首页|Improving global weather and ocean wave forecast with large artificial intelligence models

Improving global weather and ocean wave forecast with large artificial intelligence models

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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"for large weather forecast models:a large number of parameters,a large number of predictands,and large potential applications.We discuss the capacity of artificial intelligence to revolutionize numerical weather prediction,briefly outlining the underlying reasons for the significant im-provement in weather forecasting.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.Finally,we illustrate how forecasters can leverage the large weather forecast models through an example by building an artificial intelligence model for global ocean wave forecast.

Numerical weather predictionDeep learningLarge AI weather forecast modelsGlobal ocean wave forecast

Fenghua LING、Lin OUYANG、Boufeniza Redouane LARBI、Jing-Jia LUO、Tao HAN、Xiaohui ZHONG、Lei BAI

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Institute for Climate and Application Research(ICAR)/CIC-FEMD/KLME/ILCEC,School of Future Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China

Shanghai AI Laboratory,Shanghai 200232,China

Ganzhou Meteorological Bureau,Ganzhou 341000,China

The Hong Kong University of Science and Technology,Hong Kong 999077,China

Artificial Intelligence Innovation and Incubation Institute of Fudan University,Shanghai 201203,China

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2024

10.1007/s11430-024-1452-3

中国科学:地球科学(英文版)
中国科学院

中国科学:地球科学(英文版)

影响因子：1.002
ISSN：1674-7313
年,卷(期)：2024.67(12)