Search

A method for determining baselines of skill for the purpose of the verification of rare-event forecasts is described and examples are presented to illustrate the sensitivity to parameter choices. These 'practically perfect' forecasts are designed to resemble a forecast that is consistent with that which a forecaster would make given perfect knowledge of the events beforehand. The Storm Prediction Center's convective outlook slight risk areas are evaluated over the period from 1973 to 2011 using practically perfect forecasts to define the maximum values of the critical success index that a forecaster could reasonably achieve given the constraints of the forecast, as well as the minimum values of the critical success index that are considered the baseline for skillful forecasts. Based on these upper and lower bounds, the relative skill of convective outlook areas shows little to no skill until the mid-1990s, after which this value increases steadily. The annual frequency of skillful daily forecasts continues to increase from the beginning of the period of study, and the annual cycle shows maxima of the frequency of skillful daily forecasts occurring in May and June. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]