用神经网络优化海洋模型参数，显著降低误差并提升模拟精度。
Calibration of a neural network ocean closure for improved mean state and variability
- 将参数调优转化为基于集合卡尔曼反演的系统化校准问题。
- 校准后平均界面误差降低1.7至3.3倍，变率误差也大幅减少。
- 新方法抗混沌噪声干扰，且无需长时间积分即可快速校准。
全球海洋模型在粗分辨率下常存在均值状态与变率偏差，尤其因中尺度涡旋未被解析所致。传统参数化系数多采用经验调参。本文将参数调优建模为校准问题，利用集合卡尔曼反演（Ensemble Kalman Inversion, EKI）优化两个理想化海洋模型中神经网络对中尺度涡旋的参数化。校准后的参数化使时间平均流体界面及其变率的误差降低1.7–3.3倍，具体取决于指标与配置。该方法对由混沌海洋动力学引起的统计噪声具有鲁棒性。此外，我们提出一种高效校准协议，通过精心设计初始条件避免达到统计平衡所需的长时间积分。结果表明，系统性校准可显著改善粗分辨率海洋模拟，为降低全球海洋模型偏差提供了可行路径。
原文摘要 · Abstract (English)
Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameterization coefficients are typically tuned ad hoc. Here, we formulate parameter tuning as a calibration problem using Ensemble Kalman Inversion (EKI). We optimize parameters of a neural network parameterization of mesoscale eddies in two idealized ocean models at coarse resolution. The calibrated parameterization reduces errors by factors of 1.7-3.3 in the time-averaged fluid interfaces and their variability compared to the unparameterized model, depending on the metric and configuration. The EKI method is robust to noise in time-averaged statistics arising from chaotic ocean dynamics. Furthermore, we propose an efficient calibration protocol that bypasses integration to statistical equilibrium by carefully choosing an initial condition. These results demonstrate that systematic calibration can substantially improve coarse-resolution ocean simulations and provide a practical pathway for reducing biases in global ocean models.
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