P5.47
Tracking Continuous Rain Systems Using A Genetic Based Wavelet Image Registration Technique
Jearanai Vongsaard, George Mason University, Fairfax, VA; and L. S. Chiu, T. El-Ghazawi, J. Weinman, and R. Yang
Abstract Continuous space/time rainfall estimates are crucial for many hydrological applications. Conventional rain gauge networks or meteorological radars provide continuous coverage in time but have poor spatial coverage. Satellite observations provide snap shots of precipitation fields at poor temporal resolution. While a number of space-borne platforms have been deployed for rain observation, the merging of the various sensor data from different platforms to produce a continuous rainfall data set remains a major challenge. The Tropical Rainfall Measuring Mission (TRMM) satellite was launched with the first space-borne Precipitation Radar (PR) to collect accurate precipitation measurements. To validate the space data set, well-instrumented and calibrated ground validation (GV) sites are established. The paper is a first attempt to merging rainfall estimates from space-borne and ground based radars. In this paper, we develop a method of tracking rain rates that come from different sources. We describe a technique to forecast a bogus PR data that occurred before and after the passage of the PR over a GV site. Several simulations are performed at 10 minutes intervals of GV rain rates data at Melbourne, Florida on March 9, 1998 from 8:00 am to 9:00 am in order to track continuously PR rain rates which overpass the GV site at 8:30 am. We apply Genetic Algorithms Based Wavelet Image Registration Technique to find the needed transformations in order to produce a time series of PR rain rate data. The transformations include x-axis translation, y-axis translation, and rotation. Our registration technique will reduce the size of the search data by initially searching at lowest resolution by using Wavelet transformation and also using Genetic Algorithm in order to improve the speed up by reducing the search space. By registering the PR original data following the transformed results, the new PR data are presented 20 minutes before and after the overpass. Finally, the statistic analyses are used to compare the relationship between these different sources of rain rates. The increased correlation between GV and bogus PR reflectivity data demonstrated the potential use this technique in merging multi-platform remote sensing data.
Poster Session 5, New Technology and Methods (Continued)
Thursday, 18 October 2001, 9:15 AM-11:00 AM
Previous paper Next paper