Assessment of the High-Resolution Rapid Refresh Model’s Ability to Predict Mesoscale Convective Systems Using Object-Based Evaluation

AbstractAn object-based verification technique that keys off the radar-retrieved vertically integrated liquid (VIL) is used to evaluate how well the High-Resolution Rapid Refresh (HRRR) predicted mesoscale convective systems (MCSs) in 2012 and 2013. It is found that the modeled radar VIL values are roughly 50% lower than observed. This mean bias is accounted for by reducing the radar VIL threshold used to identify MCSs in the HRRR. This allows for a more fair evaluation of the model’s skill at predicting MCSs. Using an optimized VIL threshold for each summer, it is found that the HRRR reproduces the first (i.e., counts) and second moments (i.e., size distribution) of the observed MCS size distribution averaged over the eastern United States, as well as their aspect ratio, orientation, and diurnal variations. Despite threshold optimization, the HRRR tended to predict too many (few) MCSs at lead times less (greater) than 4 h because of lead time–dependent biases in the modeled radar VIL. The HRRR predicted ...

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