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Abstract:
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Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is, therefore, crucial to accurately estimate the joint probability distribution of wind speed and direction. In this work we develop a conditional approach to model the two variables, where the joint distribution is decomposed into the product of the marginal distribution of wind direction and the conditional distribution of wind speed given wind direction. To accommodate the circular nature of wind direction von Mises mixture distributions are used; the conditional wind speed distribution is modeled as a directional dependent Weibull distribution via a two-stage procedure, consisting of a binned Weibull parameter estimation, followed by a harmonic regression used to model the dependence of the Weibull parameters on wind direction. A Monte Carlo simulation study suggests that our method outperforms an alternative method that uses periodic quantile regression in terms of estimation efficiency and bias. We illustrate our method by using the outputs of climate model simulations
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