Can deep learning beat numerical weather prediction?
By Martin G. Schultz, Clara Betancourt, Bing Gong, Felix Kleinert, Michael Langguth, Lukas Hubert Leufen, Amirpasha Mozaffari, Scarlet Stadtler. Philosophical Transactions of the Royal Society A, 2021
498 citations recorded by the source corpus.
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’.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/c8fc3da2d6a0a7f05e716fa3c06fc7c813c9c049
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Citations in the corpus
- ClimaX: A foundation model for weather and climate (2023, unknown): “…of atmospheric variables, especially for meteorological applications (Grover et al., 2015; Dueben & Bauer, 2018; Weber et al., 2020; Scher & Messori, 2019; Scher, 2018; Kashinath et al., 2021; Schultz et al., 2021; Reichstein et al., 2019; Huntingford et al., 2019; Schneider et al., 2017).”
- FuXi: a cascade machine learning forecasting system for 15-day global weather forecast (2023, background): “ML-based weather forecasting systems have several advantages over NWP models, including faster speeds and the potential to provide higher accuracy than uncalibrated NWP models due to training with reanalysis data [4].”
- FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (2022, methodology): “By training on reanalysis data or observations, data-driven models can avoid limitations that exist in NWP models [Schultz et al., 2021, Balaji, 2021], such as biases in convection parameterization schemes that strongly affect precipitation forecasts.”