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

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.

[1]  Arthur E. Mynett,et al.  Improving the operational forecasting system of the stratified flow in Osaka Bay using an ensemble Kalman filter–based steady state Kalman filter , 2008 .

[2]  D. S. van Maren,et al.  Residual flow and tidal asymmetry in the Singapore Strait, with implications for resuspension and residual transport of sediment , 2012 .

[3]  M. Verlaan,et al.  Tidal flow forecasting using reduced rank square root filters , 1997 .

[4]  H. Gerritsen,et al.  Sensitivity analysis of the tidal representation in Singapore Regional Waters in a data assimilation environment , 2011 .

[5]  Albrecht Weerts,et al.  OpenDA Open Source Generic Data Assimilation Environment and its Application in Process Models , 2010 .

[6]  Amir Etemad-Shahidi,et al.  Wave data assimilation using a hybrid approach in the Persian Gulf , 2012, Ocean Dynamics.

[7]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[8]  J. Stedinger,et al.  Appraisal of the generalized likelihood uncertainty estimation (GLUE) method , 2008 .

[9]  Herman Gerritsen,et al.  Application of generic data assimilation tools (DATools) for flood forecasting purposes , 2010, Comput. Geosci..

[10]  Paola Malanotte-Rizzoli,et al.  Validation and application of an ensemble Kalman filter in the Selat Pauh of Singapore , 2010 .

[11]  Vladan Babovic,et al.  Artificial neural networks as routine for error correction with an application in Singapore regional model , 2012, Ocean Dynamics.

[12]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .

[13]  A. Weerts,et al.  Application of data assimilation in portable operational forecasting systems—the DATools assimilation environment , 2007 .

[14]  Vladan Babovic,et al.  Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory , 2010 .

[15]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[16]  Dinh-Tuan Pham,et al.  Improving the singular evolutive extended Kalman filter for strongly nonlinear models for use in ocean data assimilation , 2001 .

[17]  A. Segers,et al.  Variance reduced ensemble Kalman filtering , 2001 .

[18]  M. C. Deo,et al.  Real-time wave forecasting using genetic programming , 2008 .

[19]  Yiquan Qi,et al.  Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind-wave model for wave forecasting , 2006 .

[20]  Mohammad Ali Ghorbani,et al.  Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks , 2010, Comput. Geosci..

[21]  Geoffrey W. Cowles,et al.  Application and comparison of Kalman filters for coastal ocean problems: An experiment with FVCOM , 2009 .

[22]  Henrik Madsen,et al.  Efficient Kalman filter techniques for the assimilation of tide gauge data in three‐dimensional modeling of the North Sea and Baltic Sea system , 2004 .

[23]  Christopher K. Wikle,et al.  Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.

[24]  J. Whitaker,et al.  Ensemble Square Root Filters , 2003, Statistical Methods for Climate Scientists.

[25]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[26]  Vladan Babovic,et al.  Improving predictions of water levels and currents for Singapore regional waters through Data Assimilation using OpenDA , 2011 .

[27]  Vladan Babovic,et al.  Error correction of a predictive ocean wave model using local model approximation , 2005 .

[28]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .

[29]  H. Madsen,et al.  Comparison of extended and ensemble Kalman filters for data assimilation in coastal area modelling , 1999 .

[30]  P. Luyten,et al.  Data assimilation with the EnKF in a 1-D numerical model of a North Sea station , 2009 .

[31]  V. Babovic,et al.  Enhancing tidal prediction accuracy in a deterministic model using chaos theory , 2004 .

[32]  Vladan Babovic,et al.  Local model approximation in the real time wave forecasting , 2005 .

[33]  ALAMSYAH KURNIAWAN.  CALIBRATING THE REGIONAL TIDAL PREDICTION OF THE SINGAPORE REGIONAL MODEL USING OPENDA , 2011 .

[34]  C. Jan,et al.  Application of Kalman Filter to Short-Term Tide Level Prediction , 1996 .