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J. A. Ernst

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Hans J. Lugt and Ernst W. Schwiderski

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Local boundary layer approximations of first order are computed for the steady laminar convection flow of a semi-infinite medium, which is produced by nonuniform axisymmetric heating or cooling of an infinite horizontal surface. Velocity, pressure, and temperature profiles as well as Nusselt numbers are presented for the Prandtl numbers 0.7 and 7, and for various Grashof and Eckert numbers. Critical Grashof numbers for a heated surface exist beyond which the flow field becomes unstable. A cooled surface always yields stable motions.

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O. H. DANIEL, H. W. BRANDLI, and J. ERNST

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Makenzie J. Krocak, Joseph T. Ripberger, Sean Ernst, Carol L. Silva, and Hank C. Jenkins-Smith

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While previous work has shown the Storm Prediction Center (SPC) convective outlooks accurately capture meteorological outcomes, evidence suggests stakeholders and the public may misinterpret the categorical words currently used in the product. This work attempts to address this problem by investigating public reactions to alternative information formats that include numeric information: (1) numeric risk levels (i.e., “Level 2 of 5”) and (2) numeric probabilities (i.e., “a 5% chance”). In addition, it explores how different combinations of the categorical labels with numeric information may impact public reactions to the product. Survey data comes from the 2020 Severe Weather and Society Survey, a nationally representative survey of US adults. Participants were shown varying combinations of the information formats of interest, and then rated their concern about the weather and the likelihood of changing plans in response to the given information. Results indicate that providing numeric information (in the form of levels or probabilities) increases the likelihood of participants correctly interpreting the convective outlook information relative to categorical labels alone. Including the categorical labels increases misinterpretation, regardless of whether numeric information was included alongside the labels. Finally, findings indicate participants’ numeracy (or their ability to understand and work with numbers) had an impact on correct interpretation of the order of the outlook labels. Although there are many challenges to correctly interpreting the SPC convective outlook, using only numeric labels instead of the current categorical labels may be a relatively straightforward change that could improve public interpretation of the product.

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Sean Ernst, Joe Ripberger, Makenzie J. Krocak, Hank Jenkins-Smith, and Carol Silva

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Although severe weather forecast products, such as the Storm Prediction Center (SPC) convective outlook, are much more accurate than climatology at day-to-week time scales, tornadoes and severe thunderstorms claim dozens of lives and cause billions of dollars in damage every year. While the accuracy of this outlook has been well documented, less work has been done to explore the comprehension of the product by nonexpert users like the general public. This study seeks to fill this key knowledge gap by collecting data from a representative survey of U.S. adults in the lower 48 states about their use and interpretation of the SPC convective outlook. Participants in this study were asked to rank the words and colors used in the outlook from least to greatest risk, and their answers were compared through visualizations and statistical tests across multiple demographics. Results show that the U.S. public ranks the outlook colors similarly to their ordering in the outlook but switches the positions of several of the outlook words as compared to the operational product. Logistic regression models also reveal that more numerate individuals more correctly rank the SPC outlook words and colors. These findings suggest that the words used in the convective outlook may confuse nonexpert users, and that future work should continue to use input from public surveys to test potential improvements in the choice of outlook words. Using more easily understood words may help to increase the outlook’s decision support value and potentially reduce the harm caused by severe weather events.

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