The Vice and Virtue of Increased Horizontal Resolution in Ensemble Forecasts of Tornadic Thunderstorms in Low-CAPE, High-Shear Environments

John R. Lawson Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Corey K. Potvin Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Patrick S. Skinner Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Anthony E. Reinhart Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Abstract

Tornadoes have Lorenzian predictability horizons O(10) min, and convection-allowing ensemble prediction systems (EPSs) often provide probabilistic guidance of such events to forecasters. Given the O(0.1)-km length scale of tornadoes and O(1)-km scale of mesocyclones, operational models running at horizontal grid spacings (Δx) of 3 km may not capture narrower mesocyclones (typical of the southeastern United States) and certainly do not resolve most tornadoes per se. In any case, it requires O(50) times more computer power to reduce Δx by a factor of 3. Herein, to determine value in such an investment, we compare two EPSs, differing only in Δx (3 vs 1 km), for four low-CAPE, high-shear cases. Verification was grouped as 1) deterministic, traditional methods using pointwise evaluation, 2) a scale-aware probabilistic metric, and 3) a novel method via object identification and information theory. Results suggest 1-km forecasts better detect storms and any associated rapid low- and midlevel rotation, but at the cost of weak–moderate reflectivity forecast skill. The nature of improvement was sensitive to the case, variable, forecast lead time, and magnitude, precluding a straightforward aggregation of results. However, the distribution of object-specific information gain over all cases consistently shows greater average benefit from the 1-km EPS. We also reiterate the importance of verification methodology appropriate for the hazard of interest.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Potvin’s and Reinhart’s current affiliation: NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma.

Potvin’s and Skinner’s current affiliation: School of Meteorology, University of Oklahoma, Norman, Oklahoma.

Corresponding author: John R. Lawson, john@jrl.ac

Abstract

Tornadoes have Lorenzian predictability horizons O(10) min, and convection-allowing ensemble prediction systems (EPSs) often provide probabilistic guidance of such events to forecasters. Given the O(0.1)-km length scale of tornadoes and O(1)-km scale of mesocyclones, operational models running at horizontal grid spacings (Δx) of 3 km may not capture narrower mesocyclones (typical of the southeastern United States) and certainly do not resolve most tornadoes per se. In any case, it requires O(50) times more computer power to reduce Δx by a factor of 3. Herein, to determine value in such an investment, we compare two EPSs, differing only in Δx (3 vs 1 km), for four low-CAPE, high-shear cases. Verification was grouped as 1) deterministic, traditional methods using pointwise evaluation, 2) a scale-aware probabilistic metric, and 3) a novel method via object identification and information theory. Results suggest 1-km forecasts better detect storms and any associated rapid low- and midlevel rotation, but at the cost of weak–moderate reflectivity forecast skill. The nature of improvement was sensitive to the case, variable, forecast lead time, and magnitude, precluding a straightforward aggregation of results. However, the distribution of object-specific information gain over all cases consistently shows greater average benefit from the 1-km EPS. We also reiterate the importance of verification methodology appropriate for the hazard of interest.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Potvin’s and Reinhart’s current affiliation: NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma.

Potvin’s and Skinner’s current affiliation: School of Meteorology, University of Oklahoma, Norman, Oklahoma.

Corresponding author: John R. Lawson, john@jrl.ac
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