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Predicting weather forecast uncertainty with machine learning
Stockholm University, Faculty of Science, Department of Meteorology .
Stockholm University, Faculty of Science, Department of Meteorology .
Number of Authors: 22018 (English)In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 144, no 717, p. 2830-2841Article in journal (Refereed) Published
Abstract [en]

Weather forecasts are inherently uncertain. Therefore, for many applications forecasts are only considered valuable if an uncertainty estimate can be assigned to them. Currently, the best method to provide a confidence estimate for individual forecasts is to produce an ensemble of numerical weather simulations, which is computationally very expensive. Here, we assess whether machine learning techniques can provide an alternative approach to predict the uncertainty of a weather forecast given the large-scale atmospheric state at initialization. We propose a method based on deep learning with artificial convolutional neural networks that is trained on past weather forecasts. Given a new weather situation, it assigns a scalar value of confidence to medium-range forecasts initialized from the said atmospheric state, indicating whether the predictability is higher or lower than usual for the time of the year. While our method has a lower skill than ensemble weather forecast models in predicting forecast uncertainty, it is computationally very efficient and outperforms a range of alternative methods that do not involve performing numerical forecasts. This shows that it is possible to use machine learning in order to estimate future forecast uncertainty from past forecasts. The main constraint in the performance of our method seems to be the number of past forecasts available for training the machine learning algorithm.

Place, publisher, year, edition, pages
2018. Vol. 144, no 717, p. 2830-2841
Keywords [en]
ensembles, machine learning, statistical methods, weather forecasts
National Category
Meteorology and Atmospheric Sciences
Identifiers
URN: urn:nbn:se:su:diva-165737DOI: 10.1002/qj.3410ISI: 000455586500029OAI: oai:DiVA.org:su-165737DiVA, id: diva2:1286236
Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-02-06Bibliographically approved

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Scher, SebastianMessori, Gabriele
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
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Output format
  • html
  • text
  • asciidoc
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