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An exploration of National Weather Service daily forecasts using R Shiny

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Abstract

Weather forecasts often affect daily lives of billions of people globally. Accurate forecasts can help combat and effectively mitigate damage caused by extreme weather. Alternatively, faulty forecasts can consequently lead to unnecessary financial investments and a waste of resources. Our work explores what is the extent of variability in errors of the National Weather Service predictions as observed in 113 cities in the United States between July 1, 2014 and September 1, 2017 and attempts to model the distribution of errors. Simultaneously, we deliver an interactive tool for future researchers to explore the actual and forecast weather data as well as expose hidden patterns in the data.

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Notes

  1. JSM-2018-Weather-App: https://github.com/JiananH/JSM-2018-Weather-App.

References

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki BF (eds) Second international symposium on information theory. Academiai Kiado, Budapest

  • Box G, Jenkins G, Reinsel G (1970) Time series analysis: forecast and control. Wiley, Hoboken

    MATH  Google Scholar 

  • Chang W, Cheng J, Xie Y, Mcpherson JEA (2019)Shiny v1.4.0: web application framework for R. R package version 3.0.2 and up

  • Cox D (1969) Analysis of binary data. Chapman and Hall, London

    Google Scholar 

  • Farrand J (1990) Weather. Stewart, Tabori & Chang, New York

    Google Scholar 

  • Farrand J (1991) From gods to satellites. Weatherwise 44:30–36

    Article  Google Scholar 

  • Hyndman RJ (2006) Another look at forecast-accuracy metrics for intermittent demand. Foresight 4:43–46

    Google Scholar 

  • Lorenz EU (1972) Predictability: Does the flap of a butterfly’s wings in brazil set off a tornado in texas? Paper delivered at the American Association for the Advancement of Science, Washington

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Authors and Affiliations

  1. Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, USA

    Dooti Roy, Jianan Hui & Junxian Geng

  2. Bentley University, Waltham, USA

    Gregory Vaughan

Authors
  1. Dooti Roy
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  2. Gregory Vaughan
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  3. Jianan Hui
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  4. Junxian Geng
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Correspondence to Dooti Roy.

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Roy, D., Vaughan, G., Hui, J. et al. An exploration of National Weather Service daily forecasts using R Shiny. Comput Stat 38, 1173–1191 (2023). https://doi.org/10.1007/s00180-023-01341-9

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  • DOI: https://doi.org/10.1007/s00180-023-01341-9

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