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Abstract
The National Weather Service is planning to implement the system of probabilistic tornado warnings. In this paper, I estimate and compare the full societal costs of tornadoes with existing deterministic and potential probabilistic warnings. These full costs include the value of statistical lives lost as well as the value of the time spent sheltering. I find that probabilistic tornado warnings would decrease total expected fatalities. The improvement in decision-making would also decrease the total opportunity cost of time spent sheltering, even though the total sheltering time is likely to increase. In total, probabilistic warnings should lower the societal costs of tornadoes relative to deterministic warnings by approximately $76–139 million per year, with a large portion of this improvement coming from fewer casualties.
Significance Statement
I measure societal benefits of probabilistic and deterministic tornado warnings in the United States by evaluating their effects on expected casualties and sheltering costs. I find that probabilistic warnings deliver almost twice as much net societal benefit as deterministic ones. These gains happen as a result of fewer casualties and making protective behavior more responsive to risks and sheltering costs. This paper provides additional evidence of the need to implement probabilistic extreme weather warnings.
Abstract
The National Weather Service is planning to implement the system of probabilistic tornado warnings. In this paper, I estimate and compare the full societal costs of tornadoes with existing deterministic and potential probabilistic warnings. These full costs include the value of statistical lives lost as well as the value of the time spent sheltering. I find that probabilistic tornado warnings would decrease total expected fatalities. The improvement in decision-making would also decrease the total opportunity cost of time spent sheltering, even though the total sheltering time is likely to increase. In total, probabilistic warnings should lower the societal costs of tornadoes relative to deterministic warnings by approximately $76–139 million per year, with a large portion of this improvement coming from fewer casualties.
Significance Statement
I measure societal benefits of probabilistic and deterministic tornado warnings in the United States by evaluating their effects on expected casualties and sheltering costs. I find that probabilistic warnings deliver almost twice as much net societal benefit as deterministic ones. These gains happen as a result of fewer casualties and making protective behavior more responsive to risks and sheltering costs. This paper provides additional evidence of the need to implement probabilistic extreme weather warnings.
Abstract
The impact of climate change on subsistence agriculture is a major concern in the developing world. The vulnerability of the coastal regions to climate change has been highlighted, in particular. The present study assessed the impact of climate change on subsistence rice farming on the eastern Indian coast using an integrated approach of statistical trend analysis by the Mann–Kendall test and Sen’s slope estimation of climate data and remote sensing–based land-cover analyses using the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST) complemented by a questionnaire-based perception survey among the farming community. There has been a noticeable change in both ambient temperature and LST in the region. The delayed arrival of the monsoon critically impacts the cropping calendar. The crop harvest season has shifted farther into a time of the year that is prone to weather extremes. Analyses of NDVI and NDWI also indicate a shift in the cropping calendar. Over the years, there was an increasing degree of negative correlation between LST and NDVI in November, which indicates increasing water stress for crops in that time juncture. This may further cause crop sterility and yield loss. The study also reveals large-scale conversion of paddy-growing agricultural land into prawn aquaculture ponds. Farmers attributed such land-use change to cultivation stress caused by the delayed monsoon and consequent crop loss from weather extremes and changes in crop agronomic conditions. Farmers also report increased pest attacks and attribute that to an increasing temperature regime.
Abstract
The impact of climate change on subsistence agriculture is a major concern in the developing world. The vulnerability of the coastal regions to climate change has been highlighted, in particular. The present study assessed the impact of climate change on subsistence rice farming on the eastern Indian coast using an integrated approach of statistical trend analysis by the Mann–Kendall test and Sen’s slope estimation of climate data and remote sensing–based land-cover analyses using the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST) complemented by a questionnaire-based perception survey among the farming community. There has been a noticeable change in both ambient temperature and LST in the region. The delayed arrival of the monsoon critically impacts the cropping calendar. The crop harvest season has shifted farther into a time of the year that is prone to weather extremes. Analyses of NDVI and NDWI also indicate a shift in the cropping calendar. Over the years, there was an increasing degree of negative correlation between LST and NDVI in November, which indicates increasing water stress for crops in that time juncture. This may further cause crop sterility and yield loss. The study also reveals large-scale conversion of paddy-growing agricultural land into prawn aquaculture ponds. Farmers attributed such land-use change to cultivation stress caused by the delayed monsoon and consequent crop loss from weather extremes and changes in crop agronomic conditions. Farmers also report increased pest attacks and attribute that to an increasing temperature regime.
Abstract
Both the frequency and intensity of hot temperature extremes are expected to increase in the coming decades, challenging various socioeconomic sectors including public health. Therefore, societal attention data available in real time, such as Google search attention, could help monitor heat-wave impacts in domains with lagged data availability. Here, we jointly analyze societal attention and health impacts of heat waves in Germany at weekly time scales. We find that Google search attention responds similarly to hot temperatures as indicators of public health impacts, represented by excess mortality and hospitalizations. This emerges from piecewise linear relationships of Google search attention to and health impacts of temperature. We can then determine temperature thresholds above which both attention and public health are affected by heat. More generally, given the clear and similar response of societal indicators to heat, we conclude that heat waves can and should be defined from a joint societal and meteorological perspective, whereby temperatures are compared with thresholds established using societal data. A better joint understanding of societal attention and health impacts offers the potential to better manage future heat waves.
Abstract
Both the frequency and intensity of hot temperature extremes are expected to increase in the coming decades, challenging various socioeconomic sectors including public health. Therefore, societal attention data available in real time, such as Google search attention, could help monitor heat-wave impacts in domains with lagged data availability. Here, we jointly analyze societal attention and health impacts of heat waves in Germany at weekly time scales. We find that Google search attention responds similarly to hot temperatures as indicators of public health impacts, represented by excess mortality and hospitalizations. This emerges from piecewise linear relationships of Google search attention to and health impacts of temperature. We can then determine temperature thresholds above which both attention and public health are affected by heat. More generally, given the clear and similar response of societal indicators to heat, we conclude that heat waves can and should be defined from a joint societal and meteorological perspective, whereby temperatures are compared with thresholds established using societal data. A better joint understanding of societal attention and health impacts offers the potential to better manage future heat waves.
Abstract
Since the onset of the COVID-19 pandemic, decision-making during disasters fundamentally changed to accommodate the combined risks of hurricanes and infectious diseases. Prior research conducted in 2020 by Collins et al. examined how individuals changed their intended evacuation decision-making during the pandemic or their actual evacuation decisions during Hurricanes Laura and Sally. Hurricane Ida provided further data on evacuation decision-making when vaccinations and masks were widely available. A digital survey was disseminated to individuals affected by Hurricane Ida in 2021. Respondents provided information about their actual evacuation choices and perceptions of public shelters and COVID-19 risks. In comparison with the 2020 hurricane season, more individuals have reduced negative perceptions of hurricane shelters. However, individuals were less likely to utilize public shelters than in the 2020 season, with 11.4% more individuals stating they would definitely or probably avoid using shelters in 2021. Fewer individuals identified that COVID-19 was a primary reason they chose to stay home during Hurricane Ida (19.5% as compared with 86.8% during Hurricanes Laura and Sally). Furthermore, respondents with health risks for severe COVID-19 symptoms were no more likely to evacuate than those respondents who had no health risks. Potentially, as the pandemic progressed and vaccine availability and COVID-19 management improved, COVID-19 has had less impact on evacuation decision-making. The results from this work should guide planners in emergency management and public health in future hurricane seasons and future pandemics or other outbreaks to anticipate behavior changes and properly manage infectious disease threats.
Abstract
Since the onset of the COVID-19 pandemic, decision-making during disasters fundamentally changed to accommodate the combined risks of hurricanes and infectious diseases. Prior research conducted in 2020 by Collins et al. examined how individuals changed their intended evacuation decision-making during the pandemic or their actual evacuation decisions during Hurricanes Laura and Sally. Hurricane Ida provided further data on evacuation decision-making when vaccinations and masks were widely available. A digital survey was disseminated to individuals affected by Hurricane Ida in 2021. Respondents provided information about their actual evacuation choices and perceptions of public shelters and COVID-19 risks. In comparison with the 2020 hurricane season, more individuals have reduced negative perceptions of hurricane shelters. However, individuals were less likely to utilize public shelters than in the 2020 season, with 11.4% more individuals stating they would definitely or probably avoid using shelters in 2021. Fewer individuals identified that COVID-19 was a primary reason they chose to stay home during Hurricane Ida (19.5% as compared with 86.8% during Hurricanes Laura and Sally). Furthermore, respondents with health risks for severe COVID-19 symptoms were no more likely to evacuate than those respondents who had no health risks. Potentially, as the pandemic progressed and vaccine availability and COVID-19 management improved, COVID-19 has had less impact on evacuation decision-making. The results from this work should guide planners in emergency management and public health in future hurricane seasons and future pandemics or other outbreaks to anticipate behavior changes and properly manage infectious disease threats.
Abstract
Anticipatory actions are increasingly being taken before an extreme flood event to reduce the impacts on lives and livelihoods. Local contextualized information is required to support real-time local decisions on where and when to act and what anticipatory actions to take. This study defines an impact-based, early-warning trigger system that integrates flood forecasts with livelihood information, such as crop calendars, to target anticipatory actions better. We demonstrate the application of this trigger system using a flood case study from the Katakwi District in Uganda. First, we integrate information on the local crop cycles with the flood forecasts to define the impact-based trigger system. Second, we verify the impact-based system using historical flood impact information and then compare it with the existing hazard-based system in the context of humanitarian decisions. Study findings show that the impact-based trigger system has an improved probability of flood detection compared with the hazard-based system. There are fewer missed events in the impact-based system, while the trigger dates are similar in both systems. In a humanitarian context, the two systems trigger anticipatory actions at the same time. However, the impact-based trigger system can be further investigated in a different context (e.g., for livelihood protection) to assess the value of the local information. The impact-based system could also be a valuable tool to validate the existing hazard-based system, which builds more confidence in its use in informing anticipatory actions. The study findings, therefore, should open avenues for further dialogue on what the impact-based trigger system could mean within the broader forecast-based action landscape toward building the resilience of at-risk communities.
Abstract
Anticipatory actions are increasingly being taken before an extreme flood event to reduce the impacts on lives and livelihoods. Local contextualized information is required to support real-time local decisions on where and when to act and what anticipatory actions to take. This study defines an impact-based, early-warning trigger system that integrates flood forecasts with livelihood information, such as crop calendars, to target anticipatory actions better. We demonstrate the application of this trigger system using a flood case study from the Katakwi District in Uganda. First, we integrate information on the local crop cycles with the flood forecasts to define the impact-based trigger system. Second, we verify the impact-based system using historical flood impact information and then compare it with the existing hazard-based system in the context of humanitarian decisions. Study findings show that the impact-based trigger system has an improved probability of flood detection compared with the hazard-based system. There are fewer missed events in the impact-based system, while the trigger dates are similar in both systems. In a humanitarian context, the two systems trigger anticipatory actions at the same time. However, the impact-based trigger system can be further investigated in a different context (e.g., for livelihood protection) to assess the value of the local information. The impact-based system could also be a valuable tool to validate the existing hazard-based system, which builds more confidence in its use in informing anticipatory actions. The study findings, therefore, should open avenues for further dialogue on what the impact-based trigger system could mean within the broader forecast-based action landscape toward building the resilience of at-risk communities.
Abstract
The authors describe a tropical cyclone risk model for the Philippines using open-source methods that 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 night-lights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel, 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, Philippines, 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.
Significance Statement
Landfalling tropical cyclones are devastating disasters for which the Philippines is particularly at risk. Here we develop a model for tropical cyclone risk, quantified as property losses, over the Philippines and demonstrate its effectiveness by comparing to historical damages. We find that capturing the difference in vulnerability between the largest city in the Philippines (Manila) and more rural areas is important to accurately represent this risk. Using this model, we can more accurately constrain the risk of very extreme tropical cyclone events in the Philippines. The model can also be straightforwardly adapted for emergency planning in other countries and for climate change scenarios using openly available information.
Abstract
The authors describe a tropical cyclone risk model for the Philippines using open-source methods that 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 night-lights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel, 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, Philippines, 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.
Significance Statement
Landfalling tropical cyclones are devastating disasters for which the Philippines is particularly at risk. Here we develop a model for tropical cyclone risk, quantified as property losses, over the Philippines and demonstrate its effectiveness by comparing to historical damages. We find that capturing the difference in vulnerability between the largest city in the Philippines (Manila) and more rural areas is important to accurately represent this risk. Using this model, we can more accurately constrain the risk of very extreme tropical cyclone events in the Philippines. The model can also be straightforwardly adapted for emergency planning in other countries and for climate change scenarios using openly available information.
Abstract
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 differ by group. The results lead to recommendations and priorities for communicating about extreme heat.
Significance Statement
Effective communication between domain experts is a priority for informed decision-making under extreme weather conditions. As severe heat events increase in frequency and severity, the ability to communicate about heat and its impacts in a clear manner will become vital to life safety. The use of jargon-filled technical, scientific language can serve as a barrier to understanding and engagement, delaying decision-making and action. By identifying how extreme heat terms and concepts are understood among domain experts, risk communicators can determine where to focus on the development of plain language messaging, which improves decision support and decision-making.
Abstract
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 differ by group. The results lead to recommendations and priorities for communicating about extreme heat.
Significance Statement
Effective communication between domain experts is a priority for informed decision-making under extreme weather conditions. As severe heat events increase in frequency and severity, the ability to communicate about heat and its impacts in a clear manner will become vital to life safety. The use of jargon-filled technical, scientific language can serve as a barrier to understanding and engagement, delaying decision-making and action. By identifying how extreme heat terms and concepts are understood among domain experts, risk communicators can determine where to focus on the development of plain language messaging, which improves decision support and decision-making.
Abstract
Climate change is expected to impact individuals’ recreation choices, as changing temperatures and precipitation patterns influence participation in outdoor recreation and alternative activities. This paper empirically investigates the relationship between weather and outdoor recreation using nationally representative data from the contiguous United States. We find that across most outdoor recreation activities, participation is lowest on the coldest days [<35°F (1.7°C)] and highest at moderately high temperatures [80°–90°F (27°–32°C)]. Notable exceptions to this trend include water sports and snow and ice sports, for which participation peaks at the highest and lowest temperatures, respectively. If individuals continue to respond to temperature changes the same way that they have in the recent past, in a future climate that has fewer cool days and more moderate and hot days, our model anticipates net participation across all outdoor recreation activities will increase by 88 million trips annually at 1°C of warming (CONUS) and by up to 401 million trips at 6°C of warming, valued between $3.2 and $15.6 billion in consumer surplus annually (2010 population). The increase in trips is driven by participation in water sports; excluding water sports from future projections decreases the consumer surplus gains by approximately 75% across all modeled degrees of warming. If individuals in northern regions respond to temperature like people in southern regions currently do (a proxy for adaptation), total outdoor recreation trips will increase by an additional 17% in comparison with no adaptation at 6°C of warming. This benefit is generally not seen at lower degrees of warming.
Significance Statement
We extend the extant literature in four key ways. First, we probe how impacts vary by region of the United States, revealing disparate impacts across geographies. Second, we analyze substitution patterns between alternative activity categories, which sheds light on the broader implications beyond participation in outdoor recreation. Third, we add depth to the future projection of climate impacts by considering how our projections might change under empirically informed scenarios of adaptation and acclimatization. Fourth, our analysis showcases one way in which climate change results in net benefits.
Abstract
Climate change is expected to impact individuals’ recreation choices, as changing temperatures and precipitation patterns influence participation in outdoor recreation and alternative activities. This paper empirically investigates the relationship between weather and outdoor recreation using nationally representative data from the contiguous United States. We find that across most outdoor recreation activities, participation is lowest on the coldest days [<35°F (1.7°C)] and highest at moderately high temperatures [80°–90°F (27°–32°C)]. Notable exceptions to this trend include water sports and snow and ice sports, for which participation peaks at the highest and lowest temperatures, respectively. If individuals continue to respond to temperature changes the same way that they have in the recent past, in a future climate that has fewer cool days and more moderate and hot days, our model anticipates net participation across all outdoor recreation activities will increase by 88 million trips annually at 1°C of warming (CONUS) and by up to 401 million trips at 6°C of warming, valued between $3.2 and $15.6 billion in consumer surplus annually (2010 population). The increase in trips is driven by participation in water sports; excluding water sports from future projections decreases the consumer surplus gains by approximately 75% across all modeled degrees of warming. If individuals in northern regions respond to temperature like people in southern regions currently do (a proxy for adaptation), total outdoor recreation trips will increase by an additional 17% in comparison with no adaptation at 6°C of warming. This benefit is generally not seen at lower degrees of warming.
Significance Statement
We extend the extant literature in four key ways. First, we probe how impacts vary by region of the United States, revealing disparate impacts across geographies. Second, we analyze substitution patterns between alternative activity categories, which sheds light on the broader implications beyond participation in outdoor recreation. Third, we add depth to the future projection of climate impacts by considering how our projections might change under empirically informed scenarios of adaptation and acclimatization. Fourth, our analysis showcases one way in which climate change results in net benefits.
Abstract
Over three decades, the U.S. Global Change Research Program (USGCRP) has developed an assessment process to integrate, evaluate, and interpret scientific findings on climate change as well as discuss uncertainties. In six USGCRP assessments, authors identified research gaps, or topics that they indicated required more information or study. Examining research gaps on a continual and systematic basis can aid decisions about research projects, programmatic priorities, and strategic scientific visions. The methodology presented here addresses two aims: 1) identify and categorize research gaps within a searchable database and 2) demonstrate use of the database to inform future science planning and assessment. Results include the top 10 database themes, 18 recurring topics across assessments, and a search example for vulnerability gaps. The benefits and limitations of this approach are discussed, along with recommendations to improve future U.S. climate assessment products.
Significance Statement
The U.S. Global Change Research Program (USGCRP) regularly assesses the state of the science of climate change and its impacts in national-level reports written by federal scientists, academic researchers, practitioners, and other experts from across the country. These reports are designed to inform policy choices and decision-making by addressing scientific confidence, uncertainty, and research gaps that limit conclusions. We identified research gaps from six climate reports and organized them into a database with over 1000 entries, spanning more than 300 topic areas. Database entries were also sorted into 22 themes for searchability. The database can be used by students, researchers, and program managers to find open research questions and plan future work.
Abstract
Over three decades, the U.S. Global Change Research Program (USGCRP) has developed an assessment process to integrate, evaluate, and interpret scientific findings on climate change as well as discuss uncertainties. In six USGCRP assessments, authors identified research gaps, or topics that they indicated required more information or study. Examining research gaps on a continual and systematic basis can aid decisions about research projects, programmatic priorities, and strategic scientific visions. The methodology presented here addresses two aims: 1) identify and categorize research gaps within a searchable database and 2) demonstrate use of the database to inform future science planning and assessment. Results include the top 10 database themes, 18 recurring topics across assessments, and a search example for vulnerability gaps. The benefits and limitations of this approach are discussed, along with recommendations to improve future U.S. climate assessment products.
Significance Statement
The U.S. Global Change Research Program (USGCRP) regularly assesses the state of the science of climate change and its impacts in national-level reports written by federal scientists, academic researchers, practitioners, and other experts from across the country. These reports are designed to inform policy choices and decision-making by addressing scientific confidence, uncertainty, and research gaps that limit conclusions. We identified research gaps from six climate reports and organized them into a database with over 1000 entries, spanning more than 300 topic areas. Database entries were also sorted into 22 themes for searchability. The database can be used by students, researchers, and program managers to find open research questions and plan future work.
Abstract
The WxChallenge is a national forecasting competition in which participants at colleges and universities across North America attempt to accurately predict daily high and low temperatures, maximum wind speed, and precipitation accumulation at a variety of locations each year. Undergraduate students make up the majority of participants. In this study, we observed trends from 11 seasons of WxChallenge data and related them to existing literature on local forecasting contests. Normalized scores were calculated each day for any participant that submitted a forecast. On average, undergraduate scores improved with continued participation in the contest. Significant gains are made during their first year by forecasters who participated for multiple years. Duration of participation in the contest plays a role, but significant improvements in performance were also noted with higher academic standing, potentially as a result of forecasting experience gained through other curricular or extracurricular activities.
Significance Statement
One avenue that aspiring meteorologists have outside the classroom to hone their forecasting skills is through the WxChallenge, a national collegiate forecasting contest. We wanted to see whether, on average, students who participated longer in the contest outperformed those who did not forecast for as long. We found this to be true. Mean scores by students who participated for more than one year improved significantly by the end of the first year, and they outperformed those without prior experience in the WxChallenge in following years. These findings are important because they can be used as evidence to motivate students to join a forecasting contest early in their academic careers, allowing them to gain skill before pursuing a career in forecasting.
Abstract
The WxChallenge is a national forecasting competition in which participants at colleges and universities across North America attempt to accurately predict daily high and low temperatures, maximum wind speed, and precipitation accumulation at a variety of locations each year. Undergraduate students make up the majority of participants. In this study, we observed trends from 11 seasons of WxChallenge data and related them to existing literature on local forecasting contests. Normalized scores were calculated each day for any participant that submitted a forecast. On average, undergraduate scores improved with continued participation in the contest. Significant gains are made during their first year by forecasters who participated for multiple years. Duration of participation in the contest plays a role, but significant improvements in performance were also noted with higher academic standing, potentially as a result of forecasting experience gained through other curricular or extracurricular activities.
Significance Statement
One avenue that aspiring meteorologists have outside the classroom to hone their forecasting skills is through the WxChallenge, a national collegiate forecasting contest. We wanted to see whether, on average, students who participated longer in the contest outperformed those who did not forecast for as long. We found this to be true. Mean scores by students who participated for more than one year improved significantly by the end of the first year, and they outperformed those without prior experience in the WxChallenge in following years. These findings are important because they can be used as evidence to motivate students to join a forecasting contest early in their academic careers, allowing them to gain skill before pursuing a career in forecasting.
