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Atmospheric near surface wind speed and wind direction play an important rolein many applications, ranging from air quality modeling, building design, windturbine placement to climate change research. It is therefore crucial toaccurately estimate the joint probability distribution of wind speed anddirection. In this work we develop a conditional approach to model these twovariables, where the joint distribution is decomposed into the product of themarginal distribution of wind direction and the conditional distribution ofwind speed given wind direction. To accommodate the circular nature of winddirection a von Mises mixture model is used; the conditional wind speeddistribution is modeled as a directional dependent Weibull distribution via atwo-stage estimation procedure, consisting of a directional binned Weibullparameter estimation, followed by a harmonic regression to estimate thedependence of the Weibull parameters on wind direction. A Monte Carlosimulation study indicates that our method outperforms an alternative methodthat uses periodic spline quantile regression in terms of estimationefficiency. We illustrate our method by using the output from a regionalclimate model to investigate how the joint distribution of wind speed anddirection may change under some future climate scenarios.

Joint modeling of wind speed and wind direction through a conditional approach