Why rapid computation?

1. AdcircLite-NC: Rapid evaluation of storm surge and wave forecasts using the Notre Dame Surrogate Forecast Generator (AdcircLite for North Carolina)

2. Why rapid computation? • Urgent forecasting needed before disaster strikes • Forecast simulations are resource intensive, requiring 2.5 - 3 hours computation time on 192 processor systems • Direct simulation of multiple high-resolution ensemble members practically impossible. • Need to Accelerate forecast data throughput • Much more computer hardware (not likely) • Use rapid statistical methods instead

3. Our Approach Implement a response surface method (RSM) that rapidly predicts a response influenced by several variables • Method developed by collaborators at UND • Use pre-existing large data set of storm surge simulations from FEMA coastal Flood Insurance Study for NC • Make available rapid predictions to existing publication mechanisms

4. Leveraging of other Activities • Previous FEMA coastal flood hazard analysis • U Notre Dame’s expertise in optimization methods, and experiences in Hawaii waves • RENCI’s Cyber Infrastructure

5. Why use a Response Surface Method? • Long history in engineering, chemistry… • … more recently in hazards (Resio , Irish, et al.) • “Easy” to use higher-order interpolation • High accuracy compared to zeroth-order methods

6. Why use a Response Surface Method? Hawaii Wave Prediction Example Direct Simulation Surge Estimation Surrogate Model

7. Project Goal and Expected Architecture Components in orange already exist. Components in box on the right are already a part of the North Carolina Forecast System. Components in blue will be established by this project. AdcircLite-NC

8. What does AdcircLite-NC do? Uses 648 simulation dataset: • Water level, wave heights • Hurricane parameters (radius to max winds, forward speed, etc…) Surge response for one hurricane track 648 surge/wave simulations from FEMA FIS

9. What does the RSM look like? = estimated response at x = quadratic basis functions X = [Cp,Rm,B, Vf,θ, X0]; Central pressure deficit (Cp) Radius to maximum winds (Rm) Holland B shape parameter (B) Storm forward speed (Vf) Storm heading (θ) Along-coast distance (X0) = coefficients NB = number of basis functions

10. Progress to date: Project started 1 Jan 2013, Year 5.5 Task 1. Preliminary datasets and testing of the UNDInterpolator • Focused on getting mechanics and code developed • Established code in MATLAB to interact with database • Implemented initial version of UNDInterpolator in MATLAB • Experimenting with interpolator parameters

11. Example: • Select 100 points along NC coast • Use all of dataset to build a storm surge surrogate model • Use Hurricanes Fran (1996) and Isabel (2003) landfall parameters and surrogate model to “predict” historical response Isabel Fran Very Preliminary Results

12. Next steps, near-term • Establish better control and validation sets • Formal optimization of parameters in the UNDInterpolator • Evaluation of different validation criteria • Interpolators for significant wave height estimation.

13. Next steps, long-term Incorporation of AdcircLite-NC predictions into NC-CERA • AdcircLite-NC will produce standard ADCIRC output • Publish output files to existing data server • Same alert mechanism used by NCFS • Spatial grid defined in netCDF output files