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.

翻译：极端海况事件的精确后报对于海岸工程、风险评估及气候研究至关重要。然而，数值海浪模型的可靠性仍受限于物理参数化方案与模型输入的不确定性。本研究提出一种基于贝叶斯优化（BO）的新型校准框架，利用树结构Parzen估计器（TPE）高效估计ANEMOC-3后报海浪模型中不确定的汇项参数，具体包括底摩擦耗散、水深诱导破碎以及强逆流引起的波浪耗散。该方法实现了连续参数与离散模型结构的联合优化，显著降低了模型输出与观测数据间的差异。通过对法国大西洋沿岸包含多次强风暴事件的一个月时段进行应用，校准后的模型与浮标观测数据吻合度显著提升，相较于默认的海况求解器配置，其偏差、均方根误差和散射指数均有所降低。研究结果凸显了贝叶斯优化在自动化与增强海浪模型校准方面的潜力，为广泛的地球物理建模问题提供了可扩展且灵活的解决方案。未来拓展方向包括多目标优化、不确定性量化以及多源观测数据集的融合。