Li et al. (2026) Physics-Constrained Adapter Tuning of Meteorological Foundation Models for Global SST Forecasting

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Ying Li, Yuting Su, Dan Song, Zhu Liu, Lanjun Wang, Zhiqiang Wei, An-An Liu
• DOI: 10.1109/tgrs.2026.3676064

Research Groups

[Not available from provided text]

Short Summary

[Not available from provided text]

Objective

• To improve global sea surface temperature (SST) forecasting by applying physics-constrained adapter tuning to meteorological foundation models.

Study Configuration

• Spatial Scale: [Not available from provided text]
• Temporal Scale: [Not available from provided text]

Methodology and Data

• Models used: Meteorological Foundation Models, Physics-Constrained Adapter Tuning
• Data sources: [Not available from provided text]

Main Results

• [Not available from provided text]

Contributions

• [Not available from provided text]

Funding

• [Not available from provided text]

Citation

@article{Li2026PhysicsConstrained,
  author = {Li, Ying and Su, Yuting and Song, Dan and Liu, Zhu and Wang, Lanjun and Wei, Zhiqiang and Liu, An-An},
  title = {Physics-Constrained Adapter Tuning of Meteorological Foundation Models for Global SST Forecasting},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3676064},
  url = {https://doi.org/10.1109/tgrs.2026.3676064}
}