A Multiple-Model Convection-Permitting Ensemble Examination of the Probabilistic Prediction of Tropical Cyclones: Hurricanes Sandy (2012) and Edouard (2014)

Christopher Melhauser Department of Meteorology and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania

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Fuqing Zhang Department of Meteorology and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania

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Yonghui Weng Department of Meteorology and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania

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Yi Jin Naval Research Laboratory, Monterey, California

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Hao Jin Naval Research Laboratory, Monterey, California

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Qingyun Zhao Naval Research Laboratory, Monterey, California

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Abstract

This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles were capable of producing similar ensemble statistics for track and intensity for the first 36–48 h of model integration, with biases in the ensemble mean evident at longer forecast lead times along with increased variability in spread. The ensemble spread of a multicore ensemble with members sampled from single-core ensembles was generally as large or larger than any constituent model, especially at longer lead times. Systematically varying the physic parameterizations in the WRF-ARW ensemble can alter both the forecast ensemble mean and spread to resemble the ensemble performance using a different forecast model. Compared to the control WRF-ARW experiment, the application of the stochastic kinetic energy backscattering scheme had minimal impact on the ensemble spread of track and intensity for both cases, while the use of stochastic perturbed physics tendencies increased the ensemble spread in track for Sandy and in intensity for both cases. This case study suggests that it is important to include model physics uncertainties for probabilistic TC prediction. A single-core multiphysics ensemble can capture the ensemble mean and spread forecasted by a multicore ensemble for the presented case studies.

© 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: Prof. Fuqing Zhang, fzhang@psu.edu

Abstract

This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles were capable of producing similar ensemble statistics for track and intensity for the first 36–48 h of model integration, with biases in the ensemble mean evident at longer forecast lead times along with increased variability in spread. The ensemble spread of a multicore ensemble with members sampled from single-core ensembles was generally as large or larger than any constituent model, especially at longer lead times. Systematically varying the physic parameterizations in the WRF-ARW ensemble can alter both the forecast ensemble mean and spread to resemble the ensemble performance using a different forecast model. Compared to the control WRF-ARW experiment, the application of the stochastic kinetic energy backscattering scheme had minimal impact on the ensemble spread of track and intensity for both cases, while the use of stochastic perturbed physics tendencies increased the ensemble spread in track for Sandy and in intensity for both cases. This case study suggests that it is important to include model physics uncertainties for probabilistic TC prediction. A single-core multiphysics ensemble can capture the ensemble mean and spread forecasted by a multicore ensemble for the presented case studies.

© 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: Prof. Fuqing Zhang, fzhang@psu.edu
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