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Evaluation of an ARPS‐based canopy flow modeling system for use in future operational smoke prediction efforts
Author(s) -
Kiefer M. T.,
Zhong S.,
Heilman W. E.,
Charney J. J.,
Bian X.
Publication year - 2013
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/jgrd.50491
Subject(s) - canopy , environmental science , turbulence , turbulence kinetic energy , meteorology , atmospheric sciences , mean flow , flow (mathematics) , wind speed , mechanics , geology , geography , physics , archaeology
Efforts to develop a canopy flow modeling system based on the Advanced Regional Prediction System (ARPS) model are discussed. The standard version of ARPS is modified to account for the effect of drag forces on mean and turbulent flow through a vegetation canopy, via production and sink terms in the momentum and subgrid‐scale turbulent kinetic energy (TKE) equations. Additionally, a downward decaying net radiation profile inside the canopy is used to account for the attenuation of net radiation by vegetation elements. As a critical step in the model development process, simulations performed with the new canopy model, termed ARPS‐CANOPY, are examined and compared to observations from the Canopy Horizontal Array Turbulence Study (CHATS) experiment. Comparisons of mean and turbulent flow properties in a statistically homogeneous atmosphere are presented for two cases, one when the trees are dormant without leaves and another when the trees are full of mature leaves. The model is shown to reproduce the shape of the vertical profiles of mean wind, temperature, and TKE observed during the CHATS experiment, with errors generally smaller in the afternoon and in the case with stronger mean flow. Sensitivity experiments with relatively coarse (90 m) horizontal grid spacing retain the overall mean profile shapes and diurnal trends seen in the finer‐resolution simulations. The work described herein is part of a larger effort to develop predictive tools for close‐range (on the order of 1 km from the source) smoke dispersion from low‐intensity fires within forested areas.