A Combined Physical-Statistical Approach for Estimating Storm Surge Risk
TCS Building 240
Storm surge is an abnormal rise of seawater caused by a storm. According to the National Hurricane Center, storm surge is often the most damaging part of a hurricane and poses the most severe threat to property and life in a coastal region. Thus, it is crucially important to assess the storm surge risk, typically summarized by r-year surge return level with return period r ranging from 10, 50, 100, or even much longer along a coastline. It is however very difficult to reliably estimate these quantities due to the limited storm surge observations in space and time.
This talk presents an approach to integrate physical and statistical models to estimate extreme storm surge. Specifically, a physically-based hydrodynamics model is used to provide the needed interpolation in space and extrapolation in both time and atmospheric conditions. Statistical modeling is needed to 1) estimate the input distribution for running the computer model, 2) develop a statistical emulator in place of the computer simulator, and 3) quantify estimate uncertainty due to input distribution, statistical emulator, missing/unresolved physics.
Dr. Huang received his Ph.D. in Statistics from Purdue University and has accepted an offer for assistant professor at Clemson University starting in August. He has been intensively involving in the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences by working with Professor Michael Stein (University of Chicago), Professor Elisabeth Moyer (University of Chicago), and Dr. Doug Nychka (NCAR). His research goal is to bridge the gap between statistics and atmospheric/oceanic sciences.