Improving Real-Time Precipitation Estimation over the Mountainous Regions of the Western United States, using Multi-Sources Remotely Sensed and Lightning Data
Ali S. Amirrezvani, NOAA/CRSSTC, New York, NY; and D. S. Mahani and D. R. Khanbilvardi
Improving satellite-based rainfall retrieval algorithms for estimating high resolution (up to hourly 4km x 4km) precipitation, particularly over mountainous regions, where ground-based sources (e.g. radar and gauge networks) cannot cover, is the objective of this study. High-resolution cloud-top infrared brightness temperature (IR-Tb) from geostationary satellite (GOES) and cloud-to-ground lightning (CGL) are used for precipitation estimation. Rainy cloud with colder top temperature (Tb) and stronger lightning generally produces heavier rainfall. CGL-rainfall studies have demonstrated that lightning (L) can estimate rainfall more accurately than remote sensing-based estimates, particularly for thunderstorm events, because CGL-R relationship is highly correlated (cc= 0.72). The occurrence of CG lightning varies with topography and seasonality. An artificial neural networks algorithm has been applied for estimating rainfall from cloud-top IR in conjunction with lightning. Preliminary results demonstrate that using lightning in addition to cloud Tb could increase the accuracy of rainfall estimates up to 0.23. The results are for a short time storm in the July 2002 over an area with latitude 32-38oN and longitude 104-112oW in a mountainous region.
Poster Session 1, Advances in Technology and Operational Utility of Lightning Data
Monday, 30 January 2006, 2:30 PM-4:00 PM, Exhibit Hall A2
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