Large-Sample Application of Radar Reflectivity Object-Based Verification to Evaluate HRRR Warm-Season Forecasts

Abstract The Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification approximately 1400 forecasts composite reflectivity from the operational HRRR during April–September 2019. In this study, MODE configured prioritize deep, moist convective storm cells typical those that produce severe weather across central and eastern United States warm season. particular, attributes related distance size are given greatest attribute weights for computing interest in MODE. tends overforecast all objects, but substantially overforecasts both small objects at low-reflectivity thresholds large high-reflectivity thresholds. either underforecast southern plains or has a correct frequency bias there, whereas it States. Attribute comparisons reveal inability fully resolve convective-scale features impact data assimilation loss skill initial hours forecasts. Scalar metrics defined computed based on output, chiefly relying value. threat score (OTS), reveals similar performance as does Heidke score, with differing magnitudes, suggesting value adopting approach forecast verification. between centroids also analyzed shows gradual degradation increasing length.