NDLI: Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional waters

Content Provider	Springer Nature Link
Author	Karri, Rama Rao Badwe, Abhijit Wang, Xuan El Serafy, Ghada Sumihar, Julius Babovic, Vladan Gerritsen, Herman
Copyright Year	2012
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.
Starting Page	43
Ending Page	61
Page Count	19
File Format	PDF
ISSN	16167341
Journal	Deutsche Hydrografische Zeitschrift
Volume Number	63
Issue Number	1
e-ISSN	16167228
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-12-21
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data assimilation Ensemble Kalman filter Sea-level anomaly Non-tidal currents Singapore regional waters Malacca Strait Oceanography Geophysics/Geodesy Meteorology/Climatology Fluid- and Aerodynamics Environmental Monitoring/Analysis
Content Type	Text
Resource Type	Article
Subject	Oceanography

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