Pub Date : 2023-03-22DOI: 10.1175/wcas-d-22-0108.1
Jeannette Sutton, Nicholas Waugh, Savannah Olivas
Climate trends indicate that extreme heat events are becoming more common and more severe over time, requiring improved strategies to communicate heat risk and protective actions. However, there exists a disconnect in heat-related communication from experts, who commonly include heat related jargon (i.e., technical language), to decision makers and the general public. The use of jargon has been shown to reduce meaningful engagement with and understanding of messages written by experts. Translating technical language into comprehensible messages that encourage decision makers to take action has been identified as a priority to enable impact-based decision support. Knowing what concepts and terms are perceived as jargon, and why, is a first step to increasing communication effectiveness. With this in mind, we focus on the mental models about extreme heat among two groups of domain experts –those trained in atmospheric science and those trained in emergency management to identify how each group understands terms and concepts about extreme heat. We use a hybrid data collection method of open card sorting and think-aloud interviews to identify how participants conceptualize and categorize terms and concepts related to extreme heat. While we find few differences within the sorted categories, we learn that the processes leading to decisions about the importance of including, or not including, technical information differs by group. The results lead to recommendations and priorities for communicating about extreme heat.
{"title":"Communicating about Extreme Heat: Results from Card Sorting and Think Aloud Interviews with Experts from Differing Domains","authors":"Jeannette Sutton, Nicholas Waugh, Savannah Olivas","doi":"10.1175/wcas-d-22-0108.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0108.1","url":null,"abstract":"\u0000Climate trends indicate that extreme heat events are becoming more common and more severe over time, requiring improved strategies to communicate heat risk and protective actions. However, there exists a disconnect in heat-related communication from experts, who commonly include heat related jargon (i.e., technical language), to decision makers and the general public. The use of jargon has been shown to reduce meaningful engagement with and understanding of messages written by experts. Translating technical language into comprehensible messages that encourage decision makers to take action has been identified as a priority to enable impact-based decision support. Knowing what concepts and terms are perceived as jargon, and why, is a first step to increasing communication effectiveness. With this in mind, we focus on the mental models about extreme heat among two groups of domain experts –those trained in atmospheric science and those trained in emergency management to identify how each group understands terms and concepts about extreme heat. We use a hybrid data collection method of open card sorting and think-aloud interviews to identify how participants conceptualize and categorize terms and concepts related to extreme heat. While we find few differences within the sorted categories, we learn that the processes leading to decisions about the importance of including, or not including, technical information differs by group. The results lead to recommendations and priorities for communicating about extreme heat.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43756168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-15DOI: 10.1175/wcas-d-22-0064.1
Jessica Burgeno, S. Joslyn
When forecasts for a major weather event begin days in advance, updates may be more accurate but inconsistent with the original forecast. Evidence suggests that resulting inconsistency may reduce user trust. However, adding an uncertainty estimate to the forecast may attenuate any loss of trust due to forecast inconsistency as has been shown with forecast inaccuracy. To evaluate this hypothesis, the experiment reported here, tested the impact on trust of adding probabilistic snow accumulation forecasts to single value forecasts in a series of original and revised forecast pairs (based on historical records) that varied in both consistency and accuracy. Participants rated their trust in the forecasts and used them to make school closure decisions. Half of participants received single-value forecasts and half also received the probability of 6 or more inches (decision threshold in the assigned task). As with previous research, forecast inaccuracy was detrimental to trust although probabilistic forecasts attenuated the effect. Moreover, the inclusion of probabilistic forecasts allowed participants to make economically better decisions. Surprisingly, in this study, inconsistency increased, rather than decreased trust, perhaps because it alerted participants to uncertainty and led them to make more cautious decisions. Furthermore, the positive effect of inconsistency on trust was enhanced by the inclusion of probabilistic forecast. This work has important implications for practical settings, suggesting that both probabilistic forecasts and forecast inconsistency provide useful information to decision makers. Therefore, members of the public may well benefit from well-calibrated uncertainty estimates and newer, more reliable information.
{"title":"The Impact of Forecast Inconsistency and Probabilistic Forecasts on Users’ Trust and Decision-Making","authors":"Jessica Burgeno, S. Joslyn","doi":"10.1175/wcas-d-22-0064.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0064.1","url":null,"abstract":"\u0000When forecasts for a major weather event begin days in advance, updates may be more accurate but inconsistent with the original forecast. Evidence suggests that resulting inconsistency may reduce user trust. However, adding an uncertainty estimate to the forecast may attenuate any loss of trust due to forecast inconsistency as has been shown with forecast inaccuracy. To evaluate this hypothesis, the experiment reported here, tested the impact on trust of adding probabilistic snow accumulation forecasts to single value forecasts in a series of original and revised forecast pairs (based on historical records) that varied in both consistency and accuracy. Participants rated their trust in the forecasts and used them to make school closure decisions. Half of participants received single-value forecasts and half also received the probability of 6 or more inches (decision threshold in the assigned task). As with previous research, forecast inaccuracy was detrimental to trust although probabilistic forecasts attenuated the effect. Moreover, the inclusion of probabilistic forecasts allowed participants to make economically better decisions. Surprisingly, in this study, inconsistency increased, rather than decreased trust, perhaps because it alerted participants to uncertainty and led them to make more cautious decisions. Furthermore, the positive effect of inconsistency on trust was enhanced by the inclusion of probabilistic forecast. This work has important implications for practical settings, suggesting that both probabilistic forecasts and forecast inconsistency provide useful information to decision makers. Therefore, members of the public may well benefit from well-calibrated uncertainty estimates and newer, more reliable information.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47351781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-13DOI: 10.1175/wcas-d-22-0087.1
A. Goodin, C. Rogers, Angela Zhang
This study investigates if and how energy consumers respond to public appeals for voluntary conservation during an extended and extreme winter energy emergency. Public appeals are an increasingly important tool for managing demand when grid disruptions are anticipated, especially given the increase in severe weather events. We add to the few studies on winter energy crises by investigating a case where there were repeated public appeals during an extended event. Using a survey implemented via social media immediately after the February 2021 winter storm, we asked residents of Norman, Oklahoma a series of questions about their responses to the public appeals distributed by the utility company, including if they followed the actions suggested in the messages as well as where they got information and their level of concern about the storm impacts. We compare mean responses across a range of categorical answers using standard independent t-tests, one-way ANOVA tests, and Chi-square tests. Among the 296 respondents, there was a high degree of reported compliance, including setting the thermostat to 68 degrees Fahrenheit or lower (72%), avoiding using major appliances (86%), and turning off non-essential appliances, lights, and equipment (89%). Our findings suggest a high degree of willingness to voluntarily reduce energy consumption during an energy emergency. This is encouraging for energy managers: public appeals can be disseminated via social media at a low cost and in real time during an extended emergency event.
{"title":"Public Responses to Emergency Energy Conservation Messaging: Evidence from the 2021 Winter Storm in Norman, OK","authors":"A. Goodin, C. Rogers, Angela Zhang","doi":"10.1175/wcas-d-22-0087.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0087.1","url":null,"abstract":"\u0000This study investigates if and how energy consumers respond to public appeals for voluntary conservation during an extended and extreme winter energy emergency. Public appeals are an increasingly important tool for managing demand when grid disruptions are anticipated, especially given the increase in severe weather events. We add to the few studies on winter energy crises by investigating a case where there were repeated public appeals during an extended event. Using a survey implemented via social media immediately after the February 2021 winter storm, we asked residents of Norman, Oklahoma a series of questions about their responses to the public appeals distributed by the utility company, including if they followed the actions suggested in the messages as well as where they got information and their level of concern about the storm impacts. We compare mean responses across a range of categorical answers using standard independent t-tests, one-way ANOVA tests, and Chi-square tests. Among the 296 respondents, there was a high degree of reported compliance, including setting the thermostat to 68 degrees Fahrenheit or lower (72%), avoiding using major appliances (86%), and turning off non-essential appliances, lights, and equipment (89%). Our findings suggest a high degree of willingness to voluntarily reduce energy consumption during an energy emergency. This is encouraging for energy managers: public appeals can be disseminated via social media at a low cost and in real time during an extended emergency event.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47748094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-10DOI: 10.1175/wcas-d-22-0090.1
Amber Silver, Sam Jackson
In 2018, Hurricanes Florence and Michael affected the southeastern portion of the United States with widespread impacts in Florida, North and South Carolina, Georgia, and Virginia. The two storms were markedly different in terms of their meteorological history: Hurricane Florence made landfall as a Category 1 storm approximately two weeks after formation, while Hurricane Michael made landfall as an “unprecedented” Category 5 storm just three days after formation. The stark meteorological differences provided the opportunity to explore whether and to what extent public attention is influenced by storm severity. This study utilized both direct (i.e., tweet volume, search volume) and indirect (i.e., number of newspaper articles) measures to explore public attention at different scales. The results found that Hurricane Florence received more attention than Hurricane Michael, both regionally and nationally, across all three measures. The findings also underscore the importance of time for the process of attention-building, especially at the national-level. Taken together, the results suggest that storm severity, forecast lead-time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways. The paper concludes with some opportunities for future research that may provide additional insights into the linkages between attention, perception, and decision-making.
{"title":"Public attention during Hurricanes Florence and Michael","authors":"Amber Silver, Sam Jackson","doi":"10.1175/wcas-d-22-0090.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0090.1","url":null,"abstract":"\u0000In 2018, Hurricanes Florence and Michael affected the southeastern portion of the United States with widespread impacts in Florida, North and South Carolina, Georgia, and Virginia. The two storms were markedly different in terms of their meteorological history: Hurricane Florence made landfall as a Category 1 storm approximately two weeks after formation, while Hurricane Michael made landfall as an “unprecedented” Category 5 storm just three days after formation. The stark meteorological differences provided the opportunity to explore whether and to what extent public attention is influenced by storm severity. This study utilized both direct (i.e., tweet volume, search volume) and indirect (i.e., number of newspaper articles) measures to explore public attention at different scales. The results found that Hurricane Florence received more attention than Hurricane Michael, both regionally and nationally, across all three measures. The findings also underscore the importance of time for the process of attention-building, especially at the national-level. Taken together, the results suggest that storm severity, forecast lead-time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways. The paper concludes with some opportunities for future research that may provide additional insights into the linkages between attention, perception, and decision-making.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43676236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-08DOI: 10.1175/wcas-d-22-0049.1
J. Baldwin, Chia-ying Lee, B. Walsh, S. Camargo, A. Sobel
The authors describe a tropical cyclone risk model for the Philippines, using methods that are open-source and can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally-forced tropical cyclone hazard model are combined with data representing exposed value and vulnerability to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is distributed across a country following population density and nightlights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel (2011), with free parameters chosen by a sensitivity analysis in which simulated and historical reported damages are compared for different parameter values, and further constrained by information from household surveys about regional building characteristics. Use of different vulnerability parameters for the region around Manila yields much better agreement between simulated and actually reported losses than does a single set of parameters for the entire country. Despite the improvements from regionally refined vulnerability, the model predicts no losses for a substantial number of destructive historical storms, a difference the authors hypothesize is due to the use of wind speed as the sole metric of tropical cyclone hazard, omitting explicit representation of storm surge and/or rainfall. Bearing these limitations in mind, this model can be used to estimate return levels for tropical cyclone-caused wind hazards and asset losses for regions across the Philippines, relevant to some disaster risk reduction and management tasks; this model also provides a platform for further development of open-source tropical cyclone risk modeling.
{"title":"Vulnerability in a Tropical Cyclone Risk Model: Philippines Case Study","authors":"J. Baldwin, Chia-ying Lee, B. Walsh, S. Camargo, A. Sobel","doi":"10.1175/wcas-d-22-0049.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0049.1","url":null,"abstract":"\u0000The authors describe a tropical cyclone risk model for the Philippines, using methods that are open-source and can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally-forced tropical cyclone hazard model are combined with data representing exposed value and vulnerability to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is distributed across a country following population density and nightlights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel (2011), with free parameters chosen by a sensitivity analysis in which simulated and historical reported damages are compared for different parameter values, and further constrained by information from household surveys about regional building characteristics. Use of different vulnerability parameters for the region around Manila yields much better agreement between simulated and actually reported losses than does a single set of parameters for the entire country. Despite the improvements from regionally refined vulnerability, the model predicts no losses for a substantial number of destructive historical storms, a difference the authors hypothesize is due to the use of wind speed as the sole metric of tropical cyclone hazard, omitting explicit representation of storm surge and/or rainfall. Bearing these limitations in mind, this model can be used to estimate return levels for tropical cyclone-caused wind hazards and asset losses for regions across the Philippines, relevant to some disaster risk reduction and management tasks; this model also provides a platform for further development of open-source tropical cyclone risk modeling.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49628059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-06DOI: 10.1175/wcas-d-22-0010.1
Haven J. Cashwell, K. McNeal, K. Dello, Ryan P. Boyles, C. Davis
Species Status Assessments (SSAs) are required to be completed for endangered species by the United States Fish and Wildlife Service (USFWS) and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information since climate is a factor that will impact species in the future. To aid in including climate information, a Decision Support System (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina (SCONC) using a co-production approach. In this study, users viewed a mockup version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye-tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs, and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box and whisker plots and spatially through a sequential color ramp from white to purple was more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation.
{"title":"User Engagement Testing with a Pilot Decision Support Tool Aimed to Support Species Managers","authors":"Haven J. Cashwell, K. McNeal, K. Dello, Ryan P. Boyles, C. Davis","doi":"10.1175/wcas-d-22-0010.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0010.1","url":null,"abstract":"\u0000Species Status Assessments (SSAs) are required to be completed for endangered species by the United States Fish and Wildlife Service (USFWS) and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information since climate is a factor that will impact species in the future. To aid in including climate information, a Decision Support System (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina (SCONC) using a co-production approach. In this study, users viewed a mockup version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye-tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs, and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box and whisker plots and spatially through a sequential color ramp from white to purple was more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45836289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-03DOI: 10.1175/wcas-d-22-0074.1
R. Wilson, C. Shaffer-Morrison, H. Walpole
In the eastern Corn Belt of the United States, climate change is projected to bring warmer and wetter conditions, with more variability in the seasonal timing of rainfall, creating a multitude of challenges for agricultural production. While there are multiple adaptations to reduce the vulnerability of production to a changing climate, these adaptations have varying implications for other ecosystem services such as soil health, carbon sequestration and water quality. We explore how beliefs about and experiences with climate change might influence adaptations that vary in their provisioning of a variety of ecosystem services, and how these adaptations may vary by characteristics of the farm and farmer. Survey data were collected from 908 respondents in August through October 2019. We find only one proposed adaptation, additional tile drainage, is associated with self-reported prior negative experiences with climate change and concern about future impacts. The other proposed adaptations (i.e., cover crops, filter strips, additional fertilizer) are associated with farmer identity. The type of farmer who is likely to adapt is generally reminiscent of the type who engage in conservation practices: younger, more educated, with off-farm income and larger farms. Our results indicate that many proposed adaptations are not perceived as effective ways to mitigate specific climate driven impacts. However, increasing tile drainage is perceived as such, and there may be a need to offset the potential negative impacts to water quality of this likely adaptation through the promotion of edge-of-field filtration practices.
{"title":"Climate exacerbated impacts may drive maladaptive action in agriculture","authors":"R. Wilson, C. Shaffer-Morrison, H. Walpole","doi":"10.1175/wcas-d-22-0074.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0074.1","url":null,"abstract":"\u0000In the eastern Corn Belt of the United States, climate change is projected to bring warmer and wetter conditions, with more variability in the seasonal timing of rainfall, creating a multitude of challenges for agricultural production. While there are multiple adaptations to reduce the vulnerability of production to a changing climate, these adaptations have varying implications for other ecosystem services such as soil health, carbon sequestration and water quality. We explore how beliefs about and experiences with climate change might influence adaptations that vary in their provisioning of a variety of ecosystem services, and how these adaptations may vary by characteristics of the farm and farmer. Survey data were collected from 908 respondents in August through October 2019. We find only one proposed adaptation, additional tile drainage, is associated with self-reported prior negative experiences with climate change and concern about future impacts. The other proposed adaptations (i.e., cover crops, filter strips, additional fertilizer) are associated with farmer identity. The type of farmer who is likely to adapt is generally reminiscent of the type who engage in conservation practices: younger, more educated, with off-farm income and larger farms. Our results indicate that many proposed adaptations are not perceived as effective ways to mitigate specific climate driven impacts. However, increasing tile drainage is perceived as such, and there may be a need to offset the potential negative impacts to water quality of this likely adaptation through the promotion of edge-of-field filtration practices.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41746333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-23DOI: 10.1175/wcas-d-22-0098.1
Lauren Prox
Spanning 464.24 km2, Pokhara Metropolitan City is Nepal’s largest city by area. With over 400,000 residents, it’s also Nepal’s second most populous city. This research investigated a biometeorological system present within Pokhara Metropolitan city concerning air pollution, meteorology, and health. Different aspects of this system are more or less influential in various regions of the city and understanding these relationships can assist with future health interventions for limiting exposure to pollutants. This research was completed using data sets published in government records and scientific literature, showcasing what can be accomplished with open-source data. Key findings were a positive correlation between air pollution levels and chronic obstructive pulmonary disease hospital admissions into Pokhara’s Western Regional Hospital and a negative correlation between meteorological measurements and hospital admissions. These findings aligned with the general body of literature regarding risk factors for chronic obstructive pulmonary disease and hospitalizations. Multivariate regressions yielded better predictions for hospital admissions using both mean low and high temperatures as opposed to using one temperature variable, which implied both daily low and high temperatures correlate to hospital admissions. Results also revealed air pollution levels for pollutants equal to or less than ten micrometers and greater than 2.5 micrometers were better predictors of hospital admissions than air pollutants sized 2.5 micrometers and below. Findings prompted questions about the relationships between different pollutant sizes and their correlations to chronic obstructive pulmonary disease hospitalizations. Findings also yielded questions regarding health interventions and Pokhara’s built environment, which may be investigated in future research.
{"title":"Investigating the relationship between air pollution, meteorology, and COPD trends in Pokhara Metropolitan City, Nepal","authors":"Lauren Prox","doi":"10.1175/wcas-d-22-0098.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0098.1","url":null,"abstract":"\u0000Spanning 464.24 km2, Pokhara Metropolitan City is Nepal’s largest city by area. With over 400,000 residents, it’s also Nepal’s second most populous city. This research investigated a biometeorological system present within Pokhara Metropolitan city concerning air pollution, meteorology, and health. Different aspects of this system are more or less influential in various regions of the city and understanding these relationships can assist with future health interventions for limiting exposure to pollutants. This research was completed using data sets published in government records and scientific literature, showcasing what can be accomplished with open-source data. Key findings were a positive correlation between air pollution levels and chronic obstructive pulmonary disease hospital admissions into Pokhara’s Western Regional Hospital and a negative correlation between meteorological measurements and hospital admissions. These findings aligned with the general body of literature regarding risk factors for chronic obstructive pulmonary disease and hospitalizations. Multivariate regressions yielded better predictions for hospital admissions using both mean low and high temperatures as opposed to using one temperature variable, which implied both daily low and high temperatures correlate to hospital admissions. Results also revealed air pollution levels for pollutants equal to or less than ten micrometers and greater than 2.5 micrometers were better predictors of hospital admissions than air pollutants sized 2.5 micrometers and below. Findings prompted questions about the relationships between different pollutant sizes and their correlations to chronic obstructive pulmonary disease hospitalizations. Findings also yielded questions regarding health interventions and Pokhara’s built environment, which may be investigated in future research.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47977699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1175/wcas-d-22-0123.1
Zhewei Mi, Hongwei Zhan
Media such as Twitter has become a platform for contemporary Americans to express their opinions and allow for reaction to public opinion. Climate change is a topic of ongoing social concern. Machine-automated processing facilitates big data analysis and is suitable for analyzing a large corpus of tweets. This paper uses R tools to conduct sentiment calculation and emotion analysis on the tweet corpus from 2015 to 2018 to present the overall tendency of citizens’ attitudes toward climate change topics. The keyword analysis finds that people focus on the message’s source; CLIMATE CHANGE and GLOBAL WARMING make an association. Supporters express FEAR and SURPRISE about extreme weather and opponents’ behavior, while opponents show ANGER, DISGUST and SADNESS about politicians manufacturing climate change stories about which they have no real feelings. This study also reveals that the automatic annotation tools are still inadequate, with limited emotion lexicon and identification of negation and sarcasm.
{"title":"Text mining attitudes towards climate change: Emotion and sentiment analysis of the Twitter corpus","authors":"Zhewei Mi, Hongwei Zhan","doi":"10.1175/wcas-d-22-0123.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0123.1","url":null,"abstract":"\u0000Media such as Twitter has become a platform for contemporary Americans to express their opinions and allow for reaction to public opinion. Climate change is a topic of ongoing social concern. Machine-automated processing facilitates big data analysis and is suitable for analyzing a large corpus of tweets. This paper uses R tools to conduct sentiment calculation and emotion analysis on the tweet corpus from 2015 to 2018 to present the overall tendency of citizens’ attitudes toward climate change topics. The keyword analysis finds that people focus on the message’s source; CLIMATE CHANGE and GLOBAL WARMING make an association. Supporters express FEAR and SURPRISE about extreme weather and opponents’ behavior, while opponents show ANGER, DISGUST and SADNESS about politicians manufacturing climate change stories about which they have no real feelings. This study also reveals that the automatic annotation tools are still inadequate, with limited emotion lexicon and identification of negation and sarcasm.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43609678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-10DOI: 10.1175/wcas-d-22-0115.1
M. A. Casteel
Research has found that people who know the least about a topic are often very overconfident of their knowledge, while those who know the most often underestimate their knowledge. This finding, known as the Dunning-Kruger Effect (DKE) has recently been shown to occur in knowledge of severe weather as well. The current study investigated whether being overconfident in one’s knowledge might translate into a tendency to make poorer sheltering decisions when faced with severe weather. Participants took two severe weather quizzes, one of perceived knowledge and one of objective knowledge. Participants also predicted their performance on both quizzes. The participants then saw four wireless emergency tornado warning alerts on a simulated smartphone screen, along with a tornado scenario, and then made two protective action decisions: one about immediately sheltering in place and the other the likelihood they would drive away. The results revealed that the participants did exhibit the DKE: those with the lowest levels of knowledge exhibited the most overconfidence while those with the highest levels of knowledge underestimated their performance. Also, compared to individuals with the most knowledge, those with the least knowledge were the most likely to state that they would not shelter immediately, and would get in their car and drive away. Although more education is needed, the findings suggest a conundrum: those who know the least about severe weather, thinking they know a lot, are likely those individuals least likely to seek out additional education on the topic.
{"title":"What do I know about severe weather? The influence of weather knowledge on protective action decisions.","authors":"M. A. Casteel","doi":"10.1175/wcas-d-22-0115.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0115.1","url":null,"abstract":"Research has found that people who know the least about a topic are often very overconfident of their knowledge, while those who know the most often underestimate their knowledge. This finding, known as the Dunning-Kruger Effect (DKE) has recently been shown to occur in knowledge of severe weather as well. The current study investigated whether being overconfident in one’s knowledge might translate into a tendency to make poorer sheltering decisions when faced with severe weather. Participants took two severe weather quizzes, one of perceived knowledge and one of objective knowledge. Participants also predicted their performance on both quizzes. The participants then saw four wireless emergency tornado warning alerts on a simulated smartphone screen, along with a tornado scenario, and then made two protective action decisions: one about immediately sheltering in place and the other the likelihood they would drive away. The results revealed that the participants did exhibit the DKE: those with the lowest levels of knowledge exhibited the most overconfidence while those with the highest levels of knowledge underestimated their performance. Also, compared to individuals with the most knowledge, those with the least knowledge were the most likely to state that they would not shelter immediately, and would get in their car and drive away. Although more education is needed, the findings suggest a conundrum: those who know the least about severe weather, thinking they know a lot, are likely those individuals least likely to seek out additional education on the topic.","PeriodicalId":48971,"journal":{"name":"Weather Climate and Society","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45088863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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