Can We Predict the Predictability of High-Impact Weather Events?

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Abstract Ensemble sensitivity analysis (ESA) offers a computationally inexpensive way to diagnose sources of high-impact forecast feature uncertainty by relating a localized forecast phenomenon of interest (response function) back to early or initial forecast conditions (sensitivity variables). These information-rich diagnostic fields allow us to quantify the predictability characteristics of a specific forecast event. This work harnesses insights from a month-long dataset of ESA applied to convection-allowing model precipitation forecasts in the Central Plains of the United States. Temporally averaged and spatially averaged sensitivity statistics are correlated with a variety of other metrics, such as skill, spread, and mean forecast precipitation accumulation. A high, but imperfect, correlation (0.81) between forecast precipitation and sensitivity is discovered. This quantity confirms the qualitatively known notion that while there is a connection between predictability and event magnitude, a high-end event does not necessarily entail a low-predictability (high-sensitivity) forecast. Flow regimes within this dataset are analyzed to see which patterns lend themselves to high- and low-predictability forecast scenarios. Finally, a novel metric known as the error growth realization (EGR) ratio is introduced. Derived by dividing the two mathematical formulations of ESA, this metric shows preliminary promise as a predictor of forecast skill prior to the onset of a high-impact convective event. In essence, this research exemplifies the potential of ESA beyond its traditional use in case studies. By applying ESA to a broader dataset, we can glean valuable insight into the predictability of high-impact weather events and, in turn, work toward a collective baseline on what constitutes a high- or low-predictability event in the first place. Significance Statement Toward a climatological understanding of ensemble sensitivity analysis, we quantified the predictability of convection-allowing ensemble precipitation forecasts over a month-long dataset through ensemble sensitivity metrics. The analysis of flow regimes associated with high- and low-predictability scenarios revealed key forecast characteristics influencing predictability. The two formulations of sensitivity are spatially averaged and divided to create a novel metric known as the error growth realization ratio. This ratio shows potential as a predictor of forecast skill prior to the onset of a forecast precipitation event. These findings highlight the promise provided by advanced applications of ensemble sensitivity analysis beyond traditional case studies and work toward establishing a baseline of what delineates a high- or low-predictability event.