The continuous tracking of reflectivity data from multi-platform observations using genetic algorithm

Abstract

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. We describe a technique to forecast bogus PR data that occurred before and after the passage of the PR over a GV site. Several simulations are performed at 10-minute intervals of GV reflectivity data at Melbourne, Florida on March 9, 1998 from 8:00 am to 9:00 am to track continuously PR reflectivity which overpass the GV site at 8:30 am. We apply genetic algorithms (GAs) to find the needed transformations to produce a time series of PR reflectivity data. The transformations include translation and rotation. 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 reflectivity. The increased correlation between GV and bogus PR reflectivity data demonstrated the potential use of GAs in merging multi-platform remote sensing data.