HR: 11:05h
AN: H32A-04    [Abstracts]
TI: Delineation of Aerial Extent of Precipitation Using Multi-Spectral Remotely Sensed Data
AU: * Behrangi, A
EM: abehrang@uci.edu
AF: Department of Civil and Environmental Engineering, the Henry Samueli School of Engineering, University of California, Irvine., E 4130 Engineering Gateway, Irvine, CA 92697, United States
AU: Hsu, K
EM: kuolinh@uci.edu
AF: Department of Civil and Environmental Engineering, the Henry Samueli School of Engineering, University of California, Irvine., E 4130 Engineering Gateway, Irvine, CA 92697, United States
AU: Sorooshian, S
EM: soroosh@uci.edu
AF: Department of Civil and Environmental Engineering, the Henry Samueli School of Engineering, University of California, Irvine., E 4130 Engineering Gateway, Irvine, CA 92697, United States
AU: Kuligowski, B
EM: Bob.Kuligowski@noaa.gov
AF: NOAA/NESDIS/Center for Satellite Applications and Research (STAR), E/RA2 RM 712 WWBG 5200 Auth Rd., Camp Springs, MD 20746-4304, United States
AB: This study analyzes the importance of using multi-spectral data for precipitation area detection. Five image channels from GOES-12, including channel 1 (visible channel, 0.65μm), channel 2 (3.9μm), channel 3 (water vapor channel, 6.5μm), channel 4 (thermal channel, 10.7μm), and channel 6 (13.3μm), were evaluated. In part of the precipitation classification procedure, the self organizing feature map (SOFM) was used to classify multi-dimensional images into a number of clusters. The probability of precipitation (POP) for each cluster is then calculated based on NEXRAD precipitation observations. Experiments were set to the summer time period (Jun-Aug of 2006) over the continental United States. Different scenarios, with a combination of various image channels, were tested to find the best combination of channels for day-time and night-time precipitation detection. Using the pattern-matching technique of Lovejoy and Austin (1979) an optimum POP threshold (for 1D) or boundary (for 2D) is defined for each scenario such that satellite classes having a higher POP are treated as precipitation and those with lower POP as no-precipitation. Comparison of the scenarios is accomplished by using the equitable threat score (ETS) obtained from the contingency table. Some overall results are: 1) Including visible channel in the day-time results in a much better score than other channel combination without visible channel. 2) For night-time precipitation detection, a significant improvement in score is achieved using the combined channels (2-6) instead of one single channel alone. In the presentation, detailed ETS statistics will be provided. In addition, extension of the precipitation detection from five GOES channels to future GOES-R channels will be discussed.
DE: 1800 HYDROLOGY
DE: 1840 Hydrometeorology
DE: 1854 Precipitation (3354)
DE: 1855 Remote sensing (1640)
SC: Hydrology [H]
MN: 2007 Fall Meeting