Let’s talk about the weather: a cluster-based approach to weather forecast accuracy

Jill F. Lundell (), Brennan Bean () and Jürgen Symanzik ()
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Jill F. Lundell: Department of Mathematics and Statistics Utah State University
Brennan Bean: Department of Mathematics and Statistics Utah State University
Jürgen Symanzik: Department of Mathematics and Statistics Utah State University

Computational Statistics, 2023, vol. 38, issue 3, No 3, 1135-1155

Abstract: Abstract Improved understanding of characteristics related to weather forecast accuracy in the United States may help meteorologists develop more accurate predictions and may help Americans better interpret their daily weather forecasts. This article examines how spatio-temporal characteristics across the United States relate to forecast accuracy. We cluster the United States into six weather regions based on weather and geographic characteristics and analyze the patterns in forecast accuracy within each weather region. We then explore the relationship between climate characteristics and forecast accuracy within these weather regions. We conclude that patterns in forecast errors are closely related to the unique climates that characterize each region.

Keywords: Climate; Clustering; Data Expo 2018; Glyph plots; Random forests; Visualization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-023-01339-3

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