Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer

Chao Liu, Li Fu, Dan Yang, David R. Miller, Junming Wang

Research output: Contribution to journalArticle

Abstract

Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality, gene flow (through pollen drift), and plant disease transmission (spore dispersion). Although Lagrangian stochastic (LS) models describe stochastic wind behaviors, such models assume that wind velocities follow Gaussian distributions. However, measured surface-layer wind velocities show a strong skewness and kurtosis. This paper presents an improved model, a non-Gaussian LS model, which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis. Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study, October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model. Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model, such as the accuracy in the mean and variance of simulated velocities. This improvement also leads to better accuracy in friction velocity (i.e., a coupling of three-dimensional velocities). The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations. Moreover, improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations. Thus, the non-Gaussian model is worth applying to wind field simulation in the surface layer.

Original languageEnglish (US)
Pages (from-to)90-104
Number of pages15
JournalAdvances in Atmospheric Sciences
Volume37
Issue number1
DOIs
StatePublished - Jan 1 2020

Fingerprint

wind field
surface layer
simulation
skewness
wind velocity
disease transmission
gene flow
spore
air quality
natural resource
friction
pollen
trajectory

Keywords

  • Lagrangian stochastic model
  • non-Gaussian wind velocity
  • surface layer
  • wind field simulation

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer. / Liu, Chao; Fu, Li; Yang, Dan; Miller, David R.; Wang, Junming.

In: Advances in Atmospheric Sciences, Vol. 37, No. 1, 01.01.2020, p. 90-104.

Research output: Contribution to journalArticle

Liu, Chao ; Fu, Li ; Yang, Dan ; Miller, David R. ; Wang, Junming. / Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer. In: Advances in Atmospheric Sciences. 2020 ; Vol. 37, No. 1. pp. 90-104.
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