Paper

Weather fronts play an important role in atmospheric science. Their correlation to severe natural hazards such as extreme precipitation, cyclones or thunderstorms makes localization and understanding of frontal systems an important factor in weather forecasting. Despitetheir importance weather fronts are mostly studied on horizontal slices, ignoring their three-dimensional characteristics. In this paper we present an efficient GPU-based parallelization for the detection of three-dimensional weather fronts. We achieve comparable skill to our previous CPU-based method, on which we based our algorithm, while being more than two orders-of-magnitude faster. Furthermore, we extend our previous method by providing additional information for warm, cold, occluded, and stationary fronts. Thus, our approach drastically increases the ability to provide statistical evaluations of three-dimensional fronts for different setups. Even faster runtimes can be achieved by using multiple GPUs with linear scaling