Abstract
Probabilistic hurricane storm surge forecasting using a high-fidelity model has been considered impractical due to the overwhelming computational expense to run thousands of simulations. This article demonstrates that modern statistical tools enable good forecasting performance using a small number of carefully chosen simulations. This article offers algorithms that quickly handle the massive output of a surge model while addressing the missing data at unsubmerged locations. Also included is a new optimal design criterion for selecting simulations that accounts for the log transform required to statistically model surge data. Hurricane Michael (2018) is used as a testbed for this investigation and provides evidence for the approach’s efficacy in comparison to the existing probabilistic surge forecast method.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 460-480 |
| Number of pages | 21 |
| Journal | Annals of Applied Statistics |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2021 |
Funding
Acknowledgments. This material was based upon work partially supported by the National Science Foundation under Grant Award Number DMS-1638521 (to the Statistical and Applied Mathematical Sciences Institute), Grant Award Number DMS-1953111, Grant Award Number ACI-1339723 and by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-ND0001-01.
Keywords
- Computer experiments
- Gaussian process
- Sequential experiments
- Surrogate model-ing
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty