In this study, a computation‐efficient parameter estimation scheme for high‐resolution global tide models is developed. The method is applied to Global Tide and Surge Model with an unstructured grid with a resolution of about 2.5 km in the coastal area and about 4.9 million cells. The estimation algorithm uses an iterative least squares method, known as DUD. We use time‐series derived from the FES2014 tidal database in deep water as observations to estimate corrections to the bathymetry. Although the model and estimation algorithm run in parallel, directly applying of DUD would not be affordable computationally. To reduce the computational demand, a coarse‐to‐fine strategy is proposed by using output from a coarser model to replace the fine model. There are two approaches; One is completely replacing the fine model with a coarser model during calibration (Coarse Calibration) and the second is Coarse Incremental Calibration, that replaces the output increments between the initial model and model with modified parameters by coarser grid model simulations. To further reduce the computation time, the parameter dimension is reduced from O(10⁶) to O(10²) based on sensitivity analysis, which greatly reduces the required number of model simulations and storage. In combination, these methods form an efficient optimization strategy. Experiments show that the accuracy of the tidal representation can be improved significantly at affordable cost. Validation for other time‐periods and using coastal tide‐gauges shows that the accuracy is improved significantly. However, the calibration period of two weeks is short and leads to some over‐fitting of the model.

中文翻译：

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高分辨率全球潮汐和喘振模型的计算有效参数估计

在这项研究中，为高分辨率全球潮汐模型开发了一种计算有效的参数估计方案。将该方法应用于具有非结构化网格的全球潮汐和潮汐模型，该网格在沿海地区的分辨率约为2.5 km，具有约490万个像元。估计算法使用称为DUD的迭代最小二乘法。我们使用FES2014潮汐数据库在深水中得出的时间序列作为观测值，以估算测深仪的校正量。尽管模型和估计算法并行运行，但直接应用DUD在计算上将无法承受。为了减少计算需求，提出了一种从粗到精的策略，即使用粗模型的输出来代替精模型。有两种方法：一种是在校准（粗略校准）期间用较粗略的模型完全替代精细模型，第二种是粗略增量校准，其通过粗略的网格模型模拟来替换初始模型和模型之间的输出增量，并使用修改后的参数。为了进一步减少计算时间，将参数维从基于灵敏度分析的O（10 ⁶）到O（10 ²），这大大减少了模型仿真和存储所需的次数。结合起来，这些方法形成了有效的优化策略。实验表明，以可承受的成本可以显着提高潮汐表示的准确性。对其他时间周期和使用沿海潮汐计进行的验证表明，准确性得到了显着提高。但是，两周的校准期很短，导致模型有些过拟合。