Systematic Surface Cyclone Errors in NMC's Nested Grid Model November 1988–January 1989

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  • 1 National Meteorological Center, World Weather Budding, Washington, D.C.
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Abstract

Preliminary results from a study examining the performance of the nested grid model (NGM) in predicting cyclones covering a period from 13 November 1988 to 31 Match 1989 reveal that the NGM tends to overdevelop surface cyclones over continental regimes and underdevelop surface cyclones in oceanic regimes. The results also indicate an overall cold bias in the model forecasts of thickness and 850 mb temperatures over the cyclone center. Displacement error data indicate the NGM tends to move cyclones too slowly in the southern half of the forecast domain.

A semiautomated method has been developed at the National Meteorological Center (NMC) to track and verify sea level pressure features in the NGM. The method allows the user to interactively track a system, store its coordinates, and then retrieve information about the system from selected model forecast and analysis grids. This information can then be used to determine systematic forecast errors, compare past forecasts with the most recent forecast, and produce climatological tracks of forecast and observed systems.

Abstract

Preliminary results from a study examining the performance of the nested grid model (NGM) in predicting cyclones covering a period from 13 November 1988 to 31 Match 1989 reveal that the NGM tends to overdevelop surface cyclones over continental regimes and underdevelop surface cyclones in oceanic regimes. The results also indicate an overall cold bias in the model forecasts of thickness and 850 mb temperatures over the cyclone center. Displacement error data indicate the NGM tends to move cyclones too slowly in the southern half of the forecast domain.

A semiautomated method has been developed at the National Meteorological Center (NMC) to track and verify sea level pressure features in the NGM. The method allows the user to interactively track a system, store its coordinates, and then retrieve information about the system from selected model forecast and analysis grids. This information can then be used to determine systematic forecast errors, compare past forecasts with the most recent forecast, and produce climatological tracks of forecast and observed systems.

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