Can we predict the predictability of high-impact weather events?

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.

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Related Dataset #1 : NCEP/EMC 4KM Gridded Data (GRIB) Stage IV Data. Version 1.0

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Author Coleman, Austin
Ancell, B.
Schwartz, Craig S.
Publisher UCAR/NCAR - Library
Publication Date 2024-11-01T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-10T19:57:23.008494
Metadata Record Identifier edu.ucar.opensky::articles:42450
Metadata Language eng; USA
Suggested Citation Coleman, Austin, Ancell, B., Schwartz, Craig S.. (2024). Can we predict the predictability of high-impact weather events?. UCAR/NCAR - Library. https://n2t.net/ark:/85065/d7b280m1. Accessed 02 August 2025.

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