Physics-Guided Multimodal Transformers are the Necessary Foundation for the Next Generation of Meteorological Science
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915758238334976 |
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| author | Han, Jing Chen, Hanting Han, Kai Huang, Xiaomeng Xu, Wenjun Tao, Dacheng Zhang, Ping |
| author_facet | Han, Jing Chen, Hanting Han, Kai Huang, Xiaomeng Xu, Wenjun Tao, Dacheng Zhang, Ping |
| contents | This position paper argues that the next generation of artificial intelligence in meteorological and climate sciences must transition from fragmented hybrid heuristics toward a unified paradigm of physics-guided multimodal transformers. While purely data-driven models have achieved significant gains in predictive accuracy, they often treat atmospheric processes as mere visual patterns, frequently producing results that lack scientific consistency or violate fundamental physical laws. We contend that current ``hybrid'' attempts to bridge this gap remain ad-hoc and struggle to scale across the heterogeneous nature of meteorological data ranging from satellite imagery to sparse sensor measurements. We argue that the transformer architecture, through its inherent capacity for cross-modal alignment, provides the only viable foundation for a systematic integration of domain knowledge via physical constraint embedding and physics-informed loss functions. By advocating for this unified architectural shift, we aim to steer the community away from ``black-box'' curve fitting and toward AI systems that are inherently falsifiable, scientifically grounded, and robust enough to address the existential challenges of extreme weather and climate change. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Guided Multimodal Transformers are the Necessary Foundation for the Next Generation of Meteorological Science Han, Jing Chen, Hanting Han, Kai Huang, Xiaomeng Xu, Wenjun Tao, Dacheng Zhang, Ping Machine Learning Artificial Intelligence This position paper argues that the next generation of artificial intelligence in meteorological and climate sciences must transition from fragmented hybrid heuristics toward a unified paradigm of physics-guided multimodal transformers. While purely data-driven models have achieved significant gains in predictive accuracy, they often treat atmospheric processes as mere visual patterns, frequently producing results that lack scientific consistency or violate fundamental physical laws. We contend that current ``hybrid'' attempts to bridge this gap remain ad-hoc and struggle to scale across the heterogeneous nature of meteorological data ranging from satellite imagery to sparse sensor measurements. We argue that the transformer architecture, through its inherent capacity for cross-modal alignment, provides the only viable foundation for a systematic integration of domain knowledge via physical constraint embedding and physics-informed loss functions. By advocating for this unified architectural shift, we aim to steer the community away from ``black-box'' curve fitting and toward AI systems that are inherently falsifiable, scientifically grounded, and robust enough to address the existential challenges of extreme weather and climate change. |
| title | Physics-Guided Multimodal Transformers are the Necessary Foundation for the Next Generation of Meteorological Science |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.14174 |