Better Living Through Post-Processing: Improving Probabilistic Heavy Rainfall Prediction

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 over a long period. Effective techniques for displaying this information remain in development. Here we demonstrate a method for predicting heavy precipitation over the eastern United States at short (0-18-h) lead times, using an time-lagged ensemble of 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. A multi-agency collaboration will facilitate operational implementation of this tool, by leveraging social science experts to design effective graphics, and using testbeds to streamline evaluation and training within an operational environment. Future work will involve testing with other types of short-range ensembles (e.g., multi-core or single suite, stochastic physics), and expanding the tool to additional hazards, such as winter precipitation, severe convective storms, high wind speeds, icing and low visibility.