A Comparison of Equilibrium and Time-Evolving Approaches to Modeling the Wind Profile under Stable Stratification

Michael Optis School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada

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Adam Monahan School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada

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Abstract

In this study, the authors contrast the modeling of the wind profile under stable stratification considering both equilibrium (i.e., constant in time) and time-evolving frameworks, as well as one-dimensional (1D) and 3D approaches. The models considered include an equilibrium-based single-column model (SCM), a time-evolving SCM, and a time-evolving 3D mesoscale model. Data obtained from the Cabauw meteorological tower in the Netherlands over a 10-yr period are used to drive the models and to assess model performance. First, a composite dataset of low-level jet (LLJ) case studies was used to demonstrate the ability of the time-evolving SCM and the mesoscale model to accurately simulate the evolving stratification, the inertial oscillation, and the LLJ. The equilibrium SCM did not accurately simulate the LLJ case studies. The mean performances of the different models in different stability classes over the 10-yr period were then compared. Both the equilibrium and time-evolving SCMs were found to overestimate wind speeds in weakly and moderately stable conditions because of the influence of an internal boundary layer but were found to be more accurate in the higher-stability classes. Frequent model breakdown and the tendency to underestimate stratification limited the usefulness of the equilibrium SCM. Despite its various limitations and simplified physics, the time-evolving SCM approach is found to perform comparably to the mesoscale model while using a fraction of the computational cost but requiring local observations. Consequently, an SCM approach may be useful in the context of commercial wind resource assessment.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Michael Optis, optism@gmail.com

Abstract

In this study, the authors contrast the modeling of the wind profile under stable stratification considering both equilibrium (i.e., constant in time) and time-evolving frameworks, as well as one-dimensional (1D) and 3D approaches. The models considered include an equilibrium-based single-column model (SCM), a time-evolving SCM, and a time-evolving 3D mesoscale model. Data obtained from the Cabauw meteorological tower in the Netherlands over a 10-yr period are used to drive the models and to assess model performance. First, a composite dataset of low-level jet (LLJ) case studies was used to demonstrate the ability of the time-evolving SCM and the mesoscale model to accurately simulate the evolving stratification, the inertial oscillation, and the LLJ. The equilibrium SCM did not accurately simulate the LLJ case studies. The mean performances of the different models in different stability classes over the 10-yr period were then compared. Both the equilibrium and time-evolving SCMs were found to overestimate wind speeds in weakly and moderately stable conditions because of the influence of an internal boundary layer but were found to be more accurate in the higher-stability classes. Frequent model breakdown and the tendency to underestimate stratification limited the usefulness of the equilibrium SCM. Despite its various limitations and simplified physics, the time-evolving SCM approach is found to perform comparably to the mesoscale model while using a fraction of the computational cost but requiring local observations. Consequently, an SCM approach may be useful in the context of commercial wind resource assessment.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Michael Optis, optism@gmail.com
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