A spatio-temporal user-centric distance for forecast verification

Abstract

When predicting thunderstorms and localized severe weather events, close calls occur more frequently than direct hits. This makes it difficult for traditional verification approaches to fully represent forecast performance since events observed near a forecast event are counted both as misses and false alarms. Timing and location relative to the affected population are therefore two of the most important aspects of such a forecast. Verification of these aspects allows the determination of a safe distance for the user of a given severe weather alert. In this study, we propose a forecast verification measure based on the Generalized Distance Transform that is mathematically rigorous yet intuitive and user-friendly in its interpretation. The proposed measure compares the distance from a user location to an alert area against the distance from the same user location to a set of observed events. Time series for such comparisons can then be constructed, allowing evaluation of the timing error obtained from the difference between the two time series. Finally, the ‘worst overforecast’ and ‘worst underforecast’ are diagnosed in terms of relative distance.