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Bias Correction Method for PWV of AI Weather Models Based on GNSS PWV and LSTM
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Shirun YE1, Maijin LIN2, Wenyi YANG1, Yanping LIU1, Yukun XIAO1, Shaofeng XIE1, *
Journal of Geodesy and Geodynamics | 2026, 46(7) : 825 - 833
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Journal of Geodesy and Geodynamics | 2026, 46(7): 825-833
Bias Correction Method for PWV of AI Weather Models Based on GNSS PWV and LSTM
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Shirun YE1, Maijin LIN2, Wenyi YANG1, Yanping LIU1, Yukun XIAO1, Shaofeng XIE1, *
Affiliations
  • 1 College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
  • 2 College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
Published: 2026-07-15 doi: 10.14075/j.jgg.2025.12.414
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Based on PWV data from 240 GNSS stations of the Crustal Movement Observation Network of China from 2017 to 2019, combined with PWV data retrieved from two AI-based meteorological models, Fengwu and Pangu (hereafter referred to as FW PWV and PG PWV), this study investigates bias correction methods for AI-derived PWV. First, using GNSS PWV as a reference, the accuracy of FW PWV and PG PWV were validated. Subsequently, to improve the accuracy of the two AI-based models, a bias correction method was constructed using a long short-term memory (LSTM) network. The input features included longitude, latitude, elevation, day of year, and AI-derived PWV from 2017 to 2018 at GNSS stations, while the output feature was the difference between FW PWV or PG PWV and GNSS PWV. The method was then applied to correct the AI-derived PWV for 2019. The results show that the mean bias and RMSE of FW PWV and PG PWV are -0.01 mm, 2.88 mm and 0.28 mm, 3.04 mm, respectively. After correction, the RMSE of FW PWVL and PG PWVL decreased to 2.29 mm and 2.38 mm, with improvement rates of 20.48% and 21.71%, respectively. The reliability of the corrected results was further validated using radiosonde PWV data from 2019. The bias correction method effectively reduced RMSE across all months, with monthly RMSE values of FW PWVL and PG PWVL concentrated between 1.4 mm and 3.4 mm, and an average RMSE improvement rate of 22.30% in summer. For PG PWVL, the absolute bias in the elevation range of 0 to 1.5 km decreased by 0.45 mm, 0.46 mm, and 0.32 mm, respectively, while the RMSE improvement rate reached 23.55% in the 1.5 to 2.0 km range. FW PWVL performed best in regions above 2 km elevation, with an absolute bias reduction of 0.23 mm and an RMSE improvement rate of 29.50%.

precipitable water vapor  /  Pangu-weather model  /  Fengwu-weather model  /  LSTM  /  GNSS
叶世润, 林买金, 杨文易, 刘艳平, 肖玉坤, 谢劭峰. 基于GNSS PWV和LSTM的AI气象模型PWV偏差校正方法. 大地测量与地球动力学, 2026 , 46 (7) : 825 -833 . DOI: 10.14075/j.jgg.2025.12.414
Shirun YE, Maijin LIN, Wenyi YANG, Yanping LIU, Yukun XIAO, Shaofeng XIE. Bias Correction Method for PWV of AI Weather Models Based on GNSS PWV and LSTM[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (7) : 825 -833 . DOI: 10.14075/j.jgg.2025.12.414
Year 2026 volume 46 Issue 7
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10
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doi: 10.14075/j.jgg.2025.12.414
  • Receive Date:2025-12-02
  • Online Date:2026-10-30
  • Published:2026-07-15
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  • Received:2025-12-02
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    1 College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
    2 College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, 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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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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