Journal Article

FZJ-2021-01034

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Can deep learning beat numerical weather prediction?

Schultz, M. (Corresponding author)FZJ* ; Betancourt, C.FZJ* ; Gong, B.FZJ* ; Kleinert, F.FZJ* ; Langguth, M.FZJ* ; Leufen, L. H.FZJ* ; Mozaffari, A.FZJ* ; Stadtler, S.FZJ*

2021
Royal Society London

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Please use a persistent id in citations: http://hdl.handle.net/2128/27491  doi:10.1098/rsta.2020.0097

Abstract: 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. IntelliAQ - Artificial Intelligence for Air Quality (787576) (787576)
3. Earth System Data Exploration (ESDE) (ESDE)

Appears in the scientific report 2021

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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