Medium-Range Predictability of Historical Extreme Precipitation Events Associated with Mid-Upper Tropospheric Flow Reversal

Recent research has developed the Extreme Precipitation Index (EPI), a coupled dynamic-thermodynamic metric that can diagnose extreme precipitation events associated with mid-upper tropospheric flow reversal (e.g., Rex and Omega Blocks, cut-off cyclones, Rossby wave breaks). Several recent billion-dollar mid-latitude floods such as the 2013 Alberta Flood and 2016 Western Europe Flood were associated with flow reversal, as long-duration ascent (dynamics) occurred in the presence of anomalously warm and moist air (thermodynamics). The EPI can detect this potent combination of ingredients and offers advantages over model precipitation forecasts because it relies on mass fields instead of parameterizations.

This presentation will investigate the EPI from a historical predictability standpoint, with a primary goal of improving medium-range (3–10-day) QPF. EPI predictive skill for historical extreme precipitation events associated with flow reversal will be evaluated using the NCEP Global Ensemble Forecast System (GEFS) second-generation Reforecasts. It will be determined whether the three wettest GEFS members exhibit higher predictive skill than the three driest members, relative to an EPI reanalysis climatology. If higher EPI predictive skill is preferentially associated with the wettest GEFS Reforecast members, then the EPI can provide value to probabilistic human forecasts regarding the likelihood and locations of extreme precipitation. By later incorporating the EPI into operational deterministic and ensemble NWP models, it may improve human-produced QPF accuracy and flood alert lead times for events associated with flow reversal.