A Stochastic Total Tendency Perturbation Scheme Representing Model-Related Uncertainties in the NCEP Global Ensemble Forecast System

A stochastic representation of random errors associated with the numerical weather prediction model used in ensemble prediction systems is described and its impacts in the NCEP Global Ensemble Forecast System (GEFS) documented. In the proposed Stochastic Total Tendency Perturbation (STTP) scheme, stochastic forcing terms are formulated by randomly combining the conventional total tendencies of ensemble perturbations and rescaling them to appropriate sizes. The perturbation tendencies are estimated using finite differences with a time interval of 6 hours in its current implementation. Extensive experiments were performed and the results show that the scheme can significantly increase the ensemble spread while reducing outliers and systematic errors in the ensemble mean forecast and improving ensemble-based probabilistic forecasts as well as the ensemble forecast distribution. Forecast improvement is more consistent in the tropics than the extratropics, and more prominent in the cool season than the warm season. In the tropics, STTP improves the forecast by increasing both the statistical reliability and the resolution, while the resolution is hardly affected in the extratropics. In addition, the impact of the scheme is independent of model improvements in formulation or increase in spatial resolution. The scheme has been used in GEFS operational production since Feb. 23, 2010.

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