Anticipating Significant Precipitation Events using a Model-Climate Approach

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Friday, 3 July 2015: 9:30 AM
Salon A-2 (Hilton Chicago)
Jonathan J. Rutz, NOAA/NWS, Salt Lake City, Utah; and T. I. Alcott

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 observed precipitation. To assess the predictive skill of the GEFS, we compare the percentile rank of the ensemble-mean reforecast for a given variable and lead time (relative to all reforecasts at that lead time, i.e., the "model climate") to the percentile rank of the corresponding analysis (relative to all analyses). By focusing on percentile ranks, this approach normalizes the probability density functions of ensemble-mean forecasts and analyses for different variables and different lead times, allowing for more intuitive comparisons between these quantities. 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.

Additionally, we match 2 years (2013–2014) of operational GEFS QPF to the reforecast model climate and assess the skill of the percentile approach in anticipating significant precipitation events in real time. Our results are favorable for predicting 90th and 95th percentile precipitation events at lead times from 1 to 5 days, most notably for cases where heavy precipitation occurs over a wide area.