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November 28, 2025Remote SensingOpen Access

Physics-Informed Transformer Networks for Interpretable GNSS-R Wind Speed Retrieval

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Authors

ZZZao ZhangJXJing-Ru XuGJGuifei Jing

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Overview

Novel Transformer-GNN model reduces RMSE by 32% in wind speed retrieval, suggesting advances in interpretability and interaction with weather events.

Key Points

  • Reduces RMSE to 1.35 m/s from 1.98 m/s, highlighting improved wind speed accuracy.
  • Utilizes data from Level 1 Version 3.2 across Asian seas, showcasing validation with SFMR observations.
  • Analysis reveals condition-dependent feature attributions, indicating potential coupling mechanisms in weather analysis.
  • Model provides operationally viable inference performance, enhancing interpretability in Earth system AI applications.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/6928f106a65b730b9ea79bbehttps://doi.org/10.3390/rs17233805
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