This article explores the evolution and potential of large artificial intelligence (AI) models in transforming global weather and ocean wave forecasting. It highlights the emergence of several influential large-parameter AI weather forecast models, such as FourCastNet, Pangu-Weather, GraphCast, FengWu, and FuXi, which have demonstrated remarkable improvements in forecast accuracy, computational efficiency, and scalability compared to traditional numerical weather prediction (NWP) models.
The authors propose the "Three Large Rules" to define the key characteristics of these large AI weather forecast models: large parameter count, large number of predictands, and large scalability and downstream applicability. These models excel at capturing intricate atmospheric patterns, generating detailed forecasts for a wide range of meteorological variables, and enabling the development of specialized models for various applications.
The superior performance of large AI models is attributed to their ability to effectively leverage historical data, tackle complex nonlinear interactions, and mitigate error accumulation during long-lead predictions. Additionally, the extremely low computational cost of these models opens up new possibilities for high-resolution ensemble forecasting and user-friendly deployment, making weather information more accessible beyond large operational centers.
While acknowledging the transformative potential of large AI models, the article also emphasizes the irreplaceable value of traditional NWP models and the need for a balanced integration of AI and physics-based approaches. Challenges such as data quality control, ensemble prediction, and the incorporation of physical principles into AI models are discussed, highlighting the importance of a collaborative effort between data scientists and weather forecasters to develop more effective hybrid solutions.
The article further demonstrates the application of a large AI model, specifically a Vision Transformer (ViT) model, for global ocean wave forecasting. The ViT model exhibits promising performance in predicting wave characteristics, showcasing the potential of leveraging large AI models for various weather and ocean-related applications.
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by Fenghua Ling... at arxiv.org 04-22-2024
https://arxiv.org/pdf/2401.16669.pdfDeeper Inquiries