
Can deep learning beat numerical weather prediction?
Author(s) -
Martin Schultz,
Clara Betancourt,
Bing Gong,
Felix Kleinert,
Michael Langguth,
Lukas Hubert Leufen,
Amirpasha Mozaffari,
Scarlet Stadtler
Publication year - 2021
Publication title -
philosophical transactions - royal society. mathematical, physical and engineering sciences/philosophical transactions - royal society. mathematical, physical and engineering sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.074
H-Index - 169
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2020.0097
Subject(s) - numerical weather prediction , artificial intelligence , computer science , deep learning , artificial neural network , big data , machine learning , data assimilation , field (mathematics) , weather forecasting , workflow , weather prediction , weather patterns , data science , climate change , meteorology , data mining , geology , oceanography , physics , mathematics , database , pure mathematics
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’.