HR: 0800h
AN: GC41A-0388    [Abstracts]
TI: Validation of Satellite-based Rainfall Estimates for Severe Storms (Hurricanes & Tornados)
AU: * Nourozi, N
EM: nasimn@gmail.com
AF: The City College of New York (CCNY) at the City University of New York (CUNY)- NOAA-CREST, 138th Street & Convent Avenue , New York, NY 10031 United States
AU: Mahani, S
EM: mahani@ce.ccny.cuny.edu
AF: The City College of New York (CCNY) at the City University of New York (CUNY)- NOAA-CREST, 138th Street & Convent Avenue , New York, NY 10031 United States
AU: Khanbilvardi, R
EM: rk@ce.ccny.cuny.edu
AF: The City College of New York (CCNY) at the City University of New York (CUNY)- NOAA-CREST, 138th Street & Convent Avenue , New York, NY 10031 United States
AB: Severe storms such as hurricanes and tornadoes cause devastating damages, almost every year, over a large section of the United States. More accurate forecasting intensity and track of a heavy storm can help to reduce if not to prevent its damages to lives, infrastructure, and economy. Estimating accurate high resolution quantitative precipitation (QPE) from a hurricane, required to improve the forecasting and warning capabilities, is still a challenging problem because of physical characteristics of the hurricane even when it is still over the ocean. Satellite imagery seems to be a valuable source of information for estimating and forecasting heavy precipitation and also flash floods, particularly for over the oceans where the traditional ground-based gauge and radar sources cannot provide any information. To improve the capability of a rainfall retrieval algorithm for estimating QPE of severe storms, its product is evaluated in this study. High (hourly 4km x 4km) resolutions satellite infrared-based rainfall products, from the NESDIS Hydro-Estimator (HE) and also PERSIANN (Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Networks) algorithms, have been tested against NEXRAD stage-IV and rain gauge observations in this project. Three strong hurricanes: Charley (category 4), Jeanne (category 3), and Ivan (category 3) that caused devastating damages over Florida in the summer 2004, have been considered to be investigated. Preliminary results demonstrate that HE tends to underestimate rain rates when NEXRAD shows heavy storm (rain rates greater than 25 mm/hr) and to overestimate when NEXRAD gives low rainfall amounts, but PERSIANN tends to underestimate rain rates, in general.
DE: 1821 Floods
DE: 1854 Precipitation (3354)
DE: 1860 Streamflow
DE: 3354 Precipitation (1854)
DE: 3360 Remote sensing
SC: Global Climate Change [GC]
MN: Fall Meeting 2005