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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Babovic V (2007) Data-model integration—an approach to assimilation of sea level anomaly data into an oceanographic model. In: Navarro PG, Playan E (eds) Num modelling of hydrodynamics for water resources. Zaragoza, Spain, pp 67–75
Babovic V, Karri RR, Wang X, Ooi SK, Badwe A (2011) Efficient data assimilation for accurate forecasting of sea-level anomalies and residual currents using the Singapore regional model. Geophys Res Abstr 13. doi:EGU2011-8492
Babovic V, Sannasiraj SA, Chan ES (2005) Error correction of a predictive ocean wave model using local model approximation. J Mar Syst 53(1–4):1–17
Calkoen C, Wensink H, Twigt D, Sisomphon P, Mynett A (2009) Sea level anomalies from satellite altimetry - retrieval and validation. In: Proc of the 8th Int conf on Hydroinform, Chile, p 8. doi:188a164
Carme S, Dinh-Tuan P, Verron J (2001) Improving the singular evolutive extended Kalman filter for strongly nonlinear models for use in ocean data assimilation. Inverse Prob 17(5):1535–1559
Chen C, Malanotte-Rizzoli P, Wei J, Beardsley RC, Lai Z, Xue P, Lyu S, Xu Q, Qi J, Cowles GW (2009) Application and comparison of Kalman filters for coastal ocean problems: an experiment with FVCOM. J Geophys Res 114(C5):C05011
Chui CK, Chen G (2009) Kalman filtering: with real-time applications, 4th edn. Springer, New York
El Serafy G, Gerritsen H, Hummel S, Weerts A, Mynett A, Tanaka M (2007) Application of data assimilation in portable operational forecasting systems—the DATools assimilation environment. Ocean Dyn 57(4):485–499
El Serafy G, Mynett AE (2008) Improving the operational forecasting system of the stratified flow in Osaka Bay using an ensemble Kalman filter based steady state Kalman filter. Water Resour Res 44(6):1–19, W06416
El Serafy G, Verlaan M, Hummel S, Weerts A, Dhondia J (2010) OpenDA open source generic data assimilation environment and its application in process models. Geophys Res Abstr 12. doi:EGU2010-9346-2
Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99(C5):10143–10162
Evensen G (2007) Data assimilation—the ensemble Kalman filter. Springer, Berlin
Fuhrman DR (2001) Data assimilation and error prediction using local models. IHE, Delft
Gaur S, Deo MC (2008) Real-time wave forecasting using genetic programming. Ocean Eng 35(11–12):1166–1172
Gerritsen H, Twigt D, Mynett A, Calkoen C, Babovic V (2009) MH Box—analysis and prediction of Sea Level Anomalies and associated currents in Singapore and Malacca straits. In: Proc of 8th Int conf of Hydroinform, Chile, p 10. doi:188a163
Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks. Comput Geosci 36(5):620–627
Heemink AW, Verlaan M, Segers AJ (2001) Variance reduced ensemble kalman filtering. Mon Weather Rev 129(7):1718–1728
Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter technique. Mon Weather Rev 126(3):796–811
Kalman RE (1960) A new approach to linear filtering and prediction problems. J basic eng 82(1):35–45
Kalnay E (2003) Atmospheric modeling, data assimilation, and predictability. Cambridge University Press, Cambridge
Karri RR, Wang X, Ooi SK, Babovic V, Gerritsen H (2011) Improving predictions of water levels and currents for Singapore regional waters through Data Assimilation using OpenDA. In: Proc of 34th IAHR Biennial Congress, Brisbane, Australia, 2011. Engineers Australia, pp 4521–4528
Kernkamp H, Zijl F (2004) Further hydraulic model studies for Pulau Ubin & Pulau Tekong reclamation scheme. Interim report on hydrodynamic modelling—model set-up and calibration. Delft Hydraulics Report Z3437, Delft, The Netherlands
Kurniawan A, Ooi SK, Gerritsen H, and Twigt D (2010) Calibrating the regional tidal prediction of the Singapore Regional Model using OpenDA. In: Tao J, Chen Q, Liong S-Y (eds) Proc of 9th Int Conf of Hydroinform, Tianjin, China, 2010. Chemical Industry. pp 1406–1413
Kurniawan A, Ooi SK, Hummel S, Gerritsen H (2011) Sensitivity analysis of the tidal representation in Singapore Regional Waters in a data assimilation environment. Ocean Dyn 61(8):1121–1136
Madsen H, Canizares R (1999) Comparison of extended and ensemble Kalman filters for data assimilation in coastal area modelling. Int J Numer Methods Fluids 31(6):961–981
Moeini M, Etemad-Shahidi A, Chegini V, Rahmani I (2012) Wave data assimilation using a hybrid approach in the Persian Gulf. Ocean Dyn 62(5):785–797
Ooi SK, Zemskyy P, Sisomphon P, Gerritsen H, Twigt D (2009) The effect of grid resolution and weather forcing on hydrodynamic modelling of South East Asian waters In: Proc of 33rd IAHR Congress, Vancouver, Canada, pp 3712–3719
Ponsar S, Luyten P (2009) Data assimilation with the EnKF in a 1-D numerical model of a North Sea station. Ocean Dyn 59(6):983–996
Rao R, Babovic V (2010) Genetic programming based sea level anomaly forecasting models as data assimilation tools. In: Proc of 17th IAHR-APD cong, Auckland, New Zealand
Rao R, Gerritsen H, Van den Boogaard H, Babovic V (2010) South China Sea winds as a triggering mechanism for sea level anomalies around the Singapore coast. In: Jinhua Tao, Qiuwen Chen, Liong S-Y (eds) Proc of 9th Int Conf on Hydroinform, Tianjin, China, 2010. Chemical Industry, pp 1266–1273
Sannasiraj SA, Babovic V, Chan ES (2005) Local model approximation in the real time wave forecasting. Coast Eng 52(3):221–236
Sannasiraj SA, Zhang H, Babovic V, Chan ES (2004) Enhancing tidal prediction accuracy in a deterministic model using chaos theory. Adv Water Resour 27(7):761–772
Sorensen JVT, Madsen H (2004) Efficient Kalman filter techniques for the assimilation of tide gauge data in three-dimensional modeling of the North Sea and Baltic Sea system. J Geophys Res 109(C3):C03017
Sun Y, Babovic V, Chan ES (2010) Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory. J Hydrol 395(1–2):109–116
Sun Y, Babovic V, Chan ES (2012) Artificial neural networks as routine for error correction with an application in Singapore regional model. Ocean Dyn 62(5):661–669
Tippett MK, Anderson JL, Bishop CH, Hamill TM, Whitaker JS (2003) Ensemble square root filters. Mon Weather Rev 131(7):1485–1490
van Maren DS, Gerritsen H (2012) Residual flow and tidal asymmetry in the Singapore Strait, with implications for resuspension and residual transport of sediment. J Geophys Res 117(C4):C04021
Weerts AH, El Serafy GY, Hummel S, Dhondia J, Gerritsen H (2010) Application of generic data assimilation tools (DATools) for flood forecasting purposes. Comput Geosci 36(4):453–463
Wei J, Malanotte-Rizzoli P (2010) Validation and application of an ensemble Kalman filter in the Selat Pauh of Singapore. Ocean Dyn 60(2):395–401
Yen P-H, Jan C-D, Lee Y-P, Lee H-F (1996) Application of Kalman filter to short-term tide level prediction. J WaterW Port C 122(5):226–231
Zhang ZX, Li CW, Qi YQ, Li YS (2006) Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind-wave model for wave forecasting. J Hydroinform 8(1):65–76
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible Editor: Richard Signell
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10236-012-0584-y