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DGFormer: a physics-guided station level weather forecasting model with dynamic spatial-temporal graph neural network

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Abstract

In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal Graph Neural Networks (STGNN). However, it has large prediction errors as a result of the inherent non-linearities and the influence of dynamic spatio-temporal auto-correlation. Using a continuously-varying graph topology chronologically, while embedding domain knowledge to enforce validity, can effectively resolve the issue, but the implementation of such concept constitutes an interdisciplinary challenge for researchers. A Dynamic Graph Former (DGFormer) model is proposed to address this challenge. It combines a topology learner through a deep generative layer with domain knowledge enhancement inserted into the STGNN structure, where the derived physics-guided method allows for an efficient integration with the earth system. For capture of the optimal topology, we merge a node-embedding-based similarity metric learning and the superposition principle as physical assistants into the dynamic graph module. We evaluate our model with a real-world weather dataset on short-term (12 hours) and medium-range (360 hours) prediction tasks. DGFormer achieves outstanding performance with obvious improvements by up to 34.84% at short-term prediction and by up to 23.25% at medium-range prediction compared with the state-of-the-art methods. We also conducted detailed analyses for cities in three regions and visualized the dynamic graph, revealing the characteristics, advantages, and graph visualization of our model.

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Data Availability

We shared our code and data. Readers can find our model at https://github.com/xzwbsz/DGFormer. The dataset was partly sampled from WeatherBench [60] and partly provided by National Meteorological Center of China.

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Funding

This work was supported by the Natural Science Foundation of Jilin Province (Grant 20230101062JC), the National Key Research and Development Plan of China (Grant 2017YFC1502306), and the National Natural Science Foundation of China (No. 42175052, No. 61902143, No. 62272190, and No. U19A2061).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Qianjin Street, Changchun, 130012, Jilin, China

    Zhewen Xu, Xiaohui Wei, Jieyun Hao, Junze Han, Hongliang Li, Zijian Li, Dongyuan Tian & Nong Zhang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Qianjin Street, Changchun, 130012, Jilin, China

    Xiaohui Wei & Hongliang Li

  3. China Meteorological Administration Key Laboratory for weather prediction Studies, National Climate Center, South Zhongguancun Street, Beijing, 100081, Beijing, China

    Changzheng Liu

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  1. Zhewen Xu
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  2. Xiaohui Wei
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Contributions

Z. Xu. (First Author) : Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft; X. Wei: Resources, Supervision; J. Hao: Data Curation, Software, Validation; J. Han: Data Curation, Software; H. Li (Corresponding Author): Resources, Supervisio, Visualization, Writing - Review and Editing; C. Liu: Resources, Supervision; Z. Li: Visualization, Investigation; D. Tian: Conceptualization, Investigation; N. Zhang: Software, Validation; All authors have reviewed the manuscript. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We understand that the Corresponding Author is the sole contact for the Editorial process. He/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.

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Correspondence to Hongliang Li.

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Xu, Z., Wei, X., Hao, J. et al. DGFormer: a physics-guided station level weather forecasting model with dynamic spatial-temporal graph neural network. Geoinformatica (2024). https://doi.org/10.1007/s10707-024-00511-1

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