87th AMS Annual Meeting

Thursday, 18 January 2007: 8:30 AM
Improving Remotely Sensed Rainfall Estimates over Radar Gap Areas
209 (Henry B. Gonzalez Convention Center)
Shayesteh Mahani, Co Operative Remote Sensing Science and Technology Centre (CREST), New York, NY; and R. Khanbilvardi
Development of a multi-sensor merging approach for improving Satellite-based Precipitation Estimates (SPE) over the radar gap coverage, by merging with ground-based Radar Rainfall (RR) measurements, is the objective of the present study. Enhancement of remotely sensed rainfall estimates is very important because satellites are the only possible source of capturing information from the radar and gauge inaccessible areas. The merging algorithm is capable of extending radar information from pixels with available RR to their neighboring pixels with no radar information by merging SPE with available RR. The first step in this approach is local bias correction of SPE with respect to RR and then applying the merging algorithm to combine SPE with RR. In the present study, high resolution, hourly 4 km x 4km, SPE from Hydro Estimator algorithm (HE) was merged with NEXRAD Stage IV to generate a combined set of rainfall product for the radar gap areas. To be able to validate the generated rainfall against NEXRAD, a gap area with available radar rainfall was assumed. Several study cases in summer 2003 and 2004 were selected to test the developed merging technique. The primary results show that generated rainfall for the radar gap area is more correlated with RR measurement (average CC = 0.67) than original HE with RR (average CC = 0.36) and the RMSE between merged and radar rainfall (average 2.8 mm) is less than the RMSE between satellite and radar rainfall (average 4.48 mm). And also, the pattern and intensity of the generated rainfall for radar gap area are more similar to the pattern and value of RR. This algorithm will further be improved to better calibrate the merged SPE-RR product with ground truth rain gauge observation.

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