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Abstract
This study proposes the concept of quiet weather communication and offers the first framework of quiet weather communication strategies tied to specific public outcomes (e.g., build and maintain organization–public relationships). Most of the risk communication literature focuses on severe weather communication. We posit that through defining and examining quiet weather strategic communication we can better understand how the weather enterprise can prepare communities for future severe weather. Through four virtual focus groups with 28 NWS and broadcast meteorologists, we operationalize quiet weather communication strategies (humanize the organization, provide weather education, share the love of blue skies, and showcase quiet weather trends). We then report meteorologists’ perceptions of the strengths and weaknesses of each strategy and propose future directions for research on quiet weather communication.
Abstract
This study proposes the concept of quiet weather communication and offers the first framework of quiet weather communication strategies tied to specific public outcomes (e.g., build and maintain organization–public relationships). Most of the risk communication literature focuses on severe weather communication. We posit that through defining and examining quiet weather strategic communication we can better understand how the weather enterprise can prepare communities for future severe weather. Through four virtual focus groups with 28 NWS and broadcast meteorologists, we operationalize quiet weather communication strategies (humanize the organization, provide weather education, share the love of blue skies, and showcase quiet weather trends). We then report meteorologists’ perceptions of the strengths and weaknesses of each strategy and propose future directions for research on quiet weather communication.
Abstract
Weather index insurance (WII) has been a promising innovation that protects smallholder farmers against drought risks and provides resilience against adverse rainfall conditions. However, the uptake of WII has been hampered by high spatial and intraseasonal basis risk. To minimize intraseasonal basis risk, the standard approaches to designing WII based on seasonal cumulative rainfall have been shown to be ineffective in some cases because they do not incorporate different water requirements across each phenological stage of crop growth. One of the challenges in incorporating crop phenology in insurance design is to determine the water requirement in crop growth stages. Borrowing from agronomy, crop science, and agrometeorology, we adopt evapotranspiration methods in determining water requirements for a crop to survive in each stage that can be used as a trigger level for a WII product. Using daily rainfall and evapotranspiration data, we illustrate the use of Monte Carlo risk modeling to price an operational WII and WII-linked credit product. The risk modeling approach that we develop includes incorporation of correlation between rainfall and evapotranspiration indices that can minimize significant intertemporal basis risk in WII.
Abstract
Weather index insurance (WII) has been a promising innovation that protects smallholder farmers against drought risks and provides resilience against adverse rainfall conditions. However, the uptake of WII has been hampered by high spatial and intraseasonal basis risk. To minimize intraseasonal basis risk, the standard approaches to designing WII based on seasonal cumulative rainfall have been shown to be ineffective in some cases because they do not incorporate different water requirements across each phenological stage of crop growth. One of the challenges in incorporating crop phenology in insurance design is to determine the water requirement in crop growth stages. Borrowing from agronomy, crop science, and agrometeorology, we adopt evapotranspiration methods in determining water requirements for a crop to survive in each stage that can be used as a trigger level for a WII product. Using daily rainfall and evapotranspiration data, we illustrate the use of Monte Carlo risk modeling to price an operational WII and WII-linked credit product. The risk modeling approach that we develop includes incorporation of correlation between rainfall and evapotranspiration indices that can minimize significant intertemporal basis risk in WII.
Abstract
Ocean State Forecasts contribute to safe and sustainable fishing in India, but their usage among artisanal fishers is often limited. Our research in Thiruvananthapuram district in the southern Indian state of Kerala tested forecast quality and value and how fishers engage with forecasts. In two fishing villages, we verified forecast accuracy, skill, and reliability by comparing forecasts with observations during the 2018 monsoon season (June–September; n = 122). We assessed forecast value by analyzing fishers’ perceptions of weather and risks and the way they used forecasts based on 8 focus group discussions, 20 interviews, conversations, and logs of 10 fishing boats. We find that while forecasts are mostly accurate, inadequate forecasting of unusual events (e.g., wind >45 km h−1) and frequent fishing restrictions (n = 32) undermine their value. Fishers seek more localized and detailed forecasts, but they do not always use them. Weather forecasts are just one of the tools artisanal fishers deploy, used not simply to decide as to whether to go to sea but also to manage potential risks, allowing them to prepare for fishing under hazardous conditions. Their decisions are also based on the availability of fish and their economic needs. From our findings, we suggest that political, economic, and social marginality of south Indian fishers influences their perceptions and responses to weather-related risks. Therefore, improving forecast usage requires not only better forecast skill and wide dissemination of tailor-made weather information, but also better appreciation of risk cultures and the livelihood imperatives of artisanal fishing communities.
Abstract
Ocean State Forecasts contribute to safe and sustainable fishing in India, but their usage among artisanal fishers is often limited. Our research in Thiruvananthapuram district in the southern Indian state of Kerala tested forecast quality and value and how fishers engage with forecasts. In two fishing villages, we verified forecast accuracy, skill, and reliability by comparing forecasts with observations during the 2018 monsoon season (June–September; n = 122). We assessed forecast value by analyzing fishers’ perceptions of weather and risks and the way they used forecasts based on 8 focus group discussions, 20 interviews, conversations, and logs of 10 fishing boats. We find that while forecasts are mostly accurate, inadequate forecasting of unusual events (e.g., wind >45 km h−1) and frequent fishing restrictions (n = 32) undermine their value. Fishers seek more localized and detailed forecasts, but they do not always use them. Weather forecasts are just one of the tools artisanal fishers deploy, used not simply to decide as to whether to go to sea but also to manage potential risks, allowing them to prepare for fishing under hazardous conditions. Their decisions are also based on the availability of fish and their economic needs. From our findings, we suggest that political, economic, and social marginality of south Indian fishers influences their perceptions and responses to weather-related risks. Therefore, improving forecast usage requires not only better forecast skill and wide dissemination of tailor-made weather information, but also better appreciation of risk cultures and the livelihood imperatives of artisanal fishing communities.
Abstract
Tornadoes account for the third highest average annual weather-related fatality rate in the United States. Here, tornado fatalities are examined as rates within the context of multiple physical and social factors using tornado-level information including population and housing units within killer tornado damage paths. Fatality rates are further evaluated across annual, monthly, and diurnal categories as well as between fatality locations and across age and sex categories. The geographic distribution of fatalities is then given by season, time of day, and residential structures. Results can be used by emergency managers, meteorologists, and planners to better prepare for high-impact (i.e., fatality) events and used by researchers as quantitative evidence to further investigate the relationship between tornadoes, climate, and society.
Abstract
Tornadoes account for the third highest average annual weather-related fatality rate in the United States. Here, tornado fatalities are examined as rates within the context of multiple physical and social factors using tornado-level information including population and housing units within killer tornado damage paths. Fatality rates are further evaluated across annual, monthly, and diurnal categories as well as between fatality locations and across age and sex categories. The geographic distribution of fatalities is then given by season, time of day, and residential structures. Results can be used by emergency managers, meteorologists, and planners to better prepare for high-impact (i.e., fatality) events and used by researchers as quantitative evidence to further investigate the relationship between tornadoes, climate, and society.
Abstract
Through the years, several papers have criticized climate policy decision-making for being naïve with respect to how it views climate model outputs as objective facts and uses the outputs directly to program policies. From this and similar observations, many of the papers conclude that there is a need for shifting to a new approach on how climate policy makers may relate to climate change uncertainties. The article proposes such a shift by presenting a road map on how to address uncertainties in climate change adaptation. It consists of three major elements: first, to accept that in many cases we will not be able to reduce climate change uncertainties; second, to diversify the way in which we describe climate change uncertainties, moving from a one-dimensional technical perspective to a multidimensional perspective that applies uncertainties also to social and political processes and systems; and third, to change the way we address climate change uncertainties by moving from a predict-then-act to a reflect-then-act approach, implying that we must adapt to climate change even under high and varied forms of uncertainties. Embedded in this last point is to accept that, unlike for climate change mitigation, the precautionary principle will apply in many situations of climate change adaptation. In the last part of the article the usability of the proposed road map is demonstrated post ante on four Norwegian cases of climate-related natural hazard events.
Significance Statement
The article sums up the international policy and scientific climate change uncertainty discourses and presents a road map on how to improve the way uncertainties are addressed in local efforts of adapting to climate change. The underlying logic of the proposed road map is to expand from the so far prevailing logic of adapting only when uncertainties are low with an additional logic that is applicable in situations with high uncertainties. The road map consists of seven questions to be addressed and seven alternative adaptation options to follow. A selection of recent practical cases on local climate change adaptation efforts in Norway is then presented to demonstrate the applicability of the proposed road map.
Abstract
Through the years, several papers have criticized climate policy decision-making for being naïve with respect to how it views climate model outputs as objective facts and uses the outputs directly to program policies. From this and similar observations, many of the papers conclude that there is a need for shifting to a new approach on how climate policy makers may relate to climate change uncertainties. The article proposes such a shift by presenting a road map on how to address uncertainties in climate change adaptation. It consists of three major elements: first, to accept that in many cases we will not be able to reduce climate change uncertainties; second, to diversify the way in which we describe climate change uncertainties, moving from a one-dimensional technical perspective to a multidimensional perspective that applies uncertainties also to social and political processes and systems; and third, to change the way we address climate change uncertainties by moving from a predict-then-act to a reflect-then-act approach, implying that we must adapt to climate change even under high and varied forms of uncertainties. Embedded in this last point is to accept that, unlike for climate change mitigation, the precautionary principle will apply in many situations of climate change adaptation. In the last part of the article the usability of the proposed road map is demonstrated post ante on four Norwegian cases of climate-related natural hazard events.
Significance Statement
The article sums up the international policy and scientific climate change uncertainty discourses and presents a road map on how to improve the way uncertainties are addressed in local efforts of adapting to climate change. The underlying logic of the proposed road map is to expand from the so far prevailing logic of adapting only when uncertainties are low with an additional logic that is applicable in situations with high uncertainties. The road map consists of seven questions to be addressed and seven alternative adaptation options to follow. A selection of recent practical cases on local climate change adaptation efforts in Norway is then presented to demonstrate the applicability of the proposed road map.
Abstract
Climate adaptation is context specific, and inclusion of diverse forms of knowledge is crucial for developing resilient social–ecological systems. Emphasis on local inclusion is increasing, yet participatory approaches often fall short of facilitating meaningful engagement of diverse forms of knowledge. A central challenge is the lack of a comprehensive and comparative understanding of the social–ecological knowledge that various stakeholders use to inform adaptation decisions. We employed cultural consensus analysis to quantitatively measure and compare social–ecological knowledge within and across three stakeholder groups: government employees, researchers, and local residents in rural coastal Maryland. The results show that 1) local residents placed more emphasis on addressing socioeconomic and cultural changes than researchers and government employees, and 2) that the greatest variation in social–ecological knowledge was found among local residents. These insights yielded by cultural consensus analysis are beneficial for facilitating more inclusive adaptation planning for resilient social–ecological systems.
Abstract
Climate adaptation is context specific, and inclusion of diverse forms of knowledge is crucial for developing resilient social–ecological systems. Emphasis on local inclusion is increasing, yet participatory approaches often fall short of facilitating meaningful engagement of diverse forms of knowledge. A central challenge is the lack of a comprehensive and comparative understanding of the social–ecological knowledge that various stakeholders use to inform adaptation decisions. We employed cultural consensus analysis to quantitatively measure and compare social–ecological knowledge within and across three stakeholder groups: government employees, researchers, and local residents in rural coastal Maryland. The results show that 1) local residents placed more emphasis on addressing socioeconomic and cultural changes than researchers and government employees, and 2) that the greatest variation in social–ecological knowledge was found among local residents. These insights yielded by cultural consensus analysis are beneficial for facilitating more inclusive adaptation planning for resilient social–ecological systems.
Abstract
Increased cooperation of an interdisciplinary group of climate change professionals as a social network can play a crucial role in adaptation to climate change. To investigate this relationship at the country scale, this study uses a case study in Iran to 1) measure the cooperative relationship among climate change professionals using the network analysis approach and 2) analyze the potential of the network in promoting adaptation measures based on sustainable development. Social network analysis, which is both a quantitative and qualitative method of grounded theory, was used to analyze the data. Data collection was performed using two questionnaires, including network analysis and a survey, as well as a number of semistructured interviews with the climate change professionals. The data were collected from climate change professionals, including a sample of 55 individuals who were surveyed as a cross section of representative participants from a variety of sectors and organizations. The network relationship results have been analyzed using different tests at three levels (micro, macro, and the interactions between the two). The results have shown that the connectedness of the network is 23.7%, with 42.4% mutual links. The transitivity rate in the network is 51.39%, which determines the possibility of each professional communicating with a third party. According to the normalized degree index, 34.29% of the cases are in contact with other researchers in the network and 53.15% received a connection from others. Grounded theory analysis showed that five core categories including social capital, managerial factors, research, relations, and coordination affected the quality and utility of Iranian climate change professionals’ network.
Abstract
Increased cooperation of an interdisciplinary group of climate change professionals as a social network can play a crucial role in adaptation to climate change. To investigate this relationship at the country scale, this study uses a case study in Iran to 1) measure the cooperative relationship among climate change professionals using the network analysis approach and 2) analyze the potential of the network in promoting adaptation measures based on sustainable development. Social network analysis, which is both a quantitative and qualitative method of grounded theory, was used to analyze the data. Data collection was performed using two questionnaires, including network analysis and a survey, as well as a number of semistructured interviews with the climate change professionals. The data were collected from climate change professionals, including a sample of 55 individuals who were surveyed as a cross section of representative participants from a variety of sectors and organizations. The network relationship results have been analyzed using different tests at three levels (micro, macro, and the interactions between the two). The results have shown that the connectedness of the network is 23.7%, with 42.4% mutual links. The transitivity rate in the network is 51.39%, which determines the possibility of each professional communicating with a third party. According to the normalized degree index, 34.29% of the cases are in contact with other researchers in the network and 53.15% received a connection from others. Grounded theory analysis showed that five core categories including social capital, managerial factors, research, relations, and coordination affected the quality and utility of Iranian climate change professionals’ network.
Abstract
Weather icons are some of the most frequently used visual tools that meteorologists employ to communicate weather information. Previous research has shown a tendency for the public to make inferences about weather forecast information on the basis of the icon shown. For example, people may infer a higher likelihood of precipitation, assume a higher intensity of precipitation, or determine the duration of expected precipitation if the weather icon appears to show heavy rain. It is unknown to what extent these inferences align with what the meteorologist who chose the icon intended to convey. However, previous studies have used simulated weather icons rather than ones currently in use. The goal of our study was to explore how members of the public interpret actual weather icons they see on television or in mobile applications. An online survey distributed by broadcast meteorologists through social media was used to collect 6253 responses between August and September of 2020. Eleven weather icons currently used by broadcast meteorologists were included in the study. We also tested eight common weather phrases and asked people whether they thought the icons were good illustrators of those phrases. In addition, people were asked to assign a probability of precipitation to the icons. The findings of our study offer new and unique insights that will improve the communication of weather information by giving meteorologists information about how their audiences interpret weather icons.
Significance Statement
Millions of people are shown weather icons during daily weather broadcasts. This study used two approaches to determine whether these icons are effective elements of weather messaging. For the first approach, we showed people an icon alongside a common weather phrase and had them tell us whether the icon was a good illustrator of the weather phrase. The second approach involved showing people an icon and having them assign a probability of precipitation to it. Across eight weather phrases, none of the icons were thought to be good illustrators, but bad illustrators were clear. These results can be used to improve how icons are used as tools to communicate weather forecasts.
Abstract
Weather icons are some of the most frequently used visual tools that meteorologists employ to communicate weather information. Previous research has shown a tendency for the public to make inferences about weather forecast information on the basis of the icon shown. For example, people may infer a higher likelihood of precipitation, assume a higher intensity of precipitation, or determine the duration of expected precipitation if the weather icon appears to show heavy rain. It is unknown to what extent these inferences align with what the meteorologist who chose the icon intended to convey. However, previous studies have used simulated weather icons rather than ones currently in use. The goal of our study was to explore how members of the public interpret actual weather icons they see on television or in mobile applications. An online survey distributed by broadcast meteorologists through social media was used to collect 6253 responses between August and September of 2020. Eleven weather icons currently used by broadcast meteorologists were included in the study. We also tested eight common weather phrases and asked people whether they thought the icons were good illustrators of those phrases. In addition, people were asked to assign a probability of precipitation to the icons. The findings of our study offer new and unique insights that will improve the communication of weather information by giving meteorologists information about how their audiences interpret weather icons.
Significance Statement
Millions of people are shown weather icons during daily weather broadcasts. This study used two approaches to determine whether these icons are effective elements of weather messaging. For the first approach, we showed people an icon alongside a common weather phrase and had them tell us whether the icon was a good illustrator of the weather phrase. The second approach involved showing people an icon and having them assign a probability of precipitation to it. Across eight weather phrases, none of the icons were thought to be good illustrators, but bad illustrators were clear. These results can be used to improve how icons are used as tools to communicate weather forecasts.
Abstract
Human behaviors are believed to be sensitive to environmental conditions. However, little is known about the role of temperature in individual daily behaviors. We examine the links between temperature and food intake using nearly one million purchasing records from China. The results show that a 1°C increase in temperature would cause a 0.11% decrease in food intake, which amounts to USD 4.2 million of daily food expenditures nationwide. Moreover, females appear to be more sensitive to the temperature in their food intake than males. In addition, we observe a U-shaped relationship between the temperature and the willingness to order a takeout online, and this observation is robust under multiple alternative estimations. Our results indicate that a higher temperature would reduce energy demand for body thermoregulation, resulting in less food intake. Both extreme high and low temperatures can cause disutility. Therefore, the consumers who still want to satisfy their needs for food intake feel compelled to alter their willingness to pay under the extreme temperature events. The quantitative analysis can provide helpful references for modeling the climate–consumer relationship in integrated assessment models. Thus, it is an interesting avenue for future research to bridge the climate and consumers to identify welfare loss and inequality due to climate change.
Abstract
Human behaviors are believed to be sensitive to environmental conditions. However, little is known about the role of temperature in individual daily behaviors. We examine the links between temperature and food intake using nearly one million purchasing records from China. The results show that a 1°C increase in temperature would cause a 0.11% decrease in food intake, which amounts to USD 4.2 million of daily food expenditures nationwide. Moreover, females appear to be more sensitive to the temperature in their food intake than males. In addition, we observe a U-shaped relationship between the temperature and the willingness to order a takeout online, and this observation is robust under multiple alternative estimations. Our results indicate that a higher temperature would reduce energy demand for body thermoregulation, resulting in less food intake. Both extreme high and low temperatures can cause disutility. Therefore, the consumers who still want to satisfy their needs for food intake feel compelled to alter their willingness to pay under the extreme temperature events. The quantitative analysis can provide helpful references for modeling the climate–consumer relationship in integrated assessment models. Thus, it is an interesting avenue for future research to bridge the climate and consumers to identify welfare loss and inequality due to climate change.
Abstract
Convection-allowing model ensemble guidance, such as that provided by the Warn-on-Forecast System (WoFS), is designed to provide predictions of individual thunderstorm hazards within the next 0–6 h. The WoFS web viewer provides a large suite of storm and environmental attribute products, but the applicability of these products to the National Weather Service forecast process has not been objectively documented. Therefore, this study describes an experimental forecasting task designed to investigate what WoFS products forecasters accessed and how they accessed them for a total of 26 cases (comprising 13 weather events, each worked by two forecasters). Analysis of web access log data revealed that, in all 26 cases, product accesses were dominated in the reflectivity, rotation, hail, and surface wind categories. However, the number of different product types viewed and the number of transitions between products varied in each case. Therefore, the Levenshtein (edit distance) method was used to compute similarity scores across all 26 cases, which helped to identify what it meant for relatively similar versus dissimilar navigation of WoFS products. The Spearman’s rank correlation coefficient R results found that forecasters working the same weather event had higher similarity scores for events that produced more tornado reports and for events in which forecasters had higher performance scores. The findings from this study will influence subsequent efforts for further improving WoFS products and developing an efficient and effective user interface for operational applications.
Abstract
Convection-allowing model ensemble guidance, such as that provided by the Warn-on-Forecast System (WoFS), is designed to provide predictions of individual thunderstorm hazards within the next 0–6 h. The WoFS web viewer provides a large suite of storm and environmental attribute products, but the applicability of these products to the National Weather Service forecast process has not been objectively documented. Therefore, this study describes an experimental forecasting task designed to investigate what WoFS products forecasters accessed and how they accessed them for a total of 26 cases (comprising 13 weather events, each worked by two forecasters). Analysis of web access log data revealed that, in all 26 cases, product accesses were dominated in the reflectivity, rotation, hail, and surface wind categories. However, the number of different product types viewed and the number of transitions between products varied in each case. Therefore, the Levenshtein (edit distance) method was used to compute similarity scores across all 26 cases, which helped to identify what it meant for relatively similar versus dissimilar navigation of WoFS products. The Spearman’s rank correlation coefficient R results found that forecasters working the same weather event had higher similarity scores for events that produced more tornado reports and for events in which forecasters had higher performance scores. The findings from this study will influence subsequent efforts for further improving WoFS products and developing an efficient and effective user interface for operational applications.
