Project 4

State/Parameter Estimation and Uncertainty Quantification for Advanced Predictive Models of Extreme Events in the Coastal Ocean


  • Ibrahim Hoteit
  • Umer Altaf
  • Xiaodong Luo
  • Furrukh Sana
  • Clint Dawson (UT-Austin)
  • Troy Buttler (UT-Austin)
  • Talea Mayo (UT-Austin)


Prediction of coastal flooding due to hurricanes, tropical storms and tsunamis is a problem of international importance. The coastal U.S. and parts of Asia and the Middle East are particularly vulnerable to storm surge and inundation from tsunami waves. An increase in hurricane activity and in the size of hurricanes has led to increasingly devastating and costly coastal inundation. Hurricane Katrina (2005) and Cyclone Nargis (2008), for example, were devastating storms that killed thousands to tens of thousands. Events of this magnitude may increase in frequency and intensity with global climate change and the rise in sea levels.
The vast majority of deaths in these types of events are due to flooding and could be prevented with improved planning, warning systems and emergency response. Accurate forecasts of coastal inundation, provided in real-time to agencies in charge of emergency operations, will result in more timely and orderly evacuations, and help significantly with deployment of first responders and emergency personnel. The focus of this proposal is to address this critically important challenge.
We are working in collaboration between UT Austin and KAUST on the topic of data assimilation for short-duration extreme events, with specific application to the assimilation of real-time data into forecasts for storm surge caused by hurricanes or tropical cyclones. The project is a joint effort between Prof. C. Dawson of ICES, who has expertise in mathematical and numerical modeling, and high performance computing for storm surge applications, and Prof. I. Hoteit of KAUST, who has expertise in state-of-the-art data assimilation methodologies with applications to oceanography and meteorology. We are focusing on developing, implementing and testing Ensemble Kalman Filter, recently developed by Prof. Hoteit and collaborators, into the parallel storm surge forecasting system, ADCIRC (ADVanced CIRCulation model), developed by Prof. Dawson and collaborators. We will use data collected in real-time during recent hurricane events to test the resulting data assimilation system, and we will implement the system on high-performance computing platforms available at both UT Austin and KAUST.


T. Butller, U. Altaf, C. Dawson, I. Hoteit, X. Luo, and T. Mayo: Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting. Monthly Weather Review, under revision, 2011.

X. Luo, and I. Hoteit: Robust ensemble filtering and its relation to covariance inflation in the ensemble Kalman filter. Monthly Weather Review, doi: 10.1175/MWR-D-10-05068.1, 2011.