Modelling sea surface wind measurements on Australia's North-West Shelf

The environment experienced by offshore industries is inherently volatile, providing a strong impetus for accurate modelling and prediction of meteorological and oceanographic conditions. Numerical forecasts, obtained from physics-based deterministic models, are informative yet imperfect in their prediction, motivating the quantification of their associated uncertainty. To achieve this, we present a Bayesian hierarchical model that updates numerical forecasts of metocean conditions and examines their associated error structure. This involves a linear regression to remove systematic forecast biases and the use of time series techniques, namely autoregressive and generalised autoregressive conditional heteroscedasticity models, to remove residual time-evolving error structure. We find that the hierarchical statistical model outperforms a numerical weather prediction model in terms of mean squared error and continuous ranked probability score, a metric which is able to account for uncertainty information contained in probabilistic forecasts. While the method is generalisable to a range of metocean conditions with an available set of past measurements and numerical forecasts, we present our findings using a case study of sea surface wind data. The data is collected from a location on Australia's North-West Shelf, a region home to an extent of offshore operations and often characterised by extreme unexpected metocean conditions.