Collaborative Research: What Drives the Most Extreme Rainstorms in the Contiguous United States (US)?

University Of Oklahoma Norman Campus, Norman OK

Investigators

Abstract

Extreme precipitation, and the flash flooding it often causes, is responsible for numerous fatalities and significant damage every year. Accurate forecasts of extreme rainfall remain elusive, and this stems in part from incomplete understanding of what atmospheric factors distinguish the most extreme precipitation events from those that produce heavy, but not as extreme, rainfall. This project will use observations, machine learning, and numerical model simulations to identify key differences in environmental conditions that support these events. The project results should help weather forecasters better identify the specific conditions that lead to extreme rainfall, thereby improving the advanced warning for flash flooding events. The project additionally has a significant education and outreach component with the goal of increased public scientific literacy and engagement. Extreme precipitation, defined here as rainfall events with an average recurrence interval (ARI) of 1000 years, causes significant societal impacts in almost all cases in the United States. This project will focus on improving understanding of the most extreme rainfall events in the continental United States, and what distinguishes these events from heavy, but less extreme precipitation. The project has three main objectives. The research team will initially develop a database of the most extreme rainfall events from multiple precipitation datasets, focusing on the exceedance of the 10-, 100-, and 1000-year ARIs for 6-, 24-, 48-, and 72-hour accumulation periods. For the events identified in Objective 1, the researchers will collect data about the environmental conditions from atmospheric reanalysis datasets and use Self Organizing Maps, a machine learning technique, to identify which atmospheric parameters are associated with more and less extreme rainfall events. In the final step of the project, idealized numerical modeling will be conducted using Cloud Model 1 (CM1) to test the roles of atmospheric moisture, mesoscale ascent, vertical wind shear, and other components identified by the earlier analyses to determine the impacts on mesoscale convective systems, the large thunderstorm complexes that are frequent in the central US. This project is jointly funded by the Physical and Dynamic Meteorology program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.