A Method for Calibrating Deterministic Forecasts of Rare Events

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's historical spatial error characteristics. This kernel density function is then applied to individual forecast fields to produce probabilistic forecasts.