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 these 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 a von Mises mixture model is used; the conditional wind speed distribution is modeled as a directional dependent Weibull distribution via a two-stage estimation procedure, consisting of a directional binned Weibull parameter estimation, followed by a harmonic regression to estimate the dependence of the Weibull parameters on wind direction. A Monte Carlo simulation study indicates that our method outperforms an alternative method that uses periodic spline quantile regression in terms of estimation efficiency. We illustrate our method by using the output from a regional climate model to investigate how the joint distribution of wind speed and direction may change under some future climate scenarios.
翻译:从空气质量模型、建筑设计、风轮机布置到气候变化研究等许多应用中,接近表面风速和风向的大气在空气质量建模、建筑设计、风轮机布置到气候变化研究等方面发挥着重要作用,因此,准确估计风速和风向的共同概率分布至关重要,因此,在这项工作中,我们制定了一个有条件的模型来模拟这两个变量,在这两个变量中,联合分布被分解成风向边缘分布和风速按风向有条件分布的风速的产物。为了适应风向的循环性质,使用了冯·米泽斯混合物模型;有条件的风速分布通过一个两阶段估计程序作为方向依赖性Wibull分布的模型,其中包括方向性宾内德·魏布尔参数估计,然后进行合力回归,以估计Wibull参数对风向的依赖性。蒙特卡洛模拟研究表明,我们的方法超越了一种在估计效率方面使用定期螺纹孔回归的替代方法。我们用区域气候模型的产出来说明我们的方法,以调查未来气候情景下风速和方向的联合分布如何变化。