Inland freshwater flooding induced by heavy rainfall has been one of the most deadly and costly disasters produced by landfalling hurricanes or tropical cyclones (TCs). In contrast to the great progress in understanding and predicting TC tracks and moderate progress in TC intensity changes, quantitative rainfall forecast of TCs is still a challenge issue, and little progress has been made. This project seeks to advance our understanding of TC rainfall distribution by focusing on interactions between landfalling TCs and the atmospheric environment and to improve quantitative rainfall forecast of TCs during TC landfall. The principal investigator will foster involvement of a graduate student with particularly consideration of women and underrepresented minority students in carrying out this research project. The most challenging aspect of predicting rainfall in landfalling TCs is not only the intensity and the extent but also the spatial distribution of precipitation especially the heaviest rainfall area. The distribution and asymmetries of TC rainfall are important features inherently linked to TC-induced inland flooding and TC size changes. The overall goal of the work is to understand distribution, evolution, and asymmetries of rainfall during landfalling processes of TCs through statistical analyses of long-term satellite observations. This project will examine physical processes that influence changes of rainfall patterns for landfalling TCs in before, during, and after landfall stages, and for landfalling versus over-ocean TCs. Factors to be considered in these processes include diurnal variation, extratropical transition, and other environmental conditions such as vertical wind shear, total available moisture, and sea surface temperatures. The research will fill a critical gap in our scientific understanding of TC rainfall evolution by focusing on physical processes of precipitation changes during landfalling TCs and environmental impacts on landfalling TCs. A better understanding of these TC features is essential for improving landfalling TC forecasts.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.
