Probabilistic Hazard Products Using a Time-Lagged HRRR Ensemble

Operational forecasters are being provided today with an increasing range of probabilistic hazard detection tools, however many of these tools lack sufficient calibration or objectively demonstrated skill and reliability over a long period. Furthermore, effective techniques for displaying this information are still in development. Here we demonstrate a method for predicting heavy precipitation over the United States at short (0-18 hr) lead times, using a time-lagged ensemble of real-time forecasts from the High-Resolution Rapid Refresh model. Forecasts are calibrated using the Stage-IV precipitation analysis, with the goal of developing a system capable of real-time bias correction from a relatively short training dataset. Several methods are employed for displaying the resulting probabilistic information designed to highlight multiple hazards over several accumulation thresholds and time periods. Future work will involve testing with other types of short-range ensembles (e.g., multi-core and stochastic physics), and expanding the tool to additional hazards, such as winter precipitation, severe convective storms, high wind speeds, icing and low visibility.