonlinear multispectral statistical CLEAN-based precipitation parameter-retrieval algorithm, IEEE GEOSCI, 38(1), 2000, pp. 226-237
Citations number
28
Categorie Soggetti
Eletrical & Eletronics Engineeing
Journal title
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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.