Ensemble-based forecast uncertainty analysis of diverse heavy rainfall events

This study examines widespread heavy rainfall over 5-day periods in the central and eastern United States. First, a climatology is presented that identifies events in which more than 100 mm of precipitation fell over more than 800 000 km² in 5 days. This climatology shows that such events are most common in the cool season near the Gulf of Mexico coast and are rare in the warm season. Then, the focus turns to the years 2007 and 2008, when nine such events occurred in the United States, all of them leading to flooding. Three of these were associated with warm-season convection, three took place in the cool season, and three were caused by landfalling tropical cyclones. Global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System are used to assess forecast skill and uncertainty for these nine events, and to identify the types of weather systems associated with their relative levels of skill and uncertainty. Objective verification metrics and subjective examination are used to determine how far in advance the ensemble identified the threat of widespread heavy rains. Specific conclusions depend on the rainfall threshold and the metric chosen, but, in general, predictive skill was highest for rainfall associated with tropical cyclones and lowest for the warm-season cases. In almost all cases, the ensemble provides very skillful 5-day forecasts when initialized at the beginning of the event. In some of the events—particularly the tropical cyclones and strong baroclinic cyclones—the ensemble still shows considerable skill in 96–216-h precipitation forecasts. In other cases, however, the skill drops off much more rapidly as lead time increases. In particular, forecast skill at long lead times was the lowest and spread was the largest in the two cases associated with meso-α-scale to synoptic-scale vortices that were cut off from the primary upper-level jet. In these cases, it appears that when the vortex is present in the initial conditions, the resulting precipitation forecasts are quite accurate and certain, but at longer lead times when the model is required to both develop and correctly evolve the vortex, forecast quality is low and uncertainty is large. These results motivate further investigation of the events that were poorly predicted.