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

College of Marine Science

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

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	American English
Journal	Atmospheric Science Letters
Volume	16
DOIs	https://doi.org/10.1002/asl2.525
State	Published - Jan 1 2015

Keywords

constant flux layer
Deacon numbers
stable stratification
turbulence parameterization

Disciplines

Life Sciences

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https://doi.org/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.",

keywords = "constant flux layer, Deacon numbers, stable stratification, turbulence parameterization",

author = "Esa-Matti Tastula and Boris Galperin and Semion Sukoriansky and Ashok Luhar and Phil Anderson",

year = "2015",

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doi = "10.1002/asl2.525",

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T1 - The Importance of Surface Layer Parameterization in Modeling of Stable Atmospheric Boundary Layers

AU - Tastula, Esa-Matti

AU - Galperin, Boris

AU - Sukoriansky, Semion

AU - Luhar, Ashok

AU - Anderson, Phil

PY - 2015/1/1

Y1 - 2015/1/1

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.

KW - constant flux layer

KW - Deacon numbers

KW - stable stratification

KW - turbulence parameterization

UR - https://digitalcommons.usf.edu/msc_facpub/1498

UR - https://doi.org/10.1002/asl2.525

U2 - 10.1002/asl2.525

DO - 10.1002/asl2.525

M3 - Article

VL - 16

JO - Atmospheric Science Letters

JF - Atmospheric Science Letters

ER -

The Importance of Surface Layer Parameterization in Modeling of Stable Atmospheric Boundary Layers

Abstract

Keywords

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