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A Short-Term Forecast Method for PWV in Greenland Based on the Combination of GAN and Transformer Models
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Shengkai ZHANG1, 2, 3, Xicheng HU1, 2, 3, Li GONG1, 2, 3, Jintao LEI4, Wenhao LI5, Chao MA6, Feng XIAO1, 2, 3, *
Journal of Geodesy and Geodynamics | 2025, 45(9) : 881 - 887
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Journal of Geodesy and Geodynamics | 2025, 45(9): 881-887
A Short-Term Forecast Method for PWV in Greenland Based on the Combination of GAN and Transformer Models
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Shengkai ZHANG1, 2, 3, Xicheng HU1, 2, 3, Li GONG1, 2, 3, Jintao LEI4, Wenhao LI5, Chao MA6, Feng XIAO1, 2, 3, *
Affiliations
  • 1 Chinese Antarctic Center of Surveying and Mapping, Wuhan Universuty, 129 Luoyu Road, Wuhan 430079, China
  • 2 School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
  • 3 Key Laboratory of Polar Environment Monitoring and Public Governance, Ministry of Education, Whuhan University, 129 Luoyu Road, Wuhan 430079, China
  • 4 School of Civil Engineering and Architecture, Guangxi University, 100 East-Daxue Road, Nanning 530004, China
  • 5 School of Geomatics Science and Technology, Nanjing Tech University, 30 South-Puzhu Road, Nanjing 211816, China
  • 6 Shandong Electric Power Engineering Consulting Institute Co Ltd, 106 Minziqian Road, Jinan 250013, China

    • ZHANG Shengkai, professor, majors in polar geodesy and geophysics, E-mail:

About Author:

ZHANG Shengkai, professor, majors in polar geodesy and geophysics, E-mail:

Published: 2025-09-15 doi: 10.14075/j.jgg.2025.02.040
Outline
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Based on the PWV time series data inverted from GPS during 2010-2018 and the PWV data calculated from the ERA5 reanalysis data for the same period, a combination of the generative adversarial networks(GAN) model and the Transformer neural network model in deep learning was employed to achieve short-term forecasting of PWV in Greenland using GPS-PWV data. The prediction results were evaluated using the ERA5 data from 2019. The results show that the model performed well in most areas, with a root mean square error(RMSE) better than 4.5 mm and a correlation coefficient greater than 0.7. The correlation coefficients in spring, autumn, and winter were all above 0.5, while in summer, the correlation coefficients on some dates were slightly lower due to severe weather changes. This method is capable of predicting the spatial distribution and temporal variations of PWV in Greenland.

generative adversarial networks(GAN)  /  Transformer  /  GPS  /  Greenland  /  PWV  /  short-term forecast
张胜凯, 胡希成, 龚力, 雷锦韬, 李文浩, 马超, 肖峰. 基于GAN和Transformer模型组合的格陵兰地区PWV短时预报方法. 大地测量与地球动力学, 2025 , 45 (9) : 881 -887 . DOI: 10.14075/j.jgg.2025.02.040
Shengkai ZHANG, Xicheng HU, Li GONG, Jintao LEI, Wenhao LI, Chao MA, Feng XIAO. A Short-Term Forecast Method for PWV in Greenland Based on the Combination of GAN and Transformer Models[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (9) : 881 -887 . DOI: 10.14075/j.jgg.2025.02.040
Funding
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  • National Key Research and Development Program(2023YFC2809103)
  • Fundamental Research Funds for the Central Universities(2042022kf1204)
  • Fundamental Research Funds for the Central Universities(2042023gf0012)
  • Fundamental Research Funds for the Central Universities(2042022KF1069)
  • Open Fund of State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS(SKLGED2023-2-6)
  • Natural Science Foundation of Hubei Province(2022CFB081)
Appendix
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Year 2025 volume 45 Issue 9
33
13
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Article Info
doi: 10.14075/j.jgg.2025.02.040
  • Receive Date：2025-02-19
  • Online Date：2026-10-29
  • Published：2025-09-15
Article Data
Affiliations
History
  • Received：2025-02-19
Funding
National Key Research and Development Program(2023YFC2809103)
Fundamental Research Funds for the Central Universities(2042022kf1204)
Fundamental Research Funds for the Central Universities(2042023gf0012)
Fundamental Research Funds for the Central Universities(2042022KF1069)
Open Fund of State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS(SKLGED2023-2-6)
Natural Science Foundation of Hubei Province(2022CFB081)
Affiliations
    1 Chinese Antarctic Center of Surveying and Mapping, Wuhan Universuty, 129 Luoyu Road, Wuhan 430079, China
    2 School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    3 Key Laboratory of Polar Environment Monitoring and Public Governance, Ministry of Education, Whuhan University, 129 Luoyu Road, Wuhan 430079, China
    4 School of Civil Engineering and Architecture, Guangxi University, 100 East-Daxue Road, Nanning 530004, China
    5 School of Geomatics Science and Technology, Nanjing Tech University, 30 South-Puzhu Road, Nanjing 211816, China
    6 Shandong Electric Power Engineering Consulting Institute Co Ltd, 106 Minziqian Road, Jinan 250013, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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