Development of an adaptive Kalman filter-based storm tide forecasting model

Abstract

Extreme storm tide usually causes flooding of low-lying land in a coastal city. Hence, developing an efficient and accurate forecasting model for issuing a timely warning is important. In this study, an adaptive Kalman filter-based storm tide forecasting model was proposed and applied to the inner Harbor of Macau. The model is a dynamic linear regression model with the harmonic tidal prediction, wind speed, wind direction and atmospheric pressure as its input parameters. With persistence forecast of weather assumed during the prediction period, the model was tested with 40 cases of storm tide induced by tropical cyclones in Macau between 2005 and 2012. Success was found for forecasts with lead times up to 3 hours. The proposed adaptive model is considered a practical tool for storm tide forecast in small coastal cities.