Enhancing water level prediction through model residual correction based on Chaos theory and Kriging

Hydrodynamic models based on the physical processes are indispensable tools for predicting water levels in ocean environment. Nonetheless, their accuracies are limited by various factors such as simplifying assumptions, complex ocean bathymetry, and so on. Residual correction, as one of the data assimilation techniques, can extract information from observation and assimilate it into a numerical model to correct the model output directly. Such correction is often performed in two steps: prediction of the model residuals at measured stations followed by spatial distribution at non‐measured locations. For long‐term residual forecast, the accuracy of prediction usually deteriorates with the forecast horizon. In addition to the residual correction at measurement locations, in this paper, we address the critical question as to how to effectively update outputs for computational points without measurements. We develop a hybrid data assimilation procedure, which combines a modified local model (MLM) and an approximated ordinary kriging (AOK). This technique improves the forecasts over a long horizon over the entire computational domain. Using the proposed residual correction technique, the hybrid procedure is examined on a case study of Singapore Regional Model for correcting the water level outputs at locations with and without measurements. In order to provide a comparison, the analysis is carried out throughout prediction horizon of model residuals, tidal residuals, and sea level anomaly, respectively. The comparisons show that the proposed method can successfully assimilate and forecast all variables. Results indicate that resulting prediction accuracy can be significantly improved for all locations of interest independently of the forecast horizon. Copyright © 2014 John Wiley & Sons, Ltd.