Abstract
Hydrodynamic models are commonly used for predicting water levels and currents in the deep ocean, ocean margins and shelf seas. Their accuracy is typically limited by factors, such as the complexity of the coastal geometry and bathymetry, plus the uncertainty in the flow forcing (deep ocean tide, winds and pressure). In Southeast Asian waters with its strongly hydrodynamic characteristics, the lack of detailed marine observations (bathymetry and tides) for model validation is an additional factor limiting flow representation. This paper deals with the application of ensemble Kalman filter (EnKF)-based data assimilation with the purpose of improving the deterministic model forecast. The efficacy of the EnKF is analysed via a twin experiment conducted with the 2D barotropic Singapore regional model. The results show that the applied data assimilation can improve the forecasts significantly in this complex flow regime.
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Acknowledgements
The authors gratefully acknowledge the support and contributions of the Singapore-Delft Water Alliance (SDWA) and Deltares’ strategic research funding. The research presented in this work was carried out as part of SDWA’s ‘Must-Have Box’ research program (R-303-001-003-272). The authors also thank the Maritime and Port Authority of Singapore (MPA) and University of Hawaii Sea Level Center (UHSLC) for providing the maritime data for analysis. The authors wish to thank Martin Verlaan, Erwin Loots, Arjen Markus and Stef Hummel for the support in using the OpenDA software. The authors also wish to thank Alamsyah Kurniawan, Seng Keat Ooi, Piyamarn Sisomphon and Serene Tay for providing the necessary help in the hydrodynamic modelling.
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Karri, R.R., Badwe, A., Wang, X. et al. Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional waters. Ocean Dynamics 63, 43–61 (2013). https://doi.org/10.1007/s10236-012-0584-y
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DOI: https://doi.org/10.1007/s10236-012-0584-y