Cao et al. (2026) AMV-STECNet: A Deep Learning Framework for Spatiotemporal Error Correction of Atmospheric Motion Vectors to Enhance Numerical Weather Prediction

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Hang Cao, Hongze Leng, Yan Yan, Jun Zhao, Yudi Liu, Lilan Huang, Xingyu Chai, Baoxu Li
• DOI: 10.1109/tgrs.2026.3652160

Research Groups

[Not available in the provided text.]

Short Summary

This paper introduces AMV-STECNet, a deep learning framework designed for spatiotemporal error correction of Atmospheric Motion Vectors (AMVs) to enhance Numerical Weather Prediction (NWP).

Objective

• To develop and evaluate AMV-STECNet, a deep learning framework for spatiotemporal error correction of Atmospheric Motion Vectors (AMVs), with the aim of enhancing Numerical Weather Prediction (NWP).

Study Configuration

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

Methodology and Data

• Models used: A deep learning framework named AMV-STECNet. (Specific architectural details are not provided in the given text.)
• Data sources: Atmospheric Motion Vectors (AMVs). (Specific observation or satellite sources are not provided in the given text.)

Main Results

[Not available in the provided text.]

Contributions

[Not available in the provided text.]

Funding

[Not available in the provided text.]

Citation

@article{Cao2026AMVSTECNet,
  author = {Cao, Hang and Leng, Hongze and Yan, Yan and Zhao, Jun and Liu, Yudi and Huang, Lilan and Chai, Xingyu and Li, Baoxu},
  title = {AMV-STECNet: A Deep Learning Framework for Spatiotemporal Error Correction of Atmospheric Motion Vectors to Enhance Numerical Weather Prediction},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3652160},
  url = {https://doi.org/10.1109/tgrs.2026.3652160}
}