Print Email Facebook Twitter Data-assimilation-based parameter estimation of bathymetry and bottom friction coefficient to improve coastal accuracy in a global tide model Title Data-assimilation-based parameter estimation of bathymetry and bottom friction coefficient to improve coastal accuracy in a global tide model Author Wang, X. (TU Delft Mathematical Physics) Verlaan, M. (TU Delft Mathematical Physics; Deltares) Veenstra, Jelmer (Deltares) Lin, H.X. (TU Delft Mathematical Physics) Date 2022 Abstract Global tide and surge models play a major role in forecasting coastal flooding due to extreme events or climate change. The model performance is strongly affected by parameters such as bathymetry and bottom friction. In this study, we propose a method that estimates bathymetry globally and the bottom friction coefficient in shallow waters for a global tide and surge model (GTSMv4.1). However, the estimation effect is limited by the scarcity of available tide gauges. We propose complementing sparse tide gauges with tide time series generated using FES2014. The FES2014 dataset outperforms the GTSM in most areas and is used as observations for the deep ocean and some coastal areas, such as Hudson Bay and Labrador, where tide gauges are scarce but energy dissipation is large. The experiment is performed with a computation- and memory-efficient iterative parameter estimation scheme (time–POD-based coarse incremental parameter estimation; POD: proper orthogonal decomposition) applied to the Global Tide and Surge Model (GTSMv4.1). Estimation results show that model performance is significantly improved for the deep ocean and shallow waters, especially in the European shelf, directly using the CMEMS tide gauge data in the estimation. The GTSM is also validated by comparing to tide gauges from UHSLC, CMEMS, and some Arctic stations in the year 2014. To reference this document use: http://resolver.tudelft.nl/uuid:afe64aa0-75b6-450f-88d3-916a13dd1dfa DOI https://doi.org/10.5194/os-18-881-2022 ISSN 1812-0784 Source Ocean Science, 18 (3), 881-904 Part of collection Institutional Repository Document type journal article Rights © 2022 X. Wang, M. Verlaan, Jelmer Veenstra, H.X. Lin Files PDF os_18_881_2022.pdf 18.09 MB Close viewer /islandora/object/uuid:afe64aa0-75b6-450f-88d3-916a13dd1dfa/datastream/OBJ/view