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. 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. Various sensitivity experiments are performed in order to determine the optimal number of ensemble members, neighborhood size, member weighting and latency. Forecasts are calibrated using the Stage-IV precipitation analysis, with the goal of developing a system capable of correcting biases from a relatively short training dataset. The technique is evaluated using a large archive of model forecasts spanning multiple seasons. 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, including but not limited to: winter precipitation, severe convective storms, high wind speeds, icing and low visibility.
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