Numerical simulation of a heavy rainfall event during the PRE‐STORM Experiment

Abstract

Numerical simulation of a storm that occurred along the Kansas‐Oklahoma border June 9–10, 1985, during the Preliminary Regional Experiment for STORM‐Central (PRE‐STORM) was carried out to examine the effect of atmospheric moisture on extreme rainstorms. This storm developed in a weak synoptic forcing environment and produced more than 200 mm of rainfall in a few hours. Heavy rainfall was confined to a small area and storm structure has been described as that of a chaotic convective system. Rainfall events like the June 9–10 storm which produce large rainfall amounts in small areas over short time periods and which have weak synoptic scale forcing are a particular challenge for numerical modeling. These events are also of particular importance for problems of engineering hydrometeorology, which motivates the analyses presented in this paper. The model used in this study is the nonhydrostatic version of the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model (MM5). Three nested domains were used for modeling the June 9–10 storm. The grid spacing is 90 km for the outer, coarse grid, which covers most of the continental United States; 30 km for the intermediate grid (covering the central and southern plains); and 10 km for the local grid covering the PRE‐STORM region. Using operationaland PRE‐STORM surface and upper air observations, the model accurately reproduces storm total rainfall and storm location. To examine the role of atmospheric water vapor for heavy rainfall events, a series of model runs are carried out in which initial moisture is varied from 75% to 125% of the observed value. Storm total rainfall, storm structure, and storm timing, as well as all elements of the atmospheric water budget, exhibit a striking sensitivity to changes in atmospheric water vapor. These results have significant implications for engineering hydrometeorology procedures, especially procedures used for probable maximum precipitation (PMP) analyses.