A Stochastic Methodology for Regional Wind-Field Modeling

Schere, Kenneth L.; Coats, Carlie J.

doi:10.1175/1520-0450(1992)031<1407:asmfrw>2.0.co;2

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Microareas Of Wind Prediction Can Be Done For 48 H1

Introduction1

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Cited by 4 publications

(2 citation statements)

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“…Andreas et al [106] realized errors in the spatio-temporal interpolation methods. Wind patterns are modeled also by stochastic methods [107]. Zhong and Takle [108] investigated sealand breeze effects at complex topography by synoptic scale approaches.…”

Section: Microareas Of Wind Prediction Can Be Done For 48 Hmentioning

confidence: 99%

Progress and recent trends in wind energy

Sahin

2004

Progress in Energy and Combustion Science

275

100

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Section: Microareas Of Wind Prediction Can Be Done For 48 Hmentioning

confidence: 99%

Progress and recent trends in wind energy

Sahin

2004

Progress in Energy and Combustion Science

275

100

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“…He then developed methods to use an ensemble of states to compare predictions with observations. Schere and Coats (1992) addressed the data needs for characterizing regional-scale transport in an ensemble and concluded that air quality models should be evaluated in a probabilistic mode. Lewellen and Sykes (1989) cite this mesoscale meteorological variability as a major source of dispersion uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Parameterizing Mesoscale Wind Uncertainty for Dispersion Modeling

Peltier

Haupt

Wyngaard

et al. 2010

Journal of Applied Meteorology and Climatology

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A parameterization of numerical weather prediction uncertainty is presented for use by atmospheric transport and dispersion models. The theoretical development applies Taylor dispersion concepts to diagnose dispersion metrics from numerical wind field ensembles, where the ensemble variability approximates the wind field uncertainty. This analysis identifies persistent wind direction differences in the wind field ensemble as a leading source of enhanced ''virtual'' dispersion, and thus enhanced uncertainty for the ensemble-mean contaminant plume. This dispersion is characterized by the Lagrangian integral time scale for the gridresolved, large-scale, ''outer'' flow that is imposed through the initial and boundary conditions and by the ensemble deviation-velocity variance. Excellent agreement is demonstrated between an explicit ensemblemean contaminant plume generated from a Gaussian plume model applied to the individual wind field ensemble members and the modeled ensemble-mean plume formed from the one Gaussian plume simulation enhanced with the new ensemble dispersion metrics.

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Effects of biogenic emission uncertainties on regional photochemical modeling of control strategies

Roselle¹

1994

Atmospheric Environment

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A Stochastic Methodology for Regional Wind-Field Modeling

Cited by 4 publications

References 10 publications

Progress and recent trends in wind energy

Progress and recent trends in wind energy

Parameterizing Mesoscale Wind Uncertainty for Dispersion Modeling

Effects of biogenic emission uncertainties on regional photochemical modeling of control strategies

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