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Computation‐Efficient Parameter Estimation for a High‐Resolution Global Tide and Surge Model
Author(s) -
Wang Xiaohui,
Verlaan Martin,
Apecechea Maialen Irazoqui,
Lin Hai Xiang
Publication year - 2021
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1029/2020jc016917
Subject(s) - computation , calibration , algorithm , computer science , bathymetry , grid , tidal model , estimation theory , least squares function approximation , sensitivity (control systems) , representation (politics) , dimension (graph theory) , mathematical optimization , mathematics , geodesy , statistics , geology , engineering , oceanography , estimator , electronic engineering , pure mathematics , politics , law , political science
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 (10 6 ) to O (10 2 ) 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.