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摘要
Convection-allowing models offer forecasters unique insight into convective hazards relative to numerical models using parameterized convection. However, methods to best characterize the uncertainty of guidance derived from convection-allowing models are still unrefined. This paper proposes a method of deriving calibrated probabilistic forecasts of rare events from deterministic forecasts by fitting a parametric kernel density function to the model%26apos;s historical spatial error characteristics. This kernel density function is then applied to individual forecast fields to produce probabilistic forecasts.
- 出版日期2012-4
