Bayesian optimization for re-analysis and calibration of extreme sea state events simulated with a spectral third-generation wave model

TL;DR

This paper introduces a Bayesian Optimization framework to calibrate and re-analyze extreme sea state events in wave models, improving accuracy and reducing discrepancies with observations.

Contribution

It presents a novel Bayesian Optimization method for joint calibration of wave model parameters, enhancing model accuracy for extreme sea states.

Findings

Calibrated model shows lower bias and RMSE compared to default.

Bayesian Optimization effectively estimates uncertain model parameters.

Method improves model-observation agreement for storm events.

Abstract

Accurate hindcasting of extreme sea state events is essential for coastal engineering, risk assessment, and climate studies. However, the reliability of numerical wave models remains limited by uncertainties in physical parameterizations and model inputs. This study presents a novel calibration framework based on Bayesian Optimization (BO), leveraging the Tree structured Parzen Estimator (TPE) to efficiently estimate uncertain sink term parameters, specifically bottom friction dissipation, depth induced breaking, and wave dissipation from strong opposing currents, in the ANEMOC-3 hindcast wave model. The proposed method enables joint optimization of continuous parameters and discrete model structures, significantly reducing discrepancies between model outputs and observations. Applied to a one month period encompassing multiple intense storm events along the French Atlantic coast, the calibrated model demonstrates improved agreement with buoy measurements, achieving lower bias, RMSE, and scatter index relative to the default sea$-$state solver configuration. The results highlight the potential of BO to automate and enhance wave model calibration, offering a scalable and flexible approach applicable to a wide range of geophysical modeling problems. Future extensions include multi-objective optimization, uncertainty quantification, and integration of additional observational datasets.