HR: 17:30h
AN: H22H-07    [PDF]
TI: Coastal Rainfall Estimation using AMSR-E
AU: * McCollum, J
EM: Jeff.McCollum@noaa.gov
AF: CICS/ESSIC-NOAA, 4115 CSSB The University of Maryland, College Park, MD 20742 United States
AU: Ferraro, R
EM: Ralph.R.Ferraro@noaa.gov
AF: CICS/ESSIC-NOAA, 4115 CSSB The University of Maryland, College Park, MD 20742 United States
AB: The vast majority of microwave rainfall estimation research has been for either ocean-filled or land-filled fields of view, as the physics for both surface types are quite different. However, neither ocean-based nor land-based methods may be used for coastal pixels that contain a mixture of water and land. Current algorithms for coastal regions perform relatively poorly. We have built upon previous coastal rainfall algorithms developed for the Special Sensor Microwave/Imager (SSM/I) and TRMM Microwave Imager (TMI). Using principal component analysis, we found multi-frequency brightness temperature responses to rainfall over coastal regions, enabling us to do a more accurate rain/no-rain classification. The TMI has similar frequencies and resolutions as AMSR-E, so we could use the co-located TMI and Precipitation Radar (PR) data to determine the principal components related to rainfall. These principal components are effective in distinguishing rain from no-rain AMSR-E pixels, as we show with AMSR-E data. We include global results as well as those from the Eureka, CA, coastal radar AMSR-E validation site.
DE: 1854 Precipitation (3354)
DE: 3360 Remote sensing
DE: 3394 Instruments and techniques
SC: Hydrology [H]
MN: 2003 Fall Meeting