Predicting Direction of High‐Speed Wind: A Bayesian Approach to Conditional Cylindrical Distributions

High‐speed winds pose several dangers to both the built environment and the natural environment, affecting human lives and livelihoods, as well as endangering wildlife. On the other hand, high‐speed winds can be beneficially harnessed through the optimal orientation of turbines to maximize wind energy production. The management response to high‐speed winds is a complex process because not only is their speed of interest, but even more so, their direction. High‐speed winds are generally seasonal. In this paper, we propose a Bayesian prediction model for a circular variable (such as wind direction) conditional on a linear variable (such as high‐speed wind), model that can account for any sparsity in the (circular, linear) pair of data. The proposed Bayesian method comprises a mixture of conditional cylindrical models to capture potential multimodality in the distribution of the circular component and compares two different cylindrical distributions, namely the Abe–Ley distribution and the Kalaylioglu distribution. Monte Carlo simulation studies conducted for both distributions establish the benefit of our proposed method. Finally, we demonstrate the developed posterior predictive distribution in a real application, where we predict the direction of high‐speed winds using hourly in situ wind data from a location in South Africa.