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Abstract
Integration of the social sciences into climate assessments enhances report content and actionable science. The literature has identified the benefits and challenges in achieving coequal intellectual partnerships between the social and biogeophysical sciences in climate research. Less has been written on how to rectify the issue in the particular institutional context of a climate assessment. This article uses qualitative research methods to analyze social science integration in the United States’ Fourth National Climate Assessment. It presents findings from focus groups held with social science– and nonsocial science–trained report authors. It finds that knowledge governance, or the formal and informal mechanisms shaping how information is produced and used, and cultural worldviews about the role of social sciences in assessments and assessments in society, affected social science integration. Report authors’ principal orientation toward the social sciences was as a means of achieving what they saw as the assessment’s public function, namely, to support education, decision-making, and action. Author expertise, report framing, and knowledge systems were other key themes that emerged. Based on this analysis, we propose potential pathways toward coequal intellectual partnerships in assessments by expanding the diversity of chapter teams’ expertise, enhancing connections between authors and society, reconsidering report framing, and broadening inclusion of knowledge systems. We also discuss the potential role of applying social science theories and methods throughout the report life cycle from framing and engagement to evaluation.
Significance Statement
We wanted to understand why the Fourth U.S. National Climate Assessment used the social sciences as it did in order to identify opportunities and obstacles for enhancing social science inclusion. To do so, we held focus groups with assessment authors on their experiences with writing the report. This approach lends insight into the evolving integration of social sciences in climate assessments. Its implications for how to better integrate the social and biogeophysical sciences may be of particular interest to authors and managers of global change assessments and to other readers working on interdisciplinary climate research projects. Future studies could investigate similarities and differences in incorporating the social sciences into global, national, and state-level assessments.
Abstract
Integration of the social sciences into climate assessments enhances report content and actionable science. The literature has identified the benefits and challenges in achieving coequal intellectual partnerships between the social and biogeophysical sciences in climate research. Less has been written on how to rectify the issue in the particular institutional context of a climate assessment. This article uses qualitative research methods to analyze social science integration in the United States’ Fourth National Climate Assessment. It presents findings from focus groups held with social science– and nonsocial science–trained report authors. It finds that knowledge governance, or the formal and informal mechanisms shaping how information is produced and used, and cultural worldviews about the role of social sciences in assessments and assessments in society, affected social science integration. Report authors’ principal orientation toward the social sciences was as a means of achieving what they saw as the assessment’s public function, namely, to support education, decision-making, and action. Author expertise, report framing, and knowledge systems were other key themes that emerged. Based on this analysis, we propose potential pathways toward coequal intellectual partnerships in assessments by expanding the diversity of chapter teams’ expertise, enhancing connections between authors and society, reconsidering report framing, and broadening inclusion of knowledge systems. We also discuss the potential role of applying social science theories and methods throughout the report life cycle from framing and engagement to evaluation.
Significance Statement
We wanted to understand why the Fourth U.S. National Climate Assessment used the social sciences as it did in order to identify opportunities and obstacles for enhancing social science inclusion. To do so, we held focus groups with assessment authors on their experiences with writing the report. This approach lends insight into the evolving integration of social sciences in climate assessments. Its implications for how to better integrate the social and biogeophysical sciences may be of particular interest to authors and managers of global change assessments and to other readers working on interdisciplinary climate research projects. Future studies could investigate similarities and differences in incorporating the social sciences into global, national, and state-level assessments.
Abstract
Tornadoes are responsible for considerable property damage and loss of life across Oklahoma. While several studies have explored drivers of tornado adjustment behaviors, their results are not consistent in terms of their significance and direction. To address this shortcoming in the literature, we surveyed households using a disproportionate stratified sampling procedure from counties in Oklahoma that frequently experience tornado threats to explore drivers of adjustments. We used structural equation modeling (SEM) to explore relationships among variables highlighted in the protection motivation theory (PMT) and related literature that affect adjustment intentions and risk perceptions. Overall, we found that the factors highlighted in the PMT are effective at explaining households’ intentions of adopting adjustment behaviors associated with tornado hazards. Threat appraisals, however, were less important than coping appraisals in explaining tornado hazard adjustment intentions. In further analysis, we grouped adjustments as 1) basic (e.g., flashlight, food supply, and water supply) and 2) complex (e.g., insurance and storm shelter), and we found that while coping appraisals are significant drivers of both adjustment categories, the effect of threat appraisals is only significant for complex adjustment intentions. We also found that emotional responses to hazards are major drivers of threat appraisals, stronger than perceived knowledge and hazard salience. Moreover, we found that demographic characteristics affect both adjustment intentions and threat appraisals. The additions to the PMT and categorization of adjustment activities improve our understanding of the PMT in different contexts. Such insights provide scholars and emergency managers with strategies for risk communication efforts.
Significance Statement
Tornadoes have caused considerable property damage and loss of life across the state of Oklahoma. Here, we utilize the protection motivation theory (PMT) to explore drivers of tornado hazard adjustment intentions by surveying households from counties in Oklahoma that frequently experience tornadoes. Overall, we found that threat appraisals and coping appraisals produce differential effects depending on the type of hazard adjustment in question. Our findings show that risk perceptions are not a significant explanatory variable of basic adjustments (e.g., flashlight, food supply, and water supply) but are a significant explanatory variable of complex adjustments (e.g., insurance and storm shelter). Future work should provide broader perspectives on how to advance the PMT to better explain adjustment intentions for various hazards.
Abstract
Tornadoes are responsible for considerable property damage and loss of life across Oklahoma. While several studies have explored drivers of tornado adjustment behaviors, their results are not consistent in terms of their significance and direction. To address this shortcoming in the literature, we surveyed households using a disproportionate stratified sampling procedure from counties in Oklahoma that frequently experience tornado threats to explore drivers of adjustments. We used structural equation modeling (SEM) to explore relationships among variables highlighted in the protection motivation theory (PMT) and related literature that affect adjustment intentions and risk perceptions. Overall, we found that the factors highlighted in the PMT are effective at explaining households’ intentions of adopting adjustment behaviors associated with tornado hazards. Threat appraisals, however, were less important than coping appraisals in explaining tornado hazard adjustment intentions. In further analysis, we grouped adjustments as 1) basic (e.g., flashlight, food supply, and water supply) and 2) complex (e.g., insurance and storm shelter), and we found that while coping appraisals are significant drivers of both adjustment categories, the effect of threat appraisals is only significant for complex adjustment intentions. We also found that emotional responses to hazards are major drivers of threat appraisals, stronger than perceived knowledge and hazard salience. Moreover, we found that demographic characteristics affect both adjustment intentions and threat appraisals. The additions to the PMT and categorization of adjustment activities improve our understanding of the PMT in different contexts. Such insights provide scholars and emergency managers with strategies for risk communication efforts.
Significance Statement
Tornadoes have caused considerable property damage and loss of life across the state of Oklahoma. Here, we utilize the protection motivation theory (PMT) to explore drivers of tornado hazard adjustment intentions by surveying households from counties in Oklahoma that frequently experience tornadoes. Overall, we found that threat appraisals and coping appraisals produce differential effects depending on the type of hazard adjustment in question. Our findings show that risk perceptions are not a significant explanatory variable of basic adjustments (e.g., flashlight, food supply, and water supply) but are a significant explanatory variable of complex adjustments (e.g., insurance and storm shelter). Future work should provide broader perspectives on how to advance the PMT to better explain adjustment intentions for various hazards.
Abstract
Hazardous weather conditions can pose a threat to the functioning of transportation systems. While the impacts of extreme weather events (e.g., hurricanes/tornadoes and flooding) on transportation disruptions have received significant attention, minor transient disturbances in traffic and transport systems due to rainfall events have remained understudied. Given that a road network experiences rainfall events on a regular basis, which in turn likely reduces its efficiency through short-term disruptions, it is imperative to assess the influence of variations in rainfall intensity on the traffic speed. By synergistically using crowdsourced probe vehicle speed data and spatially explicit meteorological data, this study quantifies the sensitivity of traffic speed to rainfall events of different intensities over 1151 road sections within Alabama. It is observed that instead of variations in the rainfall intensity, traffic speed sensitivity is primarily influenced by a road section’s free-flow speed (uninterrupted speed during dry pavement conditions) and antecedent traffic volume. Relative sensitivity of road sections exhibits high consistency over different rainfall intensities across all road sections, thus underscoring the possibility of assessing sensitivities based only on speed data collected during rainfall intensities that are much more frequent. These results may be used to identify road sections and time periods with high sensitivity to rainfall, thus helping in prioritization of mitigation measures.
Significance Statement
To safeguard against hazardous driving conditions during rainfall events, from either compromised visibility or reduced friction between tires and pavement, drivers often reduce vehicle speed. However, the influence of rainfall intensity on traffic speed reduction remains unclear. This study analyzes the sensitivity of traffic speed to rainfall intensity. Our results indicate that, while rainfall indeed leads to traffic speed reductions, the extent of reduction is predominantly influenced by free-flow speed (uninterrupted vehicle speed) of the road section and the traffic volume on it instead of the rainfall intensity. These results may be used to identify high-sensitivity time periods and locations and guide prioritization of mitigation measures.
Abstract
Hazardous weather conditions can pose a threat to the functioning of transportation systems. While the impacts of extreme weather events (e.g., hurricanes/tornadoes and flooding) on transportation disruptions have received significant attention, minor transient disturbances in traffic and transport systems due to rainfall events have remained understudied. Given that a road network experiences rainfall events on a regular basis, which in turn likely reduces its efficiency through short-term disruptions, it is imperative to assess the influence of variations in rainfall intensity on the traffic speed. By synergistically using crowdsourced probe vehicle speed data and spatially explicit meteorological data, this study quantifies the sensitivity of traffic speed to rainfall events of different intensities over 1151 road sections within Alabama. It is observed that instead of variations in the rainfall intensity, traffic speed sensitivity is primarily influenced by a road section’s free-flow speed (uninterrupted speed during dry pavement conditions) and antecedent traffic volume. Relative sensitivity of road sections exhibits high consistency over different rainfall intensities across all road sections, thus underscoring the possibility of assessing sensitivities based only on speed data collected during rainfall intensities that are much more frequent. These results may be used to identify road sections and time periods with high sensitivity to rainfall, thus helping in prioritization of mitigation measures.
Significance Statement
To safeguard against hazardous driving conditions during rainfall events, from either compromised visibility or reduced friction between tires and pavement, drivers often reduce vehicle speed. However, the influence of rainfall intensity on traffic speed reduction remains unclear. This study analyzes the sensitivity of traffic speed to rainfall intensity. Our results indicate that, while rainfall indeed leads to traffic speed reductions, the extent of reduction is predominantly influenced by free-flow speed (uninterrupted vehicle speed) of the road section and the traffic volume on it instead of the rainfall intensity. These results may be used to identify high-sensitivity time periods and locations and guide prioritization of mitigation measures.
Abstract
This research examines tornadoes and their fatalities by light condition (i.e., daytime and nighttime) for the United States. The study has two primary objectives: 1) to catalog and reassess differences in daytime and nighttime, or nocturnal, tornadoes and their fatalities from spatial and temporal perspectives and 2) to employ a spatially explicit Monte Carlo simulation technique to calculate differences in daytime and nocturnal tornado–population impact potential by combining climatological tornado risk data with fine-scale, gridded estimates of day and night population density. Results reveal that nocturnal tornadoes remain a substantial impediment to mitigating tornado casualties despite long-term improvements in detection and warning of these events. Nocturnal tornadoes are nearly 2 times as deadly as daytime events, with fatalities stemming from overnight (i.e., from local midnight to sunrise) tornadoes increasing fourfold since the late nineteenth century. The proportion of all tornado fatalities that occurred during daytime hours has decreased 20% over the last 140 years while the nocturnal fatality proportion has increased 20%. The stall, or even slight growth, in U.S. tornado mortality rates over the last 30 years has, at least in part, been driven by increasing nocturnal tornado fatalities. Overall, nocturnal tornadoes affect 13% more people on average than daytime tornadoes, revealing the importance of time of day in mitigating tornado–population impacts and disasters. Emergency managers, forecasters, first responders, policy makers, and researchers should continue to focus efforts on understanding nocturnal tornadoes, especially with regard to how populations receive warnings and respond to these nocturnal threats.
Abstract
This research examines tornadoes and their fatalities by light condition (i.e., daytime and nighttime) for the United States. The study has two primary objectives: 1) to catalog and reassess differences in daytime and nighttime, or nocturnal, tornadoes and their fatalities from spatial and temporal perspectives and 2) to employ a spatially explicit Monte Carlo simulation technique to calculate differences in daytime and nocturnal tornado–population impact potential by combining climatological tornado risk data with fine-scale, gridded estimates of day and night population density. Results reveal that nocturnal tornadoes remain a substantial impediment to mitigating tornado casualties despite long-term improvements in detection and warning of these events. Nocturnal tornadoes are nearly 2 times as deadly as daytime events, with fatalities stemming from overnight (i.e., from local midnight to sunrise) tornadoes increasing fourfold since the late nineteenth century. The proportion of all tornado fatalities that occurred during daytime hours has decreased 20% over the last 140 years while the nocturnal fatality proportion has increased 20%. The stall, or even slight growth, in U.S. tornado mortality rates over the last 30 years has, at least in part, been driven by increasing nocturnal tornado fatalities. Overall, nocturnal tornadoes affect 13% more people on average than daytime tornadoes, revealing the importance of time of day in mitigating tornado–population impacts and disasters. Emergency managers, forecasters, first responders, policy makers, and researchers should continue to focus efforts on understanding nocturnal tornadoes, especially with regard to how populations receive warnings and respond to these nocturnal threats.
Abstract
Evidence exists that exposure to weather hazards, particularly in cities subject to heat island and climate change impacts, strongly affects individuals’ physical and mental health. Personal exposure to and sentiments about warm conditions can currently be expressed on social media, and recent research noted that the geotagged, time-stamped, and accessible social media databases can potentially be indicative of the public mood and health for a region. This study attempts to understand the relationships between weather and social media sentiments via Twitter and weather data from 2012 to 2019 for two cities in hot climates: Singapore and Phoenix, Arizona. We first detected weather-related tweets, and subsequently extracted keywords describing weather sensations. Furthermore, we analyzed frequencies of most used words describing weather sensations and created graphs of commonly occurring bigrams to understand connections between them. We further explored the annual trends between keywords describing heat and heat-related thermal discomfort and temperature profiles for two cities. Results showed significant relationships between frequency of heat-related tweets and temperature. For Twitter users exposed to no strong temperature seasonality, we noticed an overall negative cluster around hot sensations. Seasonal variability was more apparent in Phoenix, with more positive weather-related sentiments during the cooler months. This demonstrates the viability of Twitter data as a rapid indicator for periods of higher heat experienced by public and greater negative sentiment toward the weather, and its potential for effective tracking of real-time urban heat stress.
Significance Statement
Social media such as Twitter allow individuals to broadcast their opinions in real time, including perceptions and sensations related to weather events. Evidence from two cities exposed to hot weather—one equatorial and one desert subtropical—indicates that tweets were sensitive to seasonal temperature differences even within a small range. For Twitter users exposed to no strong temperature seasonality, generally negative sentiments to hot weather were seen year-round. In Phoenix with more pronounced seasonality, tweets were more positive in sentiment during the cooler months. This result shows promise for the medium as a rapid real-time indicator—or a snapshot—for societal sentiment to weather events.
Abstract
Evidence exists that exposure to weather hazards, particularly in cities subject to heat island and climate change impacts, strongly affects individuals’ physical and mental health. Personal exposure to and sentiments about warm conditions can currently be expressed on social media, and recent research noted that the geotagged, time-stamped, and accessible social media databases can potentially be indicative of the public mood and health for a region. This study attempts to understand the relationships between weather and social media sentiments via Twitter and weather data from 2012 to 2019 for two cities in hot climates: Singapore and Phoenix, Arizona. We first detected weather-related tweets, and subsequently extracted keywords describing weather sensations. Furthermore, we analyzed frequencies of most used words describing weather sensations and created graphs of commonly occurring bigrams to understand connections between them. We further explored the annual trends between keywords describing heat and heat-related thermal discomfort and temperature profiles for two cities. Results showed significant relationships between frequency of heat-related tweets and temperature. For Twitter users exposed to no strong temperature seasonality, we noticed an overall negative cluster around hot sensations. Seasonal variability was more apparent in Phoenix, with more positive weather-related sentiments during the cooler months. This demonstrates the viability of Twitter data as a rapid indicator for periods of higher heat experienced by public and greater negative sentiment toward the weather, and its potential for effective tracking of real-time urban heat stress.
Significance Statement
Social media such as Twitter allow individuals to broadcast their opinions in real time, including perceptions and sensations related to weather events. Evidence from two cities exposed to hot weather—one equatorial and one desert subtropical—indicates that tweets were sensitive to seasonal temperature differences even within a small range. For Twitter users exposed to no strong temperature seasonality, generally negative sentiments to hot weather were seen year-round. In Phoenix with more pronounced seasonality, tweets were more positive in sentiment during the cooler months. This result shows promise for the medium as a rapid real-time indicator—or a snapshot—for societal sentiment to weather events.
Abstract
Extreme heat events are one of the deadliest weather-related hazards in the United States and are increasing in frequency and severity as a result of anthropogenic greenhouse gas emissions. Further, some subpopulations may be more vulnerable than others because of social, economic, and political factors that create disparities in hazard impacts and responses. Vulnerability is also affected by risk perceptions, which can influence protective behaviors. In this study, we use national survey data to investigate the association of key sociodemographic factors with public risk perceptions of heatwaves. We find that risk perceptions are most associated with income, race/ethnicity, gender, and disability status. Age, an important predictor of heat mortality, had smaller associations with heat risk perceptions. Low-income, nonwhite, and disabled individuals tend to perceive themselves to be at greater risks from heatwaves than other subpopulations, corresponding to their elevated risk. Men have lower risk perceptions than women despite their higher mortality and morbidity from heat. This study helps to identify subpopulations in the United States who see themselves as at risk from extreme heat and can inform heat risk communication and other risk reduction practices.
Abstract
Extreme heat events are one of the deadliest weather-related hazards in the United States and are increasing in frequency and severity as a result of anthropogenic greenhouse gas emissions. Further, some subpopulations may be more vulnerable than others because of social, economic, and political factors that create disparities in hazard impacts and responses. Vulnerability is also affected by risk perceptions, which can influence protective behaviors. In this study, we use national survey data to investigate the association of key sociodemographic factors with public risk perceptions of heatwaves. We find that risk perceptions are most associated with income, race/ethnicity, gender, and disability status. Age, an important predictor of heat mortality, had smaller associations with heat risk perceptions. Low-income, nonwhite, and disabled individuals tend to perceive themselves to be at greater risks from heatwaves than other subpopulations, corresponding to their elevated risk. Men have lower risk perceptions than women despite their higher mortality and morbidity from heat. This study helps to identify subpopulations in the United States who see themselves as at risk from extreme heat and can inform heat risk communication and other risk reduction practices.
Abstract
Although many studies have linked complex social processes with climate change, few have examined the connections between changes in environmental factors, resources, or energy and the evolution of civilizations on the Tibetan Plateau. The Chiefdom of Lijiang was a powerful chiefdom located on the eastern Tibetan Plateau during the Ming Dynasty; it began expanding after the 1460s. Although many studies have analyzed the political and economic motivations responsible for this expansion, no high-resolution climate records representing this period of the Chiefdom of Lijiang were available until now. Here, we obtain a 621-yr reconstruction of the April–July normalized difference vegetation index (NDVI) values derived from moisture-sensitive tree rings from the eastern Tibetan Plateau. Our NDVI reconstruction accounts for 40.4% of the variability in instrumentally measured NDVI values and can effectively represent the historical changes in regional vegetation productivity that occurred on the eastern Tibetan Plateau. In combination with a reconstruction of summer temperatures on the eastern Tibetan Plateau, these results reveal that the regional climate was relatively warm and persistently wet during the period 1466–1630. This period was characterized by long periods of above-mean vegetation productivity on the eastern Tibetan Plateau that coincided with the expansion of the Chiefdom of Lijiang. We therefore propose that the NDVI anomaly and associated favorable political environment may have affected the expansion of the Chiefdom of Lijiang. Instrumental climate data and tree rings also reveal that the early twenty-first-century drought on the eastern Tibetan Plateau was the hottest drought recorded over the past six centuries, in accordance with projections of warming over the Tibetan Plateau. Future climate warming may lead to the occurrence of similar droughts, with potentially severe consequences for modern Asia.
Abstract
Although many studies have linked complex social processes with climate change, few have examined the connections between changes in environmental factors, resources, or energy and the evolution of civilizations on the Tibetan Plateau. The Chiefdom of Lijiang was a powerful chiefdom located on the eastern Tibetan Plateau during the Ming Dynasty; it began expanding after the 1460s. Although many studies have analyzed the political and economic motivations responsible for this expansion, no high-resolution climate records representing this period of the Chiefdom of Lijiang were available until now. Here, we obtain a 621-yr reconstruction of the April–July normalized difference vegetation index (NDVI) values derived from moisture-sensitive tree rings from the eastern Tibetan Plateau. Our NDVI reconstruction accounts for 40.4% of the variability in instrumentally measured NDVI values and can effectively represent the historical changes in regional vegetation productivity that occurred on the eastern Tibetan Plateau. In combination with a reconstruction of summer temperatures on the eastern Tibetan Plateau, these results reveal that the regional climate was relatively warm and persistently wet during the period 1466–1630. This period was characterized by long periods of above-mean vegetation productivity on the eastern Tibetan Plateau that coincided with the expansion of the Chiefdom of Lijiang. We therefore propose that the NDVI anomaly and associated favorable political environment may have affected the expansion of the Chiefdom of Lijiang. Instrumental climate data and tree rings also reveal that the early twenty-first-century drought on the eastern Tibetan Plateau was the hottest drought recorded over the past six centuries, in accordance with projections of warming over the Tibetan Plateau. Future climate warming may lead to the occurrence of similar droughts, with potentially severe consequences for modern Asia.
Abstract
Density altitude (DA) is an aviation parameter that helps determine specific aircraft performance characteristics for the expected atmospheric conditions. However, there are currently no detailed graphical tools for general aviation (GA) pilot education demonstrating the spatial and temporal variation of DA to help improve situational awareness. In this study, the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5) dataset is used to construct a 30-yr monthly climatology of DA for the conterminous United States. Several DA characteristics are also investigated, including the effect of humidity on DA, the determination of reasonable worst-case conditions, and the applicability of two DA rules of thumb (ROTs). Maximum values of DA (worst aircraft performance) occur during July, reaching 3600 m over areas with high surface elevations. Humidity, while tertiary to the effects of temperature and pressure, causes the DA to increase from their dry values by more than 140 m as far north as the U.S.-Canada border. The dry DA ROT performs well for all conditions outside of strong tropical cyclones, where GA flights would not be expected. The ROT to correct for the effects of humidity performs well except in high elevations or when the dewpoint temperatures fall outside the applicable range of ≥5°C. When applied outside this range, in some situations, DA errors can be greater than if no humidity correction were applied. Therefore, a new ROT to correct for humidity is introduced here that extends the applicable dewpoint temperature range to ≥−28°C and reduces errors in estimated DA.
Significance Statement
The impacts of density altitude on aircraft performance have led to numerous general aviation (GA) accidents. This study helps GA pilots better understand the spatial and temporal variability in density altitude, thereby increasing their situational awareness during flight planning. This study also evaluates commonly used approximations to estimate density altitude, so pilots can understand the situations where these approximations are (in)applicable. Results suggest the need for a humidity correction approximation when dewpoint temperatures are <5°C, which is introduced in this study.
Abstract
Density altitude (DA) is an aviation parameter that helps determine specific aircraft performance characteristics for the expected atmospheric conditions. However, there are currently no detailed graphical tools for general aviation (GA) pilot education demonstrating the spatial and temporal variation of DA to help improve situational awareness. In this study, the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5) dataset is used to construct a 30-yr monthly climatology of DA for the conterminous United States. Several DA characteristics are also investigated, including the effect of humidity on DA, the determination of reasonable worst-case conditions, and the applicability of two DA rules of thumb (ROTs). Maximum values of DA (worst aircraft performance) occur during July, reaching 3600 m over areas with high surface elevations. Humidity, while tertiary to the effects of temperature and pressure, causes the DA to increase from their dry values by more than 140 m as far north as the U.S.-Canada border. The dry DA ROT performs well for all conditions outside of strong tropical cyclones, where GA flights would not be expected. The ROT to correct for the effects of humidity performs well except in high elevations or when the dewpoint temperatures fall outside the applicable range of ≥5°C. When applied outside this range, in some situations, DA errors can be greater than if no humidity correction were applied. Therefore, a new ROT to correct for humidity is introduced here that extends the applicable dewpoint temperature range to ≥−28°C and reduces errors in estimated DA.
Significance Statement
The impacts of density altitude on aircraft performance have led to numerous general aviation (GA) accidents. This study helps GA pilots better understand the spatial and temporal variability in density altitude, thereby increasing their situational awareness during flight planning. This study also evaluates commonly used approximations to estimate density altitude, so pilots can understand the situations where these approximations are (in)applicable. Results suggest the need for a humidity correction approximation when dewpoint temperatures are <5°C, which is introduced in this study.
Abstract
The winter season in many U.S. states includes snowfall, and with it comes comments about how drivers always seem to “forget” how to drive in snow when the first snowfall of the season occurs. This study assesses the accuracy of this popular sentiment during Indiana winters from 2007 to 2020. The number of motor vehicle crashes, injuries, and fatalities during the first snowfall of the season was compared with those during subsequent snow events. A grid of 46 cells was constructed to subdivide the state, and instances of snowfall and crashes were aggregated within each cell each day during the study period. Daily crash, injury, and fatality totals in each cell were normalized by their respective means and standard deviations, allowing for data from all cells to be combined into a single dataset. Four snow accumulation thresholds were examined: 1, 13, 25, and 51 mm. Distributions at each threshold show that more crashes occur on average on days with the first snowfall of the winter season than on other days with snowfall, regardless of the accumulation threshold used. Statistical tests support this result, showing significant differences between the mean numbers of crashes at each of the four snowfall thresholds. There were also significantly more injuries on the first snowfall day and more fatalities, although fatalities were only significant for the 13-mm snowfall threshold.
Significance Statement
The purpose of my research is to answer the question: are there more motor vehicle crashes on the first day with snow each winter when compared with the number of crashes on other days with snowfall in the state of Indiana? Using four snowfall thresholds of increasing amounts, statistical tests comparing daily crashes on first snowfall and other snowfall days showed that there were significantly more crashes on average on the first day with snowfall each winter, regardless of the amount of snow accumulation. This supports the popular notion that crashes occur more frequently the first time it snows each year, although it is more likely attributed to drivers reacclimating to snowy road conditions than to forgetfulness.
Abstract
The winter season in many U.S. states includes snowfall, and with it comes comments about how drivers always seem to “forget” how to drive in snow when the first snowfall of the season occurs. This study assesses the accuracy of this popular sentiment during Indiana winters from 2007 to 2020. The number of motor vehicle crashes, injuries, and fatalities during the first snowfall of the season was compared with those during subsequent snow events. A grid of 46 cells was constructed to subdivide the state, and instances of snowfall and crashes were aggregated within each cell each day during the study period. Daily crash, injury, and fatality totals in each cell were normalized by their respective means and standard deviations, allowing for data from all cells to be combined into a single dataset. Four snow accumulation thresholds were examined: 1, 13, 25, and 51 mm. Distributions at each threshold show that more crashes occur on average on days with the first snowfall of the winter season than on other days with snowfall, regardless of the accumulation threshold used. Statistical tests support this result, showing significant differences between the mean numbers of crashes at each of the four snowfall thresholds. There were also significantly more injuries on the first snowfall day and more fatalities, although fatalities were only significant for the 13-mm snowfall threshold.
Significance Statement
The purpose of my research is to answer the question: are there more motor vehicle crashes on the first day with snow each winter when compared with the number of crashes on other days with snowfall in the state of Indiana? Using four snowfall thresholds of increasing amounts, statistical tests comparing daily crashes on first snowfall and other snowfall days showed that there were significantly more crashes on average on the first day with snowfall each winter, regardless of the amount of snow accumulation. This supports the popular notion that crashes occur more frequently the first time it snows each year, although it is more likely attributed to drivers reacclimating to snowy road conditions than to forgetfulness.
