Quantifying the Skill of the Global Ensemble Forecasting System in Predicting Observed Precipitation Based on 20 Years of Reforecast Data

Ensemble forecast systems are receiving increased attention as a tool for assessing forecast confidence, particularly in the medium range (3–8 days). For example, the National Weather Service's Western Region Headquarters is aggressively promoting the use of ensemble-based situational awareness tools, many of which focus on anticipating heavy precipitation events in the medium range. However, it is generally accepted that the medium-range quantitative precipitation forecast (QPF) from global ensembles has limited skill. Hence, recent studies focusing on the western U.S. have shown that more spatially coherent “proxy” variables, such as integrated water vapor (IWV; Ralph et al. 2004) and IWV transport (IVT; Rutz et al. 2014) are highly correlated with observed precipitation over complex terrain. Despite the increasing attention these variables are receiving, a fundamental question remains: are forecasts of proxy variables more skillful in predicting observed precipitation than the model QPF itself? We address this question by examining 20 years (1993–2012) of cool-season (October–March), Global Ensemble Forecast System (GEFS) reforecast data (Hamill et al. 2013) to quantify the relationship between selected forecast variables (QPF, IWV, IVT, and IVT convergence) and observed precipitation. We follow a model-climate approach, where the percentile rank of the ensemble-mean forecast for a given variable and lead time (relative to all forecasts at that lead time) is compared to 1) the analyzed percentile rank of that variable (relative to all analyses) and 2) the percentile rank of observed 24-h precipitation (relative to all 24-h totals). By focusing on percentile ranks, this approach normalizes the magnitudes of ensemble-mean forecasts at different lead times. Results are presented regarding the skill of GEFS ensemble-mean forecasts for the selected variables and the usefulness of each in predicting observed precipitation. A key aspect of this analysis is to determine, for a variety of variables, lead times, and geographic areas, whether exceptional forecasts relative to the model climatology can be reliable indicators of exceptional precipitation events over complex terrain.