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Minding the Weather
The Measurement of Weather Salience
Weather salience is a construct that pertains to the psychological value, significance, and attunement that people have for the weather and its changes. In this article the author describes the construct of weather salience and a measure that was created to assess it, the Weather Salience Questionnaire (WxSQ). The author evaluates the measure's psychometric properties, its relationship to owning and using a thermometer, and its relationship with prior hurricane evacuations and having experienced the effects of severe weather using a convenience sample of 946 undergraduate students. The WxSQ measurement model exhibits a good fit to the data following a maximum likelihood factor analysis of the items. The results of other analyses reveal that the WxSQ possesses acceptable psychometric properties (Cronbach's α = 0.83, test-retest reliability coefficient of 0.91). Weather salience was related to the ownership and use of a thermometer and also to being able to correctly distinguish between weather watches and warnings. Differences in weather salience scores also were observed, especially for men, between those students who had (versus had not) evacuated because of a hurricane and between those who had (versus had not) experienced weather-related property damages. The limitations of the study due to the use of an undergraduate sample are discussed along with some possible applications of the WxSQ.
Weather salience is a construct that pertains to the psychological value, significance, and attunement that people have for the weather and its changes. In this article the author describes the construct of weather salience and a measure that was created to assess it, the Weather Salience Questionnaire (WxSQ). The author evaluates the measure's psychometric properties, its relationship to owning and using a thermometer, and its relationship with prior hurricane evacuations and having experienced the effects of severe weather using a convenience sample of 946 undergraduate students. The WxSQ measurement model exhibits a good fit to the data following a maximum likelihood factor analysis of the items. The results of other analyses reveal that the WxSQ possesses acceptable psychometric properties (Cronbach's α = 0.83, test-retest reliability coefficient of 0.91). Weather salience was related to the ownership and use of a thermometer and also to being able to correctly distinguish between weather watches and warnings. Differences in weather salience scores also were observed, especially for men, between those students who had (versus had not) evacuated because of a hurricane and between those who had (versus had not) experienced weather-related property damages. The limitations of the study due to the use of an undergraduate sample are discussed along with some possible applications of the WxSQ.
Abstract
Weeklong weather science and safety workshops were conducted with 66 teachers of kindergarten through eighth grade (K–8) in three Georgia counties using the American Red Cross (ARC) Masters of Disaster (MoD) curriculum. The workshop goals included building teacher interests in the MoD, increasing teacher knowledge about the MoD curriculum, increasing and evaluating its use by teachers, disseminating information about it to other teachers, evaluating students’ weather science and safety knowledge, and evaluating students’ and families’ weather safety behavior. Workshop participation produced significant increases in teachers’ knowledge about the MoD curriculum, their general knowledge of weather science and safety, and self-efficacy in teaching their students about severe weather. In the year following the workshops, at least 32 teachers from the workshops delivered 178 MoD lessons to 2,465 students in K–8. In a sample of 291 students whose teachers delivered an MoD lesson on lightning, tornadoes, hurricanes, or floods, students obtained a mean of 60% correct responses on a comprehensive postlesson follow-up test. In a follow-up study with a subsample of 94 parents whose children received instruction from the MoD curriculum, 71% of the families indicated that they had developed safety plans and took additional steps (e.g., assembled safety kits, identified evacuation routes, and/or gathered supplies) to prepare for severe weather. This project is thought to be the first of its kind to demonstrate systematically the effectiveness of weather science and safety education for teachers, their students, and the students’ parents.
Abstract
Weeklong weather science and safety workshops were conducted with 66 teachers of kindergarten through eighth grade (K–8) in three Georgia counties using the American Red Cross (ARC) Masters of Disaster (MoD) curriculum. The workshop goals included building teacher interests in the MoD, increasing teacher knowledge about the MoD curriculum, increasing and evaluating its use by teachers, disseminating information about it to other teachers, evaluating students’ weather science and safety knowledge, and evaluating students’ and families’ weather safety behavior. Workshop participation produced significant increases in teachers’ knowledge about the MoD curriculum, their general knowledge of weather science and safety, and self-efficacy in teaching their students about severe weather. In the year following the workshops, at least 32 teachers from the workshops delivered 178 MoD lessons to 2,465 students in K–8. In a sample of 291 students whose teachers delivered an MoD lesson on lightning, tornadoes, hurricanes, or floods, students obtained a mean of 60% correct responses on a comprehensive postlesson follow-up test. In a follow-up study with a subsample of 94 parents whose children received instruction from the MoD curriculum, 71% of the families indicated that they had developed safety plans and took additional steps (e.g., assembled safety kits, identified evacuation routes, and/or gathered supplies) to prepare for severe weather. This project is thought to be the first of its kind to demonstrate systematically the effectiveness of weather science and safety education for teachers, their students, and the students’ parents.
Abstract
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
Abstract
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
