Forecasts of the 500 mb Height Using a Dynamically Oriented Statistical Model

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  • 1 Scripps institution of Oceanography, University of California San Diego, La Jolla, CA 92093
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Abstract

The forecast skill of a simple dynamically inspired statistical model of the Northern Hemisphere 500 mb height field is evaluated in spectral and physical space for a variety of forecast lead times (1–32 days) and predictand averaging times (1–32 days). The model includes viscous damping, wave propagation, climatology and implicit stochastic forcing. The largest model skill was found for forecasts of the zonal flow and the largest waves. In general, the largest forecast skills were also associated with the largest forecast error, there being a slight geographic phase shift of the skill with respect to the error. Model skills for climate (time-averaged) forecasts are greater when using instantaneous rather than time averaged. Analysis of model errors suggests areas for improvement in representing forcing terms and model physics. However, the model error fields are largely “white noise” which suggests that global forecast substantially larger than those obtained here are unlikely to be achieved by more sophisticated models.

Abstract

The forecast skill of a simple dynamically inspired statistical model of the Northern Hemisphere 500 mb height field is evaluated in spectral and physical space for a variety of forecast lead times (1–32 days) and predictand averaging times (1–32 days). The model includes viscous damping, wave propagation, climatology and implicit stochastic forcing. The largest model skill was found for forecasts of the zonal flow and the largest waves. In general, the largest forecast skills were also associated with the largest forecast error, there being a slight geographic phase shift of the skill with respect to the error. Model skills for climate (time-averaged) forecasts are greater when using instantaneous rather than time averaged. Analysis of model errors suggests areas for improvement in representing forcing terms and model physics. However, the model error fields are largely “white noise” which suggests that global forecast substantially larger than those obtained here are unlikely to be achieved by more sophisticated models.

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