Improved Storm Data Processing through Parallel Computing Approaches

A previous research study conducted at Michigan Technological University by Dr. Deborah Nykanen and her colleague Dr. Daniel Harris analyzed storm data in order to develop algorithms that will allow coarse resolution rainfall forecasted by weather models to be optimally used in high resolution hydrology models with the goal of improving stream flow predictions and early detection algorithms that can be used to warn communities about potential flash floods. This research was performed by analyzing a series of independent radar images derived from Weather Surveillance Radar-1988 Doppler (WSR-88D) data obtained from Dr. James A. Smith at Princeton University using a series of computer programs written by the original researcher and her colleagues. The program was run using a sequential algorithm that can take up to 17 hours to execute. Because of the structure of the problem, there was an opportunity for applying concurrent computing techniques to the program code. In order to speed up the program execution time, several different concurrent computing approaches have been applied to the code. Speedup analysis has been conducted for each different concurrent approach improving the code execution time by up to a factor of 93. The analysis results show how different concurrent approaches affect the speedup of code. The faster code will aid in analyzing future storm data, allowing more data to be analyzed in a shorter amount of time, and will eventually be used in improving lead time on high resolution stream flow predictions and flash flood warnings. The speedup provided by the concurrent computing approaches has been verified on previously analyzed data