Application of hybrid Kalman filter for improving water level forecast

Numerical modeling is one of the popular means to simulate and forecast the state of oceanographic systems. However, it still suffers from some limitations, e.g., parameter uncertainties, simplification of model assumptions, absence of data for proper boundary and initial conditions. This paper proposes a hybrid data assimilation scheme, which combines Kalman filter (KF) with a data-driven model (local linear model (LM)), to directly correct numerical model outputs at locations without measurements. Two different types of KF (unscented Kalman filter and two-sample Kalman filter) are tested and compared. A local LM is utilized to describe the evolution of model state and then assimilated into the KF. This in turns implifies the application of KF for highly complex nonlinear systems such as the dynamic motion of Singapore regional water. The proposed scheme is first examined using a simple hypothetical bay experiment followed by an operational modelof Singapore Regional Model (SRM) in which both are set up in Delft3D modeling environment. This combination of KF and data-driven model provides insights into the influence of different error covariance estimation on the model updating accuracy. This research also provides guidance to offline utilization of KF in updating of numerical model output.