Parameter estimation for a global tide and surge model with a memory-efficient order reduction approach. (May 2022)

• Record Type: Journal Article
• Authors: Wang, Xiaohui; Verlaan, Martin; Apecechea, Maialen Irazoqui; Lin, Hai Xiang
• Is Part Of: Ocean modelling. Volume 173(2022)
• Publication Date: 2022-05
• Subjects: Global tide and surge model -- Parameter estimation -- Model order reduction; Oceanography -- Periodicals; Océanographie -- Périodiques; Oceanography; Periodicals; 551.46
• DOI: 10.1016/j.ocemod.2022.102011
• Languages: English
• ISSNs: 1463-5003

Abstract

Abstract: Accurate parameter estimation for the Global Tide and Surge Model (GTSM) benefits from observations with long time-series. However, increasing the number of measurements leads to a large computation demand and increased memory requirements, especially for the ensemble-based methods that assimilate the measurements at one batch. In this study, a memory-efficient parameter estimation scheme using model order reduction in time patterns is developed for a high-resolution global tide model. We propose using projection onto empirical time-patterns to reduce the model output time-series to a much smaller linear subspace. Then, to further improve the estimation accuracy, we introduce an outer-loop, similar to Incremental 4D-VAR, to evaluate model-increments at a lower resolution and subsequently reduce the computational cost. The inner-loop optimizes parameters using the lower-resolution model and an iterative least-squares estimation algorithm called DUD. The outer-loop updates the initial output from the high-resolution model with updated parameters from the converged inner-loop and then restarts the inner-loop. We performed experiments to adjust the bathymetry with observations from the FES2014 dataset. Results show that the time patterns of the tide series can be successfully projected to a lower dimensional subspace, and memory requirements are reduced by a factor of 22 for our experiments. The estimation is converged after three outer iterations in our experiment, and tide representation is significantly improved, achieving a 34.5% reduction of error. The model's improvement is not only shown for the calibration dataset, but also for several validation datasets consisting of one year of time-series from FES2014 and UHSLC tide gauges. Highlights: Model order reduction significantly reduces the parameter estimation memory usage. Estimation accuracy loss in estimation by the application of model order reduction is negligible. Lengthening estimation time-span from 14 to 31 days improves the model performance. Introducing outer iterations can improve the estimation effect for non-linear models. Estimation of the bathymetry results in a 34.5% reduction in errors for GTSM.