Can deep learning beat numerical weather prediction?

The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather fo...

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Vydané v:Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Ročník 379; číslo 2194; s. 20200097
Hlavní autori: Schultz, M G, Betancourt, C, Gong, B, Kleinert, F, Langguth, M, Leufen, L H, Mozaffari, A, Stadtler, S
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 05.04.2021
Predmet:
spatiotemporal pattern recognition
deep learning
numerical weather prediction
machine learning
weather AI
ISSN:1471-2962, 1471-2962
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Popis
Shrnutí:The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach. This article is part of the theme issue 'Machine learning for weather and climate modelling'.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1471-2962
1471-2962
DOI:10.1098/rsta.2020.0097