HR: 0800h
AN: IN21A-1172 [Abstracts]
TI: Validation of Satellite Based NESDIS Rainfall Products
AU: * Ganguli, K
EM: kgangul00@ccny.cuny.edu
AF: NOAA Cooperative Remote Sensing Science & Technology Centre (CREST), Steinman Hall
140th St & Convent Ave, New York, NY 10031
AU: Mahani, S
EM: manahi@ce.ccny.cuny.edu
AF: NOAA Cooperative Remote Sensing Science & Technology Centre (CREST), Steinman Hall
140th St & Convent Ave, New York, NY 10031
AU: Khanbilvardi, R
EM: rk@ce.ccny.cuny.edu
AF: NOAA Cooperative Remote Sensing Science & Technology Centre (CREST), Steinman Hall
140th St & Convent Ave, New York, NY 10031
AB:
The objective of this study is to develop a statistical approach for validating the satellite-based NESDIS rainfall products.
Evaluation of rainfall products will be used for improving their related algorithms. Three of NESDIS rainfall algorithms:
Hydro-Estimator (HE) algorithm, GOES Multi-Spectral Rainfall Algorithm (GMSRA), and IR/microwave Blended Algorithm (Blended)
have been selected for this study. Capability of each NESDIS rainfall product has been examined against hourly NEXRAD
Stage-IV rainfall at 4km x 4km resolution, and hourly rain gauge observations. Four six hour storm events were considered
for testing the performance of every algorithm, with respect to seasonal variability, climate conditions, and storm types,
over a 2°x 2° area in Hernando County, Florida (28°N-30°N and 81°-83°W). The preliminary
results demonstrated that HE estimates mis-located rainfall with lower intensity compared to the NEXRAD rainfall during the
winter. But in the summer, HE estimates rainfall with a remarkable improvement quantitatively and spatially even with
compare to the rain gauge observations. The preliminary results confirmed the fact that the HE algorithm has shown a
substantially improved ability to estimate precipitation with compare to the GMSRA and Blend algorithms. The time series
analysis also confirmed that the satellite-based rainfall retrieval algorithms, in general, perform better for the warm
season storms in comparison with the cold season storms
DE: 1855 Remote sensing (1640)
DE: 1872 Time series analysis (3270, 4277, 4475)
DE: 3333 Model calibration (1846)
DE: 3354 Precipitation (1854)
SC: Earth and Space Science Informatics [IN]
MN: Fall Meeting 2005