Liang et al. (2026) Refining Clear-Sky AI Radiative Transfer Model Forward Predictions and Jacobian Accuracy for ATMS Using a ResNet and Physical Constraints

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Xingming Liang, Quanhua Liu, Christopher Grassotti
• DOI: 10.1109/tgrs.2026.3665759

Research Groups

[Not available in the provided text.]

Short Summary

This paper aims to improve the accuracy of clear-sky AI radiative transfer model forward predictions and Jacobian calculations for the Advanced Technology Microwave Sounder (ATMS) by utilizing a ResNet architecture and incorporating physical constraints.

Objective

• To refine the accuracy of clear-sky AI radiative transfer model forward predictions and Jacobian calculations for ATMS.
• To investigate the application of a ResNet architecture and physical constraints to achieve this refinement.

Study Configuration

• Spatial Scale: [Not specified in the provided text.]
• Temporal Scale: [Not specified in the provided text.]

Methodology and Data

• Models used: AI Radiative Transfer Model, ResNet.
• Data sources: Advanced Technology Microwave Sounder (ATMS) data.

Main Results

[Not available in the provided text.]

Contributions

[Not available in the provided text.]

Funding

[Not available in the provided text.]

Citation

@article{Liang2026Refining,
  author = {Liang, Xingming and Liu, Quanhua and Grassotti, Christopher},
  title = {Refining Clear-Sky AI Radiative Transfer Model Forward Predictions and Jacobian Accuracy for ATMS Using a ResNet and Physical Constraints},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3665759},
  url = {https://doi.org/10.1109/tgrs.2026.3665759}
}