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Exploring the differences in SPC convective outlook interpretation using categorical and numeric information.
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2022
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Source: Weather and Forecasting, 37(2), 303-311
[PDF-1.96 MB]
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Journal Title:Weather and Forecasting
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NOAA Program & Office:
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Description:While previous work has shown that 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 the following 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 U.S. 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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Source:Weather and Forecasting, 37(2), 303-311
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