Atmosphere· 2026Q2
Enhancing Observation Point Analysis for Atmospheric State Estimation Using Self-Supervised Graph Neural Networks
- 26citations
- Q2SCImago
- 2026year
Short summary
A novel self-supervised graph neural network (GNN) model improves atmospheric state estimation by representing multi-source data as graphs and outperforms existing methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Multi-source meteorological data (ground, upper-air, satellite) are integrated and represented as graph structures.
- A graph neural network model is constructed and pre-trained using self-supervised methods.
- The proposed model shows superior performance in atmospheric state estimation compared to existing methods.
- Gradient-based interpretability analysis is used to evaluate the importance of observed nodes.
AI-generated from the title and abstract; the full text is not read.
Abstract
Atmospheric state estimation is an important part of weather forecasting, and its accuracy determines the accuracy of the forecasting results. Traditional methods for atmospheric state estimation mainly rely on assimilation systems, using physical models and dynamic equations to predict the atmospheric state. However, these methods have certain limitations when dealing with large-scale meteorological data and complex meteorological phenomena. In order to solve the above problems, this study first integrates and processes data from multiple datasets including ground, upper-air, satellite, and atmospheric state, representing these data as graph structures. Secondly, a graph neural network-based network model is constructed, which is pre-trained using self-supervised methods and fine-tuned for specific tasks. Finally, gradient-based interpretability analysis is used to evaluate the importance of observed nodes. The experimental results show that both the atmospheric state estimation model and the interpretable analysis method proposed in this paper are superior to some existing representative models and methods.
The authors' abstract, as published at the source. Atmosphere, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Atmospheric Science
Atmospheric ScienceEarth and Planetary Sciences