The importance of surface layer parameterization in modeling of stable atmospheric boundary layers

Esa Matti Tastula; Boris Galperin; Semion Sukoriansky; Ashok Luhar; Phil Anderson

doi:10.1002/asl2.525

The importance of surface layer parameterization in modeling of stable atmospheric boundary layers

Esa Matti Tastula, Boris Galperin, Semion Sukoriansky, Ashok Luhar, Phil Anderson

Department of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin-Obukhov similarity theory. In this article, several surface-layer models in the format of velocity and potential temperature Deacon numbers are compared with observations from CASES99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R² demonstrated that the quasi-normal scale elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model.

Original language	English
Pages (from-to)	83-88
Number of pages	6
Journal	Atmospheric Science Letters
Volume	16
Issue number	1
DOIs	https://doi.org/10.1002/asl2.525
State	Published - 1 Jan 2015

Keywords

Constant flux layer
Deacon numbers
Stable stratification
Turbulence parameterization

ASJC Scopus subject areas

Atmospheric Science

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10.1002/asl2.525

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title = "The importance of surface layer parameterization in modeling of stable atmospheric boundary layers",

abstract = "The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin-Obukhov similarity theory. In this article, several surface-layer models in the format of velocity and potential temperature Deacon numbers are compared with observations from CASES99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R2 demonstrated that the quasi-normal scale elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model.",

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AU - Galperin, Boris

AU - Sukoriansky, Semion

AU - Luhar, Ashok

AU - Anderson, Phil

PY - 2015/1/1

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N2 - The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin-Obukhov similarity theory. In this article, several surface-layer models in the format of velocity and potential temperature Deacon numbers are compared with observations from CASES99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R2 demonstrated that the quasi-normal scale elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model.

AB - The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin-Obukhov similarity theory. In this article, several surface-layer models in the format of velocity and potential temperature Deacon numbers are compared with observations from CASES99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R2 demonstrated that the quasi-normal scale elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model.

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KW - Stable stratification

KW - Turbulence parameterization

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The importance of surface layer parameterization in modeling of stable atmospheric boundary layers

Abstract

Keywords

ASJC Scopus subject areas

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