Understanding Model-Based Probable Maximum Precipitation Estimation as a Function of Location and Season from Atmospheric Reanalysis

Abstract

Abstract Extreme precipitation events bring huge societal and economic loss around the world every year, and they have undergone spatially heterogeneous changes in the past half-century. They are fundamental to probable maximum precipitation (PMP) estimation in engineering practice, making it important to understand how extreme storm magnitudes are related to key meteorological conditions. However, there is currently a lack of information that can potentially inform the engineering profession on the controlling factors for PMP estimation. In this study, the authors present a statistical analysis of the relationship between extreme 3-day precipitation and atmospheric instability, moisture availability, and large-scale convergence over the continental United States (CONUS). The analysis is conducted using the North America Regional Reanalysis (NARR) and ECMWF ERA-Interim reanalysis data and a high-resolution regional climate simulation. While extreme 3-day precipitation events across the CONUS are mostly related to vertical velocity and moisture availability, those in the southwestern U.S. mountain regions are also controlled by atmospheric instability. Vertical velocity and relative humidity have domainwide impacts, while no significant relationship is found between extreme precipitation and air temperature. Such patterns are stable over different seasons and extreme precipitation events of various durations between 1 and 3 days. These analyses can directly help in configuring the numerical models for PMP estimation at a given location for a given storm.