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Unsupervised learning on U.S. weather forecast performance

Author

Listed:
  • Chuyuan Lin

    (Simon Fraser University)

  • Ying Yu

    (Simon Fraser University)

  • Lucas Y. Wu

    (Simon Fraser University)

  • Jiguo Cao

    (Simon Fraser University)

Abstract

Nowadays, climate events and weather predictions have a huge impact on human activities. To understand the accuracy of weather prediction, we applied the functional principal component analysis (FPCA) method to investigate the main pattern of variance within the U.S. weather prediction error over a period of 3 years. We further grouped the states in the U.S. based on their similarity in weather forecast performance using two types of functional clustering approaches: the filtering method and the model-based method. The strengths and weaknesses of each clustering method were detected through the simulation studies. Then, the clustering approaches were applied to U.S. weather data from 2014 to 2017. Through clustering, cluster-specific patterns were visually detected, and the cluster-to-cluster differences were quantified in order to identify the most and least predictable U.S. states.

Suggested Citation

  • Chuyuan Lin & Ying Yu & Lucas Y. Wu & Jiguo Cao, 2023. "Unsupervised learning on U.S. weather forecast performance," Computational Statistics, Springer, vol. 38(3), pages 1193-1213, September.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:3:d:10.1007_s00180-023-01340-w
    DOI: 10.1007/s00180-023-01340-w
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    References listed on IDEAS

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    3. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
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