Please use this identifier to cite or link to this item:
|Title:||Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional waters|
El Serafy, G.
Ensemble Kalman filter
Singapore regional waters
|Citation:||Karri, R.R., Badwe, A., Wang, X., El Serafy, G., Sumihar, J., Babovic, V., Gerritsen, H. (2013-01). Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional waters. Ocean Dynamics 63 (1) : 43-61. ScholarBank@NUS Repository. https://doi.org/10.1007/s10236-012-0584-y|
|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. © 2012 Springer-Verlag Berlin Heidelberg.|
|Source Title:||Ocean Dynamics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 21, 2018
WEB OF SCIENCETM
checked on Jun 6, 2018
checked on Jun 30, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.