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Ernest Agee and Lindsey Taylor

Abstract

The record of tornado fatalities in the United States for over two centuries (1808–2017) and decadal census records have been examined to search for historical trends. Particular attention has been given to the response to population growth and expansion into the tornado-prone regions of the country. The region selected includes the Tornado Alley of the central Great Plains, the Dixie Alley in the southeastern states, and the adjoining states in the Midwest that collectively encompass a 21-state rectangular region. The data record has been divided into two subintervals, Era A (1808–1915) and Era B (1916–2017), each of which consists of three equal-length periods. Era A is characterized by a growing and westward expanding population along with a basic absence of scientific knowledge, technology, and communications (for prediction, detection, and warning). This is followed by a renaissance of discovery and advancement in Era B that contributes to saving lives. The aforementioned periods are defined by a set of notable events that help to define the respective periods. A death per population index (DPI) is used to evaluate the 21 states in each era; there is a rise of mean DPI values to a maximum of 1.50 at the end of Era A and a subsequent fall to 0.21 at the end of Era B. It is also shown for all three periods in Era B that the deadliest tornado states, in ranked order, are Arkansas, Mississippi, Alabama, and Oklahoma. Suggestions are presented for ways to continue the decreasing trend in DPI, which would imply that the death rate increase is not as fast as the rate of population increase (or would even imply a decreasing death rate).

Open access
Mengqi Ye, Jidong Wu, Cailin Wang, and Xin He

Abstract

Tropical cyclones (TCs) can wreak havoc on the landscape and overwhelm communities. Since economic exposure is an important factor in damage function, an evaluation of economic exposure is essential because the characteristics of TC-related hazards are changing under accelerating economic development patterns. Here, we first reconstructed the wind and rainfall fields of historical TCs through an extensive database to extract the economic exposure to TC-prone areas on the mainland of China. We found that rainfall is an important factor in determining the affected extent of a TC event and that economic exposure will be misestimated when considering only the wind field. The results reveal that economic exposure to TCs has increased considerably from 1990 to 2015 and will continue to increase until the year 2100 under shared socioeconomic pathways (SSPs). We found that 66.7% of China’s gross domestic product [GDP; CNY 48.6 trillion (7.8 trillion U.S. dollars)] and 63.9% of China’s asset value [CNY 139.5 trillion (22.4 trillion U.S. dollars)] were concentrated in TC-prone areas in 2015 and increased at an average annual rate of 10.6% and 13.9%, respectively. Projections of GDP scenarios under SSPs revealed continued growth in the early twenty-first century, and the range of GDP and asset value in TC-prone areas by 2100 varied. Further detailed studies are needed to provide a detailed damage function for TC loss assessments under climate change and to consider how TC hazards will interact under changes in exposure and vulnerability related to economic development and social change.

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Allison Engblom, Kristin Timm, Raphael Mazzone, David Perkins, Teresa Myers, and Edward Maibach

Abstract

Most Americans misperceive climate change as distant risk; TV weathercasters can help correct this misperception by reporting on the current local impacts of climate change. Some weathercasters, however, are concerned that such reporting may alienate skeptical viewers. The goal of this study was to develop a better understanding of how viewers respond to climate change information delivered by weathercasters. Interviews were conducted with 30 local TV news viewers in Virginia with divergent views about climate change, categorized as engaged, disengaged, and unconvinced. During the interview, participants were shown two graphics and two videos about the local impacts of climate change. Most participants in all groups [21/30 (70%)] expressed interest in learning about climate change from weathercasters, particularly local and national impacts. Most participants in all three groups understood the key points and responded positively to both the graphics and the videos. Several unconvinced participants (6/10) were disinterested in seeing climate change information in the weather segment, but they were not opposed to it; they felt the weather segment was too short to adequately explain the information. These preliminary findings suggest that most of the local TV news viewers interviewed in this study—even those unconvinced that human-caused climate change is happening—respond positively to TV weathercasters as local climate educators. These findings are consistent with the reports of TV weathercasters who say that when they report on climate change, they receive far more positive than negative feedback from viewers.

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Katie A. Wilson, Pamela L. Heinselman, Patrick S. Skinner, Jessica J. Choate, and Kim E. Klockow-McClain

Abstract

During the 2017 Spring Forecasting Experiment in NOAA’s Hazardous Weather Testbed, 62 meteorologists completed a survey designed to test their understanding of forecast uncertainty. Survey questions were based on probabilistic forecast guidance provided by the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e). A mix of 20 multiple-choice and open-ended questions required participants to explain basic probability and percentile concepts, extract information using graphical representations of uncertainty, and determine what type of weather scenario the graphics depicted. Multiple-choice questions were analyzed using frequency counts, and open-ended questions were analyzed using thematic coding methods. Of the 18 questions that could be scored, 60%–96% of the participants’ responses aligned with the researchers’ intended response. Some of the most challenging questions proved to be those requiring qualitative explanations, such as to explain what the 70th-percentile value of accumulated rainfall represents in an ensemble-based probabilistic forecast. Additionally, participants providing answers not aligning with the intended response oftentimes appeared to consider the given information with a deterministic rather than probabilistic mindset. Applications of a deterministic mindset resulted in tendencies to focus on the worst-case scenario and to modify understanding of probabilistic concepts when presented with different variables. The findings from this survey support the need for improved basic and applied training for the development, interpretation, and use of probabilistic ensemble forecast guidance. Future work should collect data for a larger sample size to examine the knowledge gaps across specific user groups and to guide development of probabilistic forecast training tools.

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Jonghun Kam, Kimberly Stowers, and Sungyoon Kim

Abstract

This study introduces “Google Trends” as a social data source in monitoring and modeling the dynamics of drought awareness during the 2011–17 California drought. In this study, drought awareness is defined and operationalized as the relative search interest activities within California, using the search term “drought” from Google Trends. First, the 2011–17 California drought is characterized in the duration–intensity curve with other historical California droughts for comparative purposes, using the 12-month standard precipitation index data (1895–2017). Second, the potential triggers of the peaks of drought awareness during the 2011–17 California drought are investigated through Google Trends and Google Search. The Google Trends data show that the first peak of drought awareness occurred when the drought condition reached its peak and the governor declared the drought emergency (January 2014). The other peaks in August 2014, April 2015, and January 2017 are related to public interest in drought recovery driven by the forecast of the strong El Niño winter of 2014/15, the governor’s issue of water use rules, and California floods in early 2017, respectively. Last, a power-law decay model of drought awareness is fitted to the Google Trends data. According to the fitted power-law model, Californians remained interested in drought after the social trigger–related peaks longer than they did after the natural trigger–related peaks. The findings of this study suggest that it is necessary to develop a more realistic social dynamic modeling for communities that can respond to natural and human triggers and capture interactions with awareness of related disasters.

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Jessica Kuonen, Flaxen Conway, and Ted Strub

Abstract

This case study explores how to add value to regional ocean condition forecast information by bringing awareness to the processes that govern decision-making and outcomes within the system. A modified mental models research approach is applied to examine differences and similarities in perceptions of risk and comfort with uncertainty between two interdependent communities, the ocean “data provider” and “end user,” and how these perceptions impact accessibility and usefulness of data products. In this study, data providers are academic and agency scientists from institutions that provide ocean condition forecasts to public end users (n = 17). End users are members of the Oregon commercial-fishing community (n = 16). Comparisons reveal key differences and similarities related to the nature of each profession that impact perceptions of scale in time and space and reveal the ways that cumulative and intersecting risks and uncertainties act as key drivers in decision-making. Implications for expanding the current understanding of how ocean forecasts are produced and used include 1) highlighting the value of optimizing ocean forecast delivery tools based on end-user needs and information-seeking processes already in place, 2) identifying structural and cultural barriers within the data-provider network that prevent them from doing so, and 3) demonstrating the value of learning about both producers and users of scientific information and suggesting potential ways to structure cooperation and strengthen relationships between them by working toward a common desired outcome.

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Steve Rayner

Abstract

In the complex institutional and physical infrastructure nexus of South Australia, weather and climate information is highly valued by freshwater managers and users. But different users focus on very different time scales. Recent changes in water rights and technology, driven by the Millennium Drought, enable agricultural users to focus on real-time monitoring and relatively short-term forecasts (3–5 days ahead). A wide range of users make extensive use of the full 7-day weather forecasts and there is awareness of, but not reliance on, seasonal outlooks. These are widely viewed as providing “background” indications and are seldom directly used in decision-making. While concern about climate change is driving scientific research on downscaling climate impact models for the region, there are different views among decision-makers about the usefulness of these for adaptation. All forms of weather and climate information appear to be best integrated into decision-making when incorporated into sector-specific models and decision-support tools alongside other relevant variables. However, there remains something of a mismatch between scientific aspirations to improve the skill of seasonal and long-term climate forecasting and the temporal rhythms of water-resource decision-making.

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Micah J. Hewer and William A. Gough

Abstract

Because of the perceived weather sensitivity of park visitation in Ontario, Canada, several previous assessments have examined the impact of climate change. However, these assessments have predominantly been based on modeling approaches (regression analysis). The current study uses a multiyear temporal climate-analog approach to reassess the impact of climate change on visitation to Pinery Provincial Park in southwestern Ontario based on the observed effects of historical climatic anomalies on park visitation from 2000 to 2016. Consideration was also given to major events such as the North American terror attacks on 11 September 2001 and the confounding effect that events such as this may have had on the results. There were no statistically significant relationships (at the 95% confidence level) between seasonal climatic anomalies and park visitation in Ontario during the winter or spring seasons. There was a weak statistical relationship between anomalously warm summer seasons and park visitation, when compared to summer seasons with climatically normal temperatures; however, the presence of nonclimatic variables may have confounded these results, producing a false positive. Autumn-season park visitation was most sensitive to climatic anomalies, with the warmest temperatures causing visitation to increase by 37%, the wettest conditions causing visitation to decrease by 11%, and the driest conditions resulting in a 24% increase. These observed seasonal temperature anomalies represent temporal climate analogs for projected climate change across the span of the twenty-first century. Thus, the results of this study suggest that previous assessments may have overestimated the positive impacts of projected climate change on park visitation in this region.

Open access
Xianhua Wu, Zhe Xu, Hui Liu, Ji Guo, and Lei Zhou

Abstract

To investigate the general principle of the impact of tropical cyclones on employment, explore the reason for the divergence among existing research conclusions, and put forward some suggestions for post-disaster reconstruction, this paper employs meta-regression analysis to study the impacts of tropical cyclones on the quantity of labor employed and employee remuneration from four aspects: industry dimension, time dimension, income dimension, and tropical cyclone intensity. The results are as follows: 1) Tropical cyclones create an impact on the intensity of changes in employment remuneration in the primary industry, and the impact in the secondary industry is greater than that in the tertiary industry. 2) In the short term, the impact of tropical cyclones on employment is negative and the impact intensity is strong, whereas in the medium and long terms, the impact is positive and the intensity of impact decreases. 3) Although tropical cyclones increase the quantity of labor employed from low-income groups, they decrease their employment remuneration. In addition, the impact of disasters on the number of employed high-income groups is relatively small compared to that of low-income groups. 4) A higher category of tropical cyclone results in a greater positive impact on the employment of labor force. Accordingly, the following suggestions are made: 1) The government should issue corresponding policies to provide “temporary disaster subsidies” for disaster-stricken low-income groups. 2) Insurance companies should introduce commercial insurance concerning “post-disaster employment” for employers to purchase before any disaster occurs so as to offer disaster-stricken employees compensation.

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Johannes Möllmann, Matthias Buchholz, and Oliver Musshoff

Abstract

Weather derivatives are considered a promising agricultural risk management tool. Station-based meteorological indices typically provide the data underlying these instruments. However, the main shortcoming of these weather derivatives is an imperfect correlation between the weather index and the yield of the insured crop, called basis risk. This paper considers three available remotely sensed vegetation health (VH) indices, namely, the vegetation condition index (VCI), the temperature condition index (TCI), and the vegetation health index (VHI), as indices for weather derivatives in a German case study. We investigated the correlation and period of highest correlation with winter wheat yield. Moreover, we analyzed whether the use of remotely sensed VH indices for weather derivatives can reduce basis risk and thus improve the performance of weather derivatives. The two commonly used meteorological indices, precipitation and temperature sums, were employed as benchmarks. Quantile regression and index value simulation were used for the design and pricing of the weather derivatives. The analysis for the selected farms and corresponding counties in northeastern Germany revealed that, on average, the VHI resulted in the highest correlation with winter wheat yield, and VHI-based weather derivatives were also superior in terms of the hedging effectiveness. The total periods of the highest correlations ranged from the beginning of April to the end of July. VHI- and VCI-based weather derivatives led to statistically significant reductions of basis risk, compared to the benchmarks. Our results indicate that the VHI-based weather derivatives can be useful alternatives to meteorological indices, especially in regions with sparser weather station networks.

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