Abstract
The research in this article examines the emotional associations people have to common weather words and to selected terms that appear in weather communications (e.g., severe thunderstorm warning). A sample of 420 university students provided ratings for each term along four dimensions: 1. Valence (unhappy vs. happy), 2. Arousal (calm vs. excited), 3. Dominance (in control/dominant vs. controlled/passive), and 4. Surprise (unsurprising/predictable vs. surprising/unpredictable). The results of this research provide descriptive statistical data for the 141 weather words along the four dimensions. The author also examined the correlations of the four dimensions across the terms and observed a high degree of association between the rated arousal and surprise characteristics of terms. In addition, the results revealed the clustering of weather words according to shared similarities across the four affective dimensions (illustrating affective-based synonymy). The results of the research are significant because they reveal a deeper understanding of the subjective and emotional experiences of the atmosphere that people may have when describing the weather of a place. Similarly, the normative data from this research may be used in the analysis of weather- or climate-based communications to characterize the emotional significance or impact of a message.
Highlights
Many meteorological, biometeorological, and climatic products for end users are made available in the form of texts or narratives
The research in this article examined the affective properties of weather words that appear in a variety of English-language weather products, both online and in print
The results of the research revealed the ways that the weather words differed with respect to emotion
Summary
Biometeorological, and climatic products for end users are made available in the form of texts or narratives. Local weather forecasts may consist of several sentences that combine both verbal descriptive terms along with quantitative values for forecasted variables [1]. An example from the National Weather Service in the United States may be: Sunny and clear today with a high of 76. Cloudy in the late afternoon with a 10% chance of widely scattered thunderstorms. Cloudy tonight with a 30% chance of rain.
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