Enhanced Predictions of Tides and Surges through Data Assimilation

The regional waters in Singapore Strait are characterized by complex hydrodynamic phenomena as a result of the combined effect of three large water bodies viz. the South China Sea, the Andaman Sea, and the Java Sea. This leads to anomalies in water levels and generates residual currents. Numerical hydrodynamic models are generally used for predicting water levels in the ocean and seas. But their correctness is typically limited by several factors, namely the complexity associated with the coastal geometry and uncertainty in the flow forcing factors like (winds, pressure and deep ocean tides). Modeling of ocean dynamics in the Malacca strait and Singapore regional waters is particularly challenging due to the presence of large number of smaller islands and strongly nonlinear tidal interactions. The complexity is further enhanced due to the composite local bathymetry and geometry variations around the Singapore Island and meteorological effects on different scales. This study acknowledges the enhancement and better prediction of tides and surges through the use of data assimilation. Through a portable interface OpenDA, an ensemble Kalman filter is integrated with a hydrodynamic model to enhance the model predictions. To assess the sensitivity and evaluate model enhancement, a twin experiment is designed to improve tidal boundary forcing effect in a semi-enclosed estuary. The key outcomes of this study signify that the model results can be significantly improved in this complex flow regime.