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Application of Transilient Turbulence Theory to Mesoscale Numerical Weather Forecasting

William H. RaymondCooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, Wisconsin

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Roland B. StullDepartment of meteorology, University of Wisconsin, Madison, Wisconsin

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Abstract

In this study we show that a unified turbulence parameterization, when divorced from the smoothing procedures needed for numerical stability of the host model, can be implemented in a numerical weather prediction model. Our host model is the 15-layer National Center for Atmospheric Research (NCAR)/Pennsylvania State University regional three-dimensional (3-D) primitive equation mesoscale model. The unified approach utilizes transilient turbulence theory to replace many separate empirical approximations. A sixth-order implicit tangent filter is separately applied for numerical stability of the host model.

The OSCAR IV and CAPTEX datasets are used as case studies, and 72-hour forecasts are compared to control forecasts made using the original Blackadar boundary-layer version of the mesoscale model. These comparisons highlight the role of turbulence and its effect on the overall forecast. With the new turbulence parameterization we are able to produce evolving turbulent boundary layers and turbulence at higher altitudes. The turbulence is found to be sensitive to various feedback processes that modulate convection. We highlight the positive and negative aspects of the new turbulence parameterization scheme.

Abstract

In this study we show that a unified turbulence parameterization, when divorced from the smoothing procedures needed for numerical stability of the host model, can be implemented in a numerical weather prediction model. Our host model is the 15-layer National Center for Atmospheric Research (NCAR)/Pennsylvania State University regional three-dimensional (3-D) primitive equation mesoscale model. The unified approach utilizes transilient turbulence theory to replace many separate empirical approximations. A sixth-order implicit tangent filter is separately applied for numerical stability of the host model.

The OSCAR IV and CAPTEX datasets are used as case studies, and 72-hour forecasts are compared to control forecasts made using the original Blackadar boundary-layer version of the mesoscale model. These comparisons highlight the role of turbulence and its effect on the overall forecast. With the new turbulence parameterization we are able to produce evolving turbulent boundary layers and turbulence at higher altitudes. The turbulence is found to be sensitive to various feedback processes that modulate convection. We highlight the positive and negative aspects of the new turbulence parameterization scheme.

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