Uncertainty of a Weather Prediction Model in Predicting Extreme Rainfall Events: Application to Hurricane Rita

Hurricanes are one of the most severe and threatening weather events that cause major damages to west and southwestern United States each year. Hurricanes result in extreme precipitation and flooding events, which pose a significant concern to the population and exert a major negative effect on the economic growth. Regional weather models are used to determine the potential of flooding based on model predicted rainfall. In particular, the Weather Research and Forecasting (WRF) model is a next-generation mesoscale modeling system designed to improve the weather forecasts. Choosing the right model configuration can help reducing the model uncertainty due to the complicated physic equations, which govern how the state of the system changes with time. The aim of this study is to investigate the model sensitivity to different microphysics configurations for extreme weather events. Furthermore, the uncertainty of hurricane path under different microphysics options is investigated. Hurricane Rita (2005), which resulted in severe damages to the Gulf Coast region, is selected as the case study. A fine resolution of 1 km grid size is used to quantify the impacts of the variation of different microphysics schemes, and their interaction on the model results, specifically precipitation. The predicted precipitation is compared with independent ground-reference measurements. The results revealed that the Lin et. al microphysics scheme predicted the amount of precipitation more accurately.