Article(id=1311351193234928044, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2026.20250376, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740931200000, receivedDateStr=2025-03-03, revisedDate=1744992000000, revisedDateStr=2025-04-19, acceptedDate=1745596800000, acceptedDateStr=2025-04-26, onlineDate=1790580088193, onlineDateStr=2026-09-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580088193, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580088193, creator=13701087609, updateTime=1790580088193, updator=13701087609, issue=Issue{id=1311351152067830064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2026', volume='44', issue='1', pageStart='1', pageEnd='246', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790580078378, creator='13701087609', updateTime=1790588459421, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386304772927717, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386304772927718, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=15, endPage=19, ext={EN=ArticleExt(id=1311351193385922989, articleId=1311351193234928044, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Study on Precipitation Data Quality Control Method Based on Multi-source Meteorological Data and Machine Learning, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Shi-ying LI1, 2, 3, 4, Xiao-long HUANG1, 2, 3, 4, Lan-xin LUO5, Wei WU1, 2, 3, Chun-yue LV1, 2, 3, authorsList=Shi-ying LI, Xiao-long HUANG, Lan-xin LUO, Wei WU, Chun-yue LV, authorCompany=null, correspAuthors=Xiao-long HUANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311351193683718574, articleId=1311351193234928044, tenantId=1146029695717560320, journalId=1205116964453384197, language=CN, title=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, columnId=1207627659488632924, journalTitle=水电能源科学, columnName=水文水资源与环境, runingTitle=null, highlight=null, articleAbstract=

为提高降水观测数据在公共气象服务中的应用价值,基于多源气象资料及机器学习模型,开展了降水数据的质量控制方法研究。研究利用气象站数据、多源融合实况分析产品、气象雷达卫星产品以及XGBoost模型,并通过综合评分,形成了一种多源协同的降水数据质量控制方法。采用人工植入误差法对该方法的准确性进行检验。结果表明,随机植入降水异常值时,错误数据的检出率为0.85;按降水量的不同等级植入误差时,错误数据的检出率为0.79。经协同质控后,待检降水数据与邻近气象站的平均误差减小15%,相关系数提高至0.7~1.0。因此,该方法能有效筛选疑误降水数据,为气象防灾减灾提供有力的数据支撑。

, authors=李施颖1, 2, 3, 4, 黄晓龙1, 2, 3, 4, 罗岚心5, 吴薇1, 2, 3, 吕纯月1, 2, 3, authorsList=李施颖, 黄晓龙, 罗岚心, 吴薇, 吕纯月, authorCompany=null, correspAuthors=黄晓龙, authorNote=

李施颖(1994-),女,工程师,研究方向为气象资料质量控制及检验分析,E-mail:

, correspAuthorsNote=
黄晓龙(1981-),男,正高级工程师,研究方向为气象资料质量控制、融合与产品评估,E-mail:
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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
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李施颖 1, 2, 3, 4 , 黄晓龙 1, 2, 3, 4 , 罗岚心 5 , 吴薇 1, 2, 3 , 吕纯月 1, 2, 3
水电能源科学 | 水文水资源与环境 2026,44(1): 15-19
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水电能源科学 |水文水资源与环境 2026 , 44 (1) : 15 -19
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
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李施颖1, 2, 3, 4 , 黄晓龙1, 2, 3, 4 , 罗岚心5, 吴薇1, 2, 3, 吕纯月1, 2, 3
作者信息
  • 1.四川省气象探测数据中心,四川 成都 610072
  • 2.高原与盆地暴雨旱涝灾害四川省重点实验室,四川 成都 610072
  • 3.川西南(雅安)暴雨实验室,四川 雅安 625099
  • 4.温江国家气候观象台,四川 成都 611130
  • 5.国家气象信息中心,北京 100081
通讯作者:
黄晓龙(1981-),男,正高级工程师,研究方向为气象资料质量控制、融合与产品评估,E-mail:
作者简介:

李施颖(1994-),女,工程师,研究方向为气象资料质量控制及检验分析,E-mail:

Study on Precipitation Data Quality Control Method Based on Multi-source Meteorological Data and Machine Learning
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
出版时间: 2026-01-25 doi: 10.20040/j.cnki.1000-7709.2026.20250376
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为提高降水观测数据在公共气象服务中的应用价值,基于多源气象资料及机器学习模型,开展了降水数据的质量控制方法研究。研究利用气象站数据、多源融合实况分析产品、气象雷达卫星产品以及XGBoost模型,并通过综合评分,形成了一种多源协同的降水数据质量控制方法。采用人工植入误差法对该方法的准确性进行检验。结果表明,随机植入降水异常值时,错误数据的检出率为0.85;按降水量的不同等级植入误差时,错误数据的检出率为0.79。经协同质控后,待检降水数据与邻近气象站的平均误差减小15%,相关系数提高至0.7~1.0。因此,该方法能有效筛选疑误降水数据,为气象防灾减灾提供有力的数据支撑。

降水  /  雷达产品  /  卫星产品  /  机器学习  /  质量控制

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
  • 国家气象信息中心2023年“揭榜挂帅”项目(13)
  • 川西南(雅安)暴雨实验室科技发展基金项目(CXNBYSYSYWZD202406)
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doi: 10.20040/j.cnki.1000-7709.2026.20250376
  • 接收时间:2025-03-03
  • 首发时间:2026-09-28
  • 出版时间:2026-01-25
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  • 收稿日期:2025-03-03
  • 修回日期:2025-04-19
  • 录用日期:2025-04-26
基金
国家气象信息中心2023年“揭榜挂帅”项目(13)
川西南(雅安)暴雨实验室科技发展基金项目(CXNBYSYSYWZD202406)
作者信息
    1.四川省气象探测数据中心,四川 成都 610072
    2.高原与盆地暴雨旱涝灾害四川省重点实验室,四川 成都 610072
    3.川西南(雅安)暴雨实验室,四川 雅安 625099
    4.温江国家气候观象台,四川 成都 611130
    5.国家气象信息中心,北京 100081

通讯作者:

黄晓龙(1981-),男,正高级工程师,研究方向为气象资料质量控制、融合与产品评估,E-mail:
参考文献
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https://castjournals.cast.org.cn/joweb/sdnykx/CN/10.20040/j.cnki.1000-7709.2026.20250376
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2种不同金属材料的力学参数
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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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