Open AccessThe importance of surface layer parameterization in modeling of stable atmospheric boundary layers
Author(s) -
Tastula EsaMatti,
Galperin Boris,
Sukoriansky Semion,
Luhar Ashok,
Anderson Phil
Publication year - 2014
Publication title -
atmospheric science letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.951
H-Index - 45
ISSN - 1530-261X
DOI - 10.1002/asl2.525
Subject(s) - weather research and forecasting model , boundary layer , surface (topology) , planetary boundary layer , similarity (geometry) , environmental science , surface layer , meteorology , scale (ratio) , layer (electronics) , mathematics , atmospheric sciences , statistical physics , materials science , geology , physics , thermodynamics , computer science , geometry , artificial intelligence , image (mathematics) , composite material , quantum mechanics
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 R 2 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.