Sensitivity Analysis of Hurricane Arthur (2014) Storm Surge Forecasts to WRF Physics Parameterizations and Model Configurations

Fan Zhang Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, Maryland

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Ming Li Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, Maryland

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Andrew C. Ross Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

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Serena Blyth Lee Griffith School of Engineering, Griffith Climate Change Response Program, Griffith Centre for Coastal Management, Griffith University, Gold Coast, Queensland, Australia

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Da-Lin Zhang Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland

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Abstract

Through a case study of Hurricane Arthur (2014), the Weather Research and Forecasting (WRF) Model and the Finite Volume Coastal Ocean Model (FVCOM) are used to investigate the sensitivity of storm surge forecasts to physics parameterizations and configurations of the initial and boundary conditions in WRF. The turbulence closure scheme in the planetary boundary layer affects the prediction of the storm intensity: the local closure scheme produces lower equivalent potential temperature than the nonlocal closure schemes, leading to significant reductions in the maximum surface wind speed and surge heights. On the other hand, higher-class cloud microphysics schemes overpredict the wind speed, resulting in large overpredictions of storm surge at some coastal locations. Without cumulus parameterization in the outermost domain, both the wind speed and storm surge are grossly underpredicted as a result of large precipitation decreases in the storm center. None of the choices for the WRF physics parameterization schemes significantly affect the prediction of Arthur’s track. Sea surface temperature affects the latent heat release from the ocean surface and thus storm intensity and storm surge predictions. The large-scale atmospheric circulation models provide the initial and boundary conditions for WRF, and influence both the track and intensity predictions, thereby changing the spatial distribution of storm surge along the coastline. These sensitivity analyses underline the need to use an ensemble modeling approach to improve the storm surge forecasts.

© 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: Fan Zhang, fzhang@umces.edu

Abstract

Through a case study of Hurricane Arthur (2014), the Weather Research and Forecasting (WRF) Model and the Finite Volume Coastal Ocean Model (FVCOM) are used to investigate the sensitivity of storm surge forecasts to physics parameterizations and configurations of the initial and boundary conditions in WRF. The turbulence closure scheme in the planetary boundary layer affects the prediction of the storm intensity: the local closure scheme produces lower equivalent potential temperature than the nonlocal closure schemes, leading to significant reductions in the maximum surface wind speed and surge heights. On the other hand, higher-class cloud microphysics schemes overpredict the wind speed, resulting in large overpredictions of storm surge at some coastal locations. Without cumulus parameterization in the outermost domain, both the wind speed and storm surge are grossly underpredicted as a result of large precipitation decreases in the storm center. None of the choices for the WRF physics parameterization schemes significantly affect the prediction of Arthur’s track. Sea surface temperature affects the latent heat release from the ocean surface and thus storm intensity and storm surge predictions. The large-scale atmospheric circulation models provide the initial and boundary conditions for WRF, and influence both the track and intensity predictions, thereby changing the spatial distribution of storm surge along the coastline. These sensitivity analyses underline the need to use an ensemble modeling approach to improve the storm surge forecasts.

© 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: Fan Zhang, fzhang@umces.edu
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