HR: 0800h
AN: GC41A-0383 [Abstracts]
TI: Improving Precipitation Estimation Using Multi-Sources Remote Sensing and Lightning Data, Particularly
for Thunderstorms Over Mountainous Regions
AU: * Amirrezvani, A S
EM: aamirrezvani@gc.cuny.edu
AF: The City University of New York (CUNY), Convent Avenue at 138th Street, New York, NY 10031
United States
AU: Mahani, S E
EM: mahani@ce.ccny.cuny.edu
AF: The City University of New York (CUNY), Convent Avenue at 138th Street, New York, NY 10031
United States
AU: Khanbilvardi, R M
EM: rk@ce.ccny.cuny.edu
AF: The City University of New York (CUNY), Convent Avenue at 138th Street, New York, NY 10031
United States
AB:
Improving satellite-based rainfall retrieval algorithms to estimate high resolution (up to hourly 4km x 4km) precipitation,
particularly for thunderstorm events over mountainous regions, is the objective of this study. Using remotely sensed cloud
information for estimating accurate high resolution rainfall over mountains and remote areas, where ground-based sources
(e.g. radar and gauge networks) cannot cover, is still a challenge. In this study, high-resolution cloud-top infrared
brightness temperature (Tb) from geostationary satellite (GOES) in conjunction with cloud-to-ground lightning (CGL) is
used for precipitation estimation. A rainy cloud with colder top temperature and stronger lightning generally produces
heavier rainfall. CGL-rainfall (CGL-R) studies have demonstrated that rainfall is more correlated to lightning (cc = 0.72)
than to satellite-based cloud information, particularly for thunderstorm events. The occurrence of CG lightning varies with
topography and seasonality. An artificial neural networks algorithm has been applied for estimating rainfall from the
combination of cloud-top IR and lightning. Preliminary results demonstrate that using lightning in addition to cloud Tb
could increase the accuracy of rainfall estimates up to 0.23. The preliminary results are for a short time storm in the
July 2002 over an area with latitude 32°-38°N and longitude 104°-112°W in a mountainous region.
DE: 1816 Estimation and forecasting
DE: 3324 Lightning
DE: 3354 Precipitation (1854)
DE: 3360 Remote sensing
SC: Global Climate Change [GC]
MN: Fall Meeting 2005