SPACE–TIME MODELLING OF SYDNEY HARBOUR WINDS

Abstract

SummaryThis paper develops a space‐time statistical model for local forecasting of surface‐level wind fields in a coastal region with complex topography. The statistical model makes use of output from deterministic numerical weather prediction models which are able to produce forecasts of surface wind fields on a spatial grid. When predicting surface winds at observing stations, errors can arise due to sub‐grid scale processes not adequately captured by the numerical weather prediction model, and the statistical model attempts to correct for these influences. In particular, it uses information from observing stations within the study region as well as topographic information to account for local bias. Bayesian methods for inference are used in the model, with computations carried out using Markov chain Monte Carlo algorithms. Empirical performance of the model is described, illustrating that a structured Bayesian approach to complicated space‐time models of the type considered in this paper can be readily implemented and can lead to improvements in forecasting over traditional methods.