Geophysical models make predictions relying on parameter values to be estimated from data. However, existing methods are costly because they require either many runs of the complex geophysical model or to implement an adjoint model. Here, we propose an alternative approach based on optimal control theory which is the simultaneous perturbations stochastic approximation (SPSA). This gradient-descent method is generic and easy to implement, and its computational cost does not increase with the number of parameters to optimize. This study aims at highlighting the potential of SPSA for parameter identification in geophysical models. Through the example of vertical turbulent mixing in the upper ocean, we show with twin experiments that the method could successfully identify parameter values that minimize model-data discrepancy. The efficient and easy-to-get results provided by SPSA in this study should pave the way for a broader use of parameter identification in the complex and embedded models commonly used in geophysical sciences.

中文翻译：

---

在复杂和嵌入式地球物理模型中识别参数的快速通用方法：以海洋中的湍流混合为例

地球物理模型根据要从数据中估计的参数值进行预测。然而，现有方法成本高昂，因为它们需要多次运行复杂的地球物理模型或实施伴随模型。在这里，我们提出了一种基于最优控制理论的替代方法，即同时扰动随机近似（SPSA）。这种梯度下降方法通用且易于实现，其计算成本不会随着要优化的参数数量而增加。本研究旨在突出 SPSA 在地球物理模型中参数识别的潜力。通过上层海洋垂直湍流混合的示例，我们通过双实验表明该方法可以成功识别参数值，从而最大限度地减少模型数据差异。