Many time-lagged ensembles are underdispersive, meaning that an envelope of forecasts initialized at successive hours tends to underestimate the range of possible future states. To address this issue in the HRRR-TLE, we employ statistical post-processing methods, such as spatial filtering schemes, to augment the ensemble spread and achieve more reliable probabilistic forecasts. Prior to the application of a spatial filter, the quantitative precipitation forecast from each member of the HRRR-TLE is bias corrected using a threshold frequency technique and a relatively short training dataset, which further improves the reliability of probabilistic heavy rainfall forecasts. In future, similar bias-correction techniques will be applied to other fields using trusted analyses and observation datasets.
This presentation will include an overview of the reasoning behind the current HRRR-TLE configuration, a description of hazard identification algorithms, a demonstration of the improvement offered by statistical post-processing, and finally several case-study examples to show typical forecast output.