Joint modeling of wind speed and wind direction through a conditional approach
Eva Murphy, Whitney Huang, Julie Bessac, Jiali Wang, Rao Kotamarthi

TL;DR
This paper introduces a novel conditional modeling approach for joint wind speed and direction distribution, utilizing a von Mises mixture and directional-dependent Weibull distribution, with applications in climate scenario analysis.
Contribution
The paper develops a new conditional modeling framework that effectively captures the joint distribution of wind speed and direction, accommodating their circular and dependent nature.
Findings
The proposed method outperforms spline-based approaches in estimation efficiency.
Monte Carlo simulations validate the model's accuracy and robustness.
Application to climate model data reveals potential changes in wind patterns under future scenarios.
Abstract
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…
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Taxonomy
TopicsWind and Air Flow Studies · Vehicle emissions and performance · Air Quality and Health Impacts
