Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models
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Abstract (arXiv)
The rapid advancement of artificial intelligence technologies, particularly in recent years, has led to the emergence of several large parameter artificial intelligence weather forecast models. These models represent a significant breakthrough, overcoming the limitations of traditional numerical weather prediction models and indicating the emergence of profound potential tools for atmosphere-ocean forecasts. This study explores the evolution of these advanced artificial intelligence forecast models, and based on the identified commonalities, proposes the "Three Large Rules" to measure their development. We discuss the potential of artificial intelligence in revolutionizing numerical weather prediction, and briefly outlining the underlying reasons for its great potential. While acknowledging the high accuracy, computational efficiency, and ease of deployment of large artificial intelligence forecast models, we also emphasize the irreplaceable values of traditional numerical forecasts and explore the challenges in the future development of large-scale artificial intelligence atmosphere-ocean forecast models. We believe that the optimal future of atmosphere-ocean weather forecast lies in achieving a seamless integration of artificial intelligence and traditional numerical models. Such a synthesis is anticipated to offer a more advanced and reliable approach for improved atmosphere-ocean forecasts. Additionally, we illustrate how forecasters can adapt and leverage the advanced artificial intelligence model through an example by building a large artificial intelligence model for global ocean wave forecast.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 67 references that are in the map. First posted on arXiv 30 Jan 2024.
Builds on
22 of 67 references.
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10 more
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Citing papers per year: 2024: 3; 2025: 2.
5 papers.
- 2024FuXi-2.0: Advancing machine learning weather forecasting model for practical applicationscited by 11
- 2024Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Modelcited by 9
- 2024FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmospherecited by 8
- 2025ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecastingcited by 8
- 2025FlowCast-ODE: Continuous Hourly Weather Forecasting with Dynamic Flow Matching and ODE Solvercited by 1
- 2025ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting10 Oct 2025
- 2025FlowCast-ODE: Continuous Hourly Weather Forecasting with Dynamic Flow Matching and ODE Solver18 Sep 2025
- 2024FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere15 Nov 2024
- 2024FuXi-2.0: Advancing machine learning weather forecasting model for practical applications11 Sep 2024
- 2024Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model16 Feb 2024