Home Journals Articles Updates About
CN
image
收藏切换
Study on Precipitation Data Quality Control Method Based on Multi-source Meteorological Data and Machine Learning
收藏切换
Shi-ying LI1, 2, 3, 4, Xiao-long HUANG1, 2, 3, 4, Lan-xin LUO5, Wei WU1, 2, 3, Chun-yue LV1, 2, 3
Water Resources and Power | 2026, 44(1) : 15 - 19
Less
收藏切换
Water Resources and Power | 2026, 44(1): 15-19
Study on Precipitation Data Quality Control Method Based on Multi-source Meteorological Data and Machine Learning
Full
Shi-ying LI1, 2, 3, 4, Xiao-long HUANG1, 2, 3, 4, Lan-xin LUO5, Wei WU1, 2, 3, Chun-yue LV1, 2, 3
Affiliations
  • 1.Sichuan Meteorological Observation and Data Center, Chengdu 610072, China
  • 2.Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
  • 3.Southwest Sichuan (Ya'an) Rainstorm Laboratory, Ya’an 625099, China
  • 4.Wenjiang National Climatology Observatory, Chengdu 611130, China
  • 5.National Meteorological Information Center, Beijing 100081, China
Published: 2026-01-25 doi: 10.20040/j.cnki.1000-7709.2026.20250376
Outline
收藏切换

To enhance the application value of precipitation observation data in public meteorological services, this study investigates a quality control method for precipitation data based on multi-source meteorological data and machine learning models. Based on the meteorological station data, multi-source integrated analysis products, meteorological radar, and satellite products, as well as the XGBoost model, a multi-source collaborative quality control method for precipitation data was developd by comprehensive scoring. The accuracy of this method was evaluated using the artificial error implantation technique. The results indicate that when random precipitation anomalies are implanted, the detection rate of erroneous data is 0.85; When errors are implanted at different precipitation levels, the detection rate of erroneous data is 0.79. After collaborative quality control, the average error between the precipitation data to be tested and neighboring meteorological stations is reduced by 15%, and the correlation coefficient is increased to 0.7-1.0. Therefore, this method can effectively identify suspicious precipitation data, which can provide strong data support for meteorological disaster prevention and mitigation.

precipitation  /  radar products  /  satellite products  /  machine learning  /  quality control
李施颖, 黄晓龙, 罗岚心, 吴薇, 吕纯月. 基于多源气象资料与机器学习的降水数据质量控制方法研究. 水电能源科学, 2026 , 44 (1) : 15 -19 . DOI: 10.20040/j.cnki.1000-7709.2026.20250376
Shi-ying LI, Xiao-long HUANG, Lan-xin LUO, Wei WU, Chun-yue LV. Study on Precipitation Data Quality Control Method Based on Multi-source Meteorological Data and Machine Learning[J]. Water Resources and Power, 2026 , 44 (1) : 15 -19 . DOI: 10.20040/j.cnki.1000-7709.2026.20250376
Appendix
Less
Year 2026 volume 44 Issue 1
67
8
Cite this Article
BibTeX
Article Info
doi: 10.20040/j.cnki.1000-7709.2026.20250376
  • Receive Date：2025-03-03
  • Online Date：2026-09-28
  • Published：2026-01-25
Article Data
Affiliations
History
  • Received：2025-03-03
  • Revised：2025-04-19
  • Accepted：2025-04-26
Funding
Affiliations
    1.Sichuan Meteorological Observation and Data Center, Chengdu 610072, China
    2.Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
    3.Southwest Sichuan (Ya'an) Rainstorm Laboratory, Ya’an 625099, China
    4.Wenjiang National Climatology Observatory, Chengdu 611130, China
    5.National Meteorological Information Center, Beijing 100081, China
References
Share
https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2026.20250376
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
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
total species (%)		 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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House