Multifidelity Computer Model Emulation with High-Dimensional Output: An Application to Storm Surge

TL;DR

This paper develops a parallel autoregressive cokriging emulator to efficiently predict high-fidelity storm surges across large spatial domains, combining multiple models of varying fidelity levels.

Contribution

It introduces a novel multilevel emulator that efficiently combines high- and low-fidelity models for accurate storm surge prediction.

Findings

The emulator achieves high accuracy comparable to high-fidelity models.

It significantly reduces computational costs for large-scale surge prediction.

The method effectively handles high-dimensional output data.

Abstract

Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm surges. Such computer models can be implemented with a wide range of fidelity levels, with computational burdens varying by several orders of magnitude due to the nature of the system. The threat posed by surge makes greater fidelity highly desirable, however such models and their high-volume output tend to come at great computational cost, which can make detailed study of coastal flood hazards prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different fidelity levels important. We propose a parallel partial autoregressive cokriging model to predict highly-accurate storm surges in a computationally efficient way over a large spatial domain. This emulator has the capability of predicting storm surges as accurately as a high-fidelity computer model given any storm characteristics over a large spatial domain.