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A spatial extension of weather forecasts

Author

Listed:
  • Benjamin Schweitzer

    (Miami University)

  • Robert C. Garrett

    (Miami University)

  • Nichole Rook

    (Miami University)

  • Thomas J. Fisher

    (Miami University)

Abstract

One of the more popular usages of predictive modeling is in the forecasting of weather. We use machine learning techniques to spatially extend provided forecasts to sites across the continental United States. The forecasts and observed weather for 113 sites across the United States (2014–2017) were used, along with supplementary data on observed weather from the National Oceanic and Atmospheric Administration National Climatic Data Center. Based on the spatially extended forecasts, visual displays are created to analyze the prediction accuracy of the forecasts. Our results allow for an in-depth exploration into the accuracy of our new weather forecasts across the nation.

Suggested Citation

  • Benjamin Schweitzer & Robert C. Garrett & Nichole Rook & Thomas J. Fisher, 2023. "A spatial extension of weather forecasts," Computational Statistics, Springer, vol. 38(3), pages 1157-1171, September.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:3:d:10.1007_s00180-023-01336-6
    DOI: 10.1007/s00180-023-01336-6
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
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    Cited by:

    1. Mine Cetinkaya-Rundel & Wendy Martinez, 2023. "The 2018 data challenge expo of the American statistical association," Computational Statistics, Springer, vol. 38(3), pages 1117-1122, September.

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