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Joint Modeling of Wind Speed and Wind Direction Through a Conditional Approach

Journal Article · · EnvironMetrics (London)
DOI:https://doi.org/10.1002/env.70011· OSTI ID:2563309
 [1];  [2];  [3];  [4];  [4]
  1. Wake Forest University, Winston-Salem, NC (United States)
  2. Clemson University, Clemson, SC (United States)
  3. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  4. Argonne National Laboratory (ANL), Argonne, IL (United States)
+ Show Author Affiliations
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 two other approaches in estimation efficiency: one that utilizes periodic spline quantile regression and another that generates data from the commonly used Abe-Ley distribution for cylindrical data. 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. Our method indicates significant changes in the variation of wind speed with respect to some directions.
Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
2563309
Report Number(s):
NREL/JA--2C00-93155; MainId:94937; UUID:b0c2bd74-ced8-404a-b3a5-dfc30dae6dcf; MainAdminId:76742
Journal Information:
EnvironMetrics (London), Journal Name: EnvironMetrics (London) Journal Issue: 3 Vol. 36; ISSN 1180-4009
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (12)

FIGURE 1(p. 2)figure FIGURE 1
FIGURE 2(p. 3)figure FIGURE 2
FIGURE 3(p. 5)figure FIGURE 3
FIGURE 4(p. 8)figure FIGURE 4
FIGURE 5(p. 9)figure FIGURE 5
TABLE 1(p. 10)table TABLE 1
TABLE 2(p. 11)table TABLE 2
FIGURE 6(p. 12)figure FIGURE 6
FIGURE 7(p. 12)figure FIGURE 7
FIGURE 8(p. 13)figure FIGURE 8
FIGURE 9(p. 14)figure FIGURE 9
FIGURE 10(p. 15)figure FIGURE 10

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Related Subjects

17 WIND ENERGY
97 MATHEMATICS AND COMPUTING
Abe-Ley distribution
conditional approach
periodic quantile regression
von mises
weibull
wind speed and direction