A vector autoregressive model to predict hurricane tracks

Abstract

The stochastic behaviour of hurricane tracks is expressed by a vector autoregressive time series model. Historical data and correlation analysis were used to identify the model structure of a typical hurricane track. The parameter estimation scheme is based on recursive and iterative algorithms. The recursive approach is used when a small number of points have been collected from a hurricane. On the Other hand, iterative algorithms are used when enough information for optimal estimation is available. The multivariate time series model was used to predict hurricane tracks during the 1990 hurricane season in the North Atlantic ocean. Prediction errors from the vector autoregressive model are compared with errors from the NHC90 model. The NHC90 model performs better than the studied model; however, the vector autoregressive model uses a small amount of information and may help to reduce official forecasting errors.