A Hierarchical Spatio-Temporal Dynamical Model for Predicting Precipitation Occurrence and Accumulation
Ali Arab, Tolessa Deksissa
Mathematics and Statistics, Georgetown University, Washington, DC, United States; University of the District of Columbia, Washington, DC, United States

The problem of predicting occurrence and accumulation of precipitation is of considerable interest in many disciplines such as atmospheric sciences, agriculture, and hydrology, among others. The predictions based on climate models are often in a coarse resolution that are unable to provide accurate predictions for specific locations. Alternatively, statistical modeling of precipitation data can provide more reliable predictions at higher resolutions. There are several statistical models suggested in the literature, but most of these models ignore the spatial and/or temporal dependence of precipitation fields which results in lack of prediction accuracy. In this paper, we develop a statistical method that yields predictive distributions for precipitation occurrence and accumulation while accounting for spatial and temporal correlation in the precipitation fields. The predictive distributions for precipitation accumulation can then be used to obtain exceedance probability of rainfall accumulation beyond a threshold in order to issue flash flood warnings, and optimize evacuation management in case of flooding events. The proposed modeling approach is based on a hierarchical modeling framework that allows breaking down a complex problem into simpler components that are linked together probabilistically. We implement our proposed approach using historic precipitation data in the Washington D.C. area. Another advantage of the proposed model is that it allows for estimating historic records (while accounting for uncertainty in the estimates) for a weather station which was established in the 1960s using data from weather stations with longer historic records of precipitation records from other stations in the area that go back to the late 19th century.

Keywords: Precipitation prediction models; Spatio-temporal models; Bayesian statistics; Trend analysis

Biography: Ali Arab is an Assistant Professor of Statistics at the Department of Mathematics and Statistics of Georgetown University. His research interests are focused on hierarchical models for spatio-temporal processed, spatial statistics, Bayesian inference, and risk and reliability analysis. He earned his Ph.D. from the Department of Statistics of the University of Missouri-Columbia in 2007 under the guidance of Porfessor Chris Wikle.