Search Results

You are looking at 1 - 7 of 7 items for

  • Author or Editor: Hank C. Jenkins-Smith x
  • Refine by Access: All Content x
Clear All Modify Search
Kevin Goebbert, Hank C. Jenkins-Smith, Kim Klockow, Matthew C. Nowlin, and Carol L. Silva

Abstract

This paper analyzes the changes Americans perceive to be taking place in their local weather and tests a series of hypotheses about why they hold these perceptions. Using data from annual nationwide surveys of the American public taken from 2008 to 2011, coupled with geographically specific measures of temperature and precipitation changes over that same period, the authors evaluate the relationship between perceptions of weather changes and actual changes in local weather. In addition, the survey data include measures of individual-level characteristics (age, education level, gender, and income) as well as cultural worldview and political ideology. Rival hypotheses about the origins of Americans’ perceptions of weather change are tested, and it is found that actual weather changes are less predictive of perceived changes in local temperatures, but better predictors of perceived flooding and droughts. Cultural biases and political ideology also shape perceptions of changes in local weather. Overall, the analysis herein indicates that beliefs about changes in local temperatures have been more heavily politicized than is true for beliefs about local precipitation patterns. Therefore, risk communications linking changes in local patterns of precipitation to broader changes in the climate are more likely to penetrate identity-protective cognitions about climate.

Full access
Joseph T. Ripberger, Hank C. Jenkins-Smith, Carol L. Silva, Deven E. Carlson, and Matthew Henderson

Abstract

Effective communication about severe weather requires that providers of weather information disseminate accurate and timely messages and that the intended recipients (i.e., the population at risk) receive and react to these messages. This article contributes to extant research on the second half of this equation by introducing a “real time” measure of public attention to severe weather risk communication based on the growing stream of data that individuals publish on social media platforms, in this case, Twitter. The authors develop a metric that tracks temporal fluctuations in tornado-related Twitter activity between 25 April 2012 and 11 November 2012 and assess the validity of the metric by systematically comparing fluctuations in Twitter activity to the issuance of tornado watches and warnings, which represent basic but important forms of communication designed to elicit, and therefore correlate with, public attention. The assessment finds that the measure demonstrates a high degree of convergent validity, suggesting that social media data can be used to advance our understanding of the relationship between risk communication, attention, and public reactions to severe weather.

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

Abstract

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.

Significance Statement

The SPC convective outlook contains vital information that can help people prepare for a severe weather event. The categorical labels in this product are often ordered incorrectly by members of the public. This work shows using numeric levels or probabilities reduces the tendency for people to order the levels incorrectly.

Restricted access
Makenzie J. Krocak, Jinan N. Allan, Joseph T. Ripberger, Carol L. Silva, and Hank C. Jenkins-Smith

Abstract

Nocturnal tornadoes are challenging to forecast and even more challenging to communicate. Numerous studies have evaluated the forecasting challenges, but fewer have investigated when and where these events pose the greatest communication challenges. This study seeks to evaluate variation in confidence among U.S. residents in receiving and responding to tornado warnings by hour of day. Survey experiment data come from the Severe Weather and Society Survey, an annual survey of U.S. adults. Results indicate that respondents are less confident about receiving warnings overnight, specifically in the early morning hours [from 12:00 AM to 4:00 AM local time (0000–0400 LT)]. We then use the survey results to inform an analysis of hourly tornado climatology data. We evaluate where nocturnal tornadoes are most likely to occur during the time frame when residents are least confident in their ability to receive tornado warnings. Results show that the Southeast experiences the highest number of nocturnal tornadoes during the time period of lowest confidence, as well as the largest proportion of tornadoes in that time frame. Finally, we estimate and assess two multiple linear regression models to identify individual characteristics that may influence a respondent’s confidence in receiving a tornado between 12:00 AM and 4:00 AM. These results indicate that age, race, weather awareness, weather sources, and the proportion of nocturnal tornadoes in the local area relate to warning reception confidence. The results of this study should help inform policymakers and practitioners about the populations at greatest risk for challenges associated with nocturnal tornadoes. Discussion focuses on developing more effective communication strategies, particularly for diverse and vulnerable populations.

Restricted access
Joseph T. Ripberger, Carol L. Silva, Hank C. Jenkins-Smith, and Mark James

Abstract

The Central Region Headquarters of the National Weather Service (NWS) recently launched an experimental product that supplements traditional tornado and severe thunderstorm warning products with information about the potential impact of warned storms. As yet, however, we know relatively little about the influence of consequence-based messages on warning responsiveness. To address this gap, we fielded two surveys of U.S. residents that live in tornado-prone regions of the country. Both surveys contained an experiment wherein participants were randomly assigned a consequence-based tornado warning message and asked to indicate how they would respond if they were to receive such a warning. Respondents that were assigned to higher-impact categories were more likely choose protective action than respondents assigned to lower-impact categories. There was, however, a threshold beyond which escalating the projected consequences of the storm no longer increased the probability of protective action. To account for this, we show that the relationship between consequence-based messages and protective action depends on the type of action being considered. At lower levels of projected impact, increasing the expected consequences of the storm simultaneously increased the probability that respondents selected a “shelter in place” or “leave residence” option. At higher levels of projected impact, this relationship changed—increasing the projected consequences of the storm decreased the probability that respondents would shelter in place and increased the probability that they would leave their residence for what they perceived to be a safer location. In some severe storm situations, this behavior may increase rather than decrease the risks.

Full access
Joseph T. Ripberger, Carol L. Silva, Hank C. Jenkins-Smith, Jinan Allan, Makenzie Krocak, Wesley Wehde, and Sean Ernst
Full access
Joseph T. Ripberger, Carol L. Silva, Hank C. Jenkins-Smith, Jinan Allan, Makenzie Krocak, Wesley Wehde, and Sean Ernst

Abstract

Effective risk communication in the weather enterprise requires deep knowledge about the communities that enterprise members serve. This includes knowledge of the atmospheric and climate conditions in these communities as well as knowledge about the characteristics of the people living in these communities. Enterprise members often have access to data that facilitate the first type of knowledge, but relatively little social or behavioral data on the populations they serve. This article introduces an effort to overcome these challenges by developing a database of community statistics and an interactive platform that provides dynamic access to the database. Specific emphasis is given to one set of statistics in the community database: estimates of tornado warning reception, comprehension, and response by county warning area in the contiguous United States. Exploration of these estimates indicates significant variation in reception and comprehension across communities. This variation broadly aligns with tornado climatology, but there are noticeable differences within climatologically comparable regions that underline the importance of community-specific information. Verification of the estimates using independent observations from a random sample of communities confirms that the estimates are largely accurate, but there are a few consistent anomalies that prompt questions about why some communities exhibit higher or lower levels of reception, comprehension, and response than models suggest. The article concludes with a discussion of next steps and an invitation to use and contribute to the project as it progresses.

Free access