Abstract Number:
3206
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Katherine Kreuser (1), Whitney Huang (1)
Institutions:
(1) Clemson University, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Storm surge is the unusual rise in sea level caused by a storm's winds pushing water onshore. Due to high damage to roads, buildings, and lives, accurate estimation of storm surge high quantiles (e.g., r-year return level) and associated uncertainty are essential. Purely data-driven approaches to these tasks pose a challenge, as the number of hurricanes occurring near any single location is limited. A physically-driven approach, utilizing high-fidelity hydrodynamic computer simulations, provides an alternative by leveraging well-known physics to model how the surge level responds to hypothetical storms, where these simulated storms are parameterized by a few key characteristics. This approach requires the following tasks: 1) estimate the joint distribution of storm characteristics; 2) emulate the computer model input-output relationship via surrogate models; and 3) integrate out the input distribution to obtain the surge output distribution, then determine the synthetic data for high quantile (e.g. 1-in-100 year return level) estimation. A case study of this approach in South West Florida using the computer model ADCIRC will be presented to illustrate the proposed workflow.
Keywords:
Gaussian Processes|Computer Model|Quantile Estimation|Hurricanes and Storm Surges| |
Sponsors:
Section on Statistics and the Environment
Tracks:
Climate and Meteorology
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