Article(id=1324320319896429258, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1324320221309313194, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2026.06.00049, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1782144000000, receivedDateStr=2026-06-23, revisedDate=1789056000000, revisedDateStr=2026-09-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1793672168945, onlineDateStr=2026-11-03, pubDate=1790524800000, pubDateStr=2026-09-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793672168945, onlineIssueDateStr=2026-11-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793672168945, creator=13701087609, updateTime=1793672168945, updator=13701087609, issue=Issue{id=1324320221309313194, tenantId=1146029695717560320, journalId=1146031591421210625, year='2026', volume='44', issue='18', pageStart='1', pageEnd='180', issueExtLink='null', onlineDate='null', pubDate='1790524800000', pubDateStr='2026-09-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793672145439, creator='13701087609', updateTime=1793673806843, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324327189847605469, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1324320221309313194, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324327189847605470, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1324320221309313194, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=45, endPage=55, ext={EN=ArticleExt(id=1324320320651403979, articleId=1324320319896429258, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Application of Meridian Project data in space environment forecasting, columnId=1150494642224591153, journalTitle=Science & Technology Review, columnName=Exclusive, runingTitle=null, highlight=null, articleAbstract=

Space environment forecasting is an essential part of ensuring the safety of space engineering and supporting space science exploration. As a national major scientific facility for ground−based integrated monitoring of the space environment, the Chinese Meridian Project (CMP) provides continuous, multi−sphere, multi−parameter observational data covering the sun, interplanetary space, magnetosphere, ionosphere, and middle and upper atmosphere, offering key data support for space environment forecasting and assurance. Following the causal chain of the solar−terrestrial space environment, this paper systematically reviews the main elements of space environment forecasting, requirements of the supporting data, and forecast model requirements of space environment forecasting, summarizes the study work using CMP data in solar activity characteristic identification, geomagnetic index construction, ionospheric TEC forecasting, and ionospheric irregularity identification and forecasting, and further elaborates the types of CMP data used in practical operational space environment forecasting, their application methods, and their effectiveness. Finally, it analyzes the current challenges facing data application and forecasting assurance and proposes suggestions for deepening the future application of CMP data, providing a reference for further tapping the forecasting value of CMP data and for capacity building to enhance space environment forecasting capabilities.

, authors=Bingxian LUO1, 2, Yanhong CHEN1, *, Qiuzhen ZHONG1, 2, Zhitao LI2, Jiyao XU2, Jia LIU2, Jinyue YANG1, 2, authorsList=Bingxian LUO, Yanhong CHEN, Qiuzhen ZHONG, Zhitao LI, Jiyao XU, Jia LIU, Jinyue YANG, authorCompany=null, correspAuthors=Yanhong CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., 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=1324320324401111766, articleId=1324320319896429258, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=子午工程数据在空间环境预报中的应用, columnId=1150494642375586098, journalTitle=科技导报, columnName=特色专题, runingTitle=null, highlight=null, articleAbstract=

空间环境预报是保障航天任务安全、支撑空间科学探测的重要环节。子午工程作为中国空间环境地基综合监测大科学装置,提供太阳、行星际、地磁、电离层、中高层大气等多圈层多要素连续观测数据,能为空间环境预报保障提供关键数据支撑。按照日地空间环境因果链顺序系统梳理了空间环境预报的核心要素、预报数据和预报模式需求,总结了利用子午工程数据在太阳活动特征识别、地磁指数构建、电离层总电子含量预报、电离层不规则体识别与预报方面的工作,阐述了在空间环境实际业务预报中应用的子午工程数据类型、应用方式和成效。最后分析了当前数据应用和预报保障面临的挑战,并提出未来子午工程数据深化应用的建议,为未来子午工程数据预报价值深入挖掘和空间环境预报能力提升建设提供参考。

, authors=罗冰显1, 2, 陈艳红1, *, 钟秋珍1, 2, 李志涛2, 徐寄遥2, 刘佳2, 杨金月1, 2, authorsList=罗冰显, 陈艳红, 钟秋珍, 李志涛, 徐寄遥, 刘佳, 杨金月, authorCompany=null, correspAuthors=陈艳红, authorNote=

罗冰显,研究员,研究方向为空间环境预报和应用,电子信箱:

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陈艳红(通信作者),研究员,研究方向为空间环境预报,电子信箱:
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Journal of Geophysical Research: Space Physics, 2022, 127(8): e2022JA030451., articleTitle=Development of the Sanya incoherent scatter radar and preliminary results, refAbstract=null), Reference(id=1324320336178717499, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, doi=null, pmid=null, pmcid=null, year=2024, volume=22, issue=1, pageStart=e2023SW003727, pageEnd=null, url=null, language=null, rfNumber=[29], rfOrder=28, authorNames=Gao P D, Cai J H, Wang Z, journalName=Space Weather, refType=null, unstructuredReference=Gao P D, Cai J H, Wang Z, et al. Prediction of ionograms with/without spread−F at Hainan by a combined spatio−temporal neural network[J]. Space Weather, 2024, 22(1): e2023SW003727., articleTitle=Prediction of ionograms with/without spread−F at Hainan by a combined spatio−temporal neural network, refAbstract=null), Reference(id=1324320336237437756, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, doi=null, pmid=null, pmcid=null, year=2023, volume=23, issue=20, pageStart=13413, pageEnd=13431, url=null, language=null, rfNumber=[30], rfOrder=29, authorNames=Tian P H, Yu B K, Ye H L, journalName=Atmospheric Chemistry and Physics, refType=null, unstructuredReference=Tian P H, Yu B K, Ye H L, et al. Ionospheric irregularity reconstruction using multisource data fusion via deep learning[J]. Atmospheric Chemistry and Physics, 2023, 23(20): 13413-13431., articleTitle=Ionospheric irregularity reconstruction using multisource data fusion via deep learning, refAbstract=null), Reference(id=1324320336333906749, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, doi=null, pmid=null, pmcid=null, year=2026, volume=46, issue=3, pageStart=650, pageEnd=657, url=null, language=null, rfNumber=[31], rfOrder=30, authorNames=赖昌, 汪鹏超, 李钦增, journalName=空间科学学报, refType=null, unstructuredReference=赖昌, 汪鹏超, 李钦增. 基于子午工程气辉成像观测的中高层大气波动智能识别及关键参数提取[J]. 空间科学学报, 2026, 46(3): 650-657., articleTitle=基于子午工程气辉成像观测的中高层大气波动智能识别及关键参数提取, refAbstract=null)], funds=[Fund(id=1324320332328346398, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, awardId=2024YFC2206902, language=CN, fundingSource=国家重点研发计划项目(2024YFC2206902), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324320324652770007, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, xref=1, ext=[AuthorCompanyExt(id=1324320324661158616, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, companyId=1324320324652770007, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China), AuthorCompanyExt(id=1324320324669547225, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, companyId=1324320324652770007, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1中国科学院国家空间科学中心,北京 100190)]), AuthorCompany(id=1324320324732461786, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, xref=2, ext=[AuthorCompanyExt(id=1324320324740850395, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, companyId=1324320324732461786, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2University of Chinese Academy of Sciences, Beijing 101408, China), AuthorCompanyExt(id=1324320324749239004, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, companyId=1324320324732461786, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2中国科学院大学,北京 101408)])], figs=[ArticleFig(id=1324320329505579792, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=EN, label=null, caption=null, figureFileSmall=EQIDshFUNpWPix7o9mjK9w==, figureFileBig=U1/S6aq3eK8bO7Lp1OGsgg==, tableContent=null), ArticleFig(id=1324320329568494353, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=CN, label=图1, caption=子午工程台站布局, figureFileSmall=EQIDshFUNpWPix7o9mjK9w==, figureFileBig=U1/S6aq3eK8bO7Lp1OGsgg==, tableContent=null), ArticleFig(id=1324320331338490642, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=EN, label=null, caption=null, figureFileSmall=2UXCOo11uX7olTKrZG0ksQ==, figureFileBig=tFYEb5v5RYIjm753ZnuA1Q==, tableContent=null), ArticleFig(id=1324320331426571027, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=CN, label=图2, caption=空间环境预报技术发展历程, figureFileSmall=2UXCOo11uX7olTKrZG0ksQ==, figureFileBig=tFYEb5v5RYIjm753ZnuA1Q==, tableContent=null), ArticleFig(id=1324320331531428628, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=EN, label=null, caption=null, figureFileSmall=FYuAxd87TjUgVXzgKFC2IQ==, figureFileBig=UE9vVB51/doQVUqAiFZFLA==, tableContent=null), ArticleFig(id=1324320331598537493, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=CN, label=图3, caption=子午工程数据对空间站任务的支持, figureFileSmall=FYuAxd87TjUgVXzgKFC2IQ==, figureFileBig=UE9vVB51/doQVUqAiFZFLA==, tableContent=null), ArticleFig(id=1324320331678229270, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=EN, label=null, caption=null, figureFileSmall=zgTk3GubjYVkO37g7u4RtQ==, figureFileBig=HdIAzJzoaztmOT9ACVInFA==, tableContent=null), ArticleFig(id=1324320331749532439, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=CN, label=图4, caption=神舟二十号返回期间地磁Ap指数, figureFileSmall=zgTk3GubjYVkO37g7u4RtQ==, figureFileBig=HdIAzJzoaztmOT9ACVInFA==, tableContent=null), ArticleFig(id=1324320331812447000, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
预报对象预报内容时间提前
量需求
预报需要的
参数
国外数据来源子午工程数据来源子午工程数据特点
太阳活动
指数
F10.71~27 d活动区个数、
面积、磁类型
SDO、GONG等怀柔、赣榆太阳磁场观测地基观测精度高,但无法全天时、全天候观测
历史F10.7彭蒂克顿天文台/
NOAA SWPC
明安图厘米波射电频谱仪、廊坊10 cm射电观测分辨率高,可自主生成F10.7
太阳X射线
耀斑
耀斑级别
爆发时间
1~3 d活动区面积、
磁类型
SDO、GONG等怀柔、赣榆太阳磁场观测地基观测精度高,但无法全天时、全天候观测
历史F10.7彭蒂克顿天文台/
NOAA SWPC
明安图厘米波射电频谱仪、廊坊10 cm射电观测分辨率高,可自主生成F10.7
活动区耀斑
爆发历史数据
地球静止轨道运行环境卫星(Geostationary Operational Environmental Satellite,GOES)太阳Hα观测能够给出耀斑有、无和位置,但不能给出精确的耀斑级别
太阳质子事件级别
发生时间
持续时间
1~3 d源区信息、CME
速度、角宽度
SDO、SOHO多站射电观测、太阳Hα
观测
数据分辨率高,可反演获得CME速度,缺乏角宽度信息
历史质子通量GOES无—
太阳风和
行星际
预报
太阳风速度、行星际磁场
南向分量
1~27 d全日面综合
磁图
SDO、GONG等怀柔、赣榆太阳磁场观测地基观测精度高,但无法全天时、全天候观测
CME观测SOHO射电观测数据分辨率高,可反演获得CME速度,缺乏角宽度信息
冕洞观测SDO无—
历史太阳风DSCOVR/ACE行星际闪烁监测望远镜(Interplanetary Scintillation Telescope,IPS)非直接速度观测,能反演给出多个位置速度,时间不连续
地磁指数/
地磁暴
Kp/Ap、Dst1~3 dCME参数SOHO射电观测数据分辨率高,可反演获得CME速度,但缺乏角宽度信息
冕洞观测SDO无—
太阳风DSCOVR/ACEIPS非直接速度观测,能反演给出多个位置速度,时间不连续
高能
电子暴
级别
发生时间
持续时间
1~3 d太阳风速度DSCOVR/ACEIPS非直接速度观测,能反演给出多个位置速度,时间不连续
地磁指数GFZ/SWPC地磁监测分辨率高,可自主生成地磁指数
历史高能电子
子通量
GOES卫星无无
磁层顶
穿越事件
发生穿越的
时间
1 h太阳风速度DSCOVRIPS非直接速度观测,能反演给出多个位置速度,时间不连续
电离层
预报
级别发生时间
持续时间
0~3 dGNSS−TEC/foF2CODE/JPL电离层监测网中国区域更精细观测
中高层
大气扰动
发生时间
持续时间
1~3 d大气密度/大气风场卫星数据中高层大气监测网地基固定台站长期数据积累
), ArticleFig(id=1324320331913110297, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=CN, label=表1, caption=

业务预报内容及数据来源

, figureFileSmall=null, figureFileBig=null, tableContent=
预报对象预报内容时间提前
量需求
预报需要的
参数
国外数据来源子午工程数据来源子午工程数据特点
太阳活动
指数
F10.71~27 d活动区个数、
面积、磁类型
SDO、GONG等怀柔、赣榆太阳磁场观测地基观测精度高,但无法全天时、全天候观测
历史F10.7彭蒂克顿天文台/
NOAA SWPC
明安图厘米波射电频谱仪、廊坊10 cm射电观测分辨率高,可自主生成F10.7
太阳X射线
耀斑
耀斑级别
爆发时间
1~3 d活动区面积、
磁类型
SDO、GONG等怀柔、赣榆太阳磁场观测地基观测精度高,但无法全天时、全天候观测
历史F10.7彭蒂克顿天文台/
NOAA SWPC
明安图厘米波射电频谱仪、廊坊10 cm射电观测分辨率高,可自主生成F10.7
活动区耀斑
爆发历史数据
地球静止轨道运行环境卫星(Geostationary Operational Environmental Satellite,GOES)太阳Hα观测能够给出耀斑有、无和位置,但不能给出精确的耀斑级别
太阳质子事件级别
发生时间
持续时间
1~3 d源区信息、CME
速度、角宽度
SDO、SOHO多站射电观测、太阳Hα
观测
数据分辨率高,可反演获得CME速度,缺乏角宽度信息
历史质子通量GOES无—
太阳风和
行星际
预报
太阳风速度、行星际磁场
南向分量
1~27 d全日面综合
磁图
SDO、GONG等怀柔、赣榆太阳磁场观测地基观测精度高,但无法全天时、全天候观测
CME观测SOHO射电观测数据分辨率高,可反演获得CME速度,缺乏角宽度信息
冕洞观测SDO无—
历史太阳风DSCOVR/ACE行星际闪烁监测望远镜(Interplanetary Scintillation Telescope,IPS)非直接速度观测,能反演给出多个位置速度,时间不连续
地磁指数/
地磁暴
Kp/Ap、Dst1~3 dCME参数SOHO射电观测数据分辨率高,可反演获得CME速度,但缺乏角宽度信息
冕洞观测SDO无—
太阳风DSCOVR/ACEIPS非直接速度观测,能反演给出多个位置速度,时间不连续
高能
电子暴
级别
发生时间
持续时间
1~3 d太阳风速度DSCOVR/ACEIPS非直接速度观测,能反演给出多个位置速度,时间不连续
地磁指数GFZ/SWPC地磁监测分辨率高,可自主生成地磁指数
历史高能电子
子通量
GOES卫星无无
磁层顶
穿越事件
发生穿越的
时间
1 h太阳风速度DSCOVRIPS非直接速度观测,能反演给出多个位置速度,时间不连续
电离层
预报
级别发生时间
持续时间
0~3 dGNSS−TEC/foF2CODE/JPL电离层监测网中国区域更精细观测
中高层
大气扰动
发生时间
持续时间
1~3 d大气密度/大气风场卫星数据中高层大气监测网地基固定台站长期数据积累
), ArticleFig(id=1324320331988607770, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
预报模式预报时间
提前量
均方根误差RMSE/标准差SD/平均误差MAE参考模型备注
电离层闪烁地图重建
模型[19]
0MAE=0.069全球电离层闪烁模型(global ionospheric scintillation model, GISM)MAE=0.291个月数据评估
电离层闪烁预报模式
ISNet[20]
1 hRMSE=0.053(弱闪烁)
RMSE=0.124(中等闪烁)
RMSE=0.183(强闪烁)
MAE=0.026(弱闪烁)
MAE =0.081(中等闪烁)
MAE =0.106(强闪烁)
−1年数据评估
电离层等离子体泡预报
模式[21]
1 hRMSE= 0.110 TECU/min
长短期记忆网络:
RMSE=0.114 TECU/min
门控循环单元模型:
RMSE=0.117 TECU/min
2 d数据评估
电离层TEC地图球谐
函数模式[22]
0MAE=−1.12 TECU
SD=3.98 TECU
−2024年5月11–12日暴时数据评估
电离层TEC同化模式[23]0平静期:
MAE= 1.07 TECU
RMSE=2.52 TECU
SD=3.45 TECU
扰动期:
MAE= 1.63 TECU
RMSE=3.51 TECU
SD=2.77 TECU
国际参考电离层模型
平静期:
MAE=1.9 TECU
SD=5.72 TECU
扰动期:
MAE=4.05 TECU
SD=4.18 TECU
2014年1月16日地磁平静条件和2013年6月29日地磁扰动条件下的评估
中国地区单站TEC
预报模式[24]
1 d平静期:RMSE<3.2 TECU
磁暴期:RMSE<4.8 TECU
−1个太阳活动高年+1个太阳
活动低年数据评估
全球TEC地图预报模式[25]1 d2019年:
MAE=0.87 TECU
RMSE=1.28 TECU
2024年:
MAE=3.87 TECU
RMSE=5.28 TECU
欧洲定轨中心预报
2019年:
MAE=1.02 TECU
RMSE=1.43 TECU
2024年:
MAE=4.48 TECU
RMSE=5.97 TECU
1个太阳活动高年+1个太阳
活动低年数据评估
), ArticleFig(id=1324320332064105243, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=CN, label=表2, caption=

电离层预报模型预报效果对比

, figureFileSmall=null, figureFileBig=null, tableContent=
预报模式预报时间
提前量
均方根误差RMSE/标准差SD/平均误差MAE参考模型备注
电离层闪烁地图重建
模型[19]
0MAE=0.069全球电离层闪烁模型(global ionospheric scintillation model, GISM)MAE=0.291个月数据评估
电离层闪烁预报模式
ISNet[20]
1 hRMSE=0.053(弱闪烁)
RMSE=0.124(中等闪烁)
RMSE=0.183(强闪烁)
MAE=0.026(弱闪烁)
MAE =0.081(中等闪烁)
MAE =0.106(强闪烁)
−1年数据评估
电离层等离子体泡预报
模式[21]
1 hRMSE= 0.110 TECU/min
长短期记忆网络:
RMSE=0.114 TECU/min
门控循环单元模型:
RMSE=0.117 TECU/min
2 d数据评估
电离层TEC地图球谐
函数模式[22]
0MAE=−1.12 TECU
SD=3.98 TECU
−2024年5月11–12日暴时数据评估
电离层TEC同化模式[23]0平静期:
MAE= 1.07 TECU
RMSE=2.52 TECU
SD=3.45 TECU
扰动期:
MAE= 1.63 TECU
RMSE=3.51 TECU
SD=2.77 TECU
国际参考电离层模型
平静期:
MAE=1.9 TECU
SD=5.72 TECU
扰动期:
MAE=4.05 TECU
SD=4.18 TECU
2014年1月16日地磁平静条件和2013年6月29日地磁扰动条件下的评估
中国地区单站TEC
预报模式[24]
1 d平静期:RMSE<3.2 TECU
磁暴期:RMSE<4.8 TECU
−1个太阳活动高年+1个太阳
活动低年数据评估
全球TEC地图预报模式[25]1 d2019年:
MAE=0.87 TECU
RMSE=1.28 TECU
2024年:
MAE=3.87 TECU
RMSE=5.28 TECU
欧洲定轨中心预报
2019年:
MAE=1.02 TECU
RMSE=1.43 TECU
2024年:
MAE=4.48 TECU
RMSE=5.97 TECU
1个太阳活动高年+1个太阳
活动低年数据评估
), ArticleFig(id=1324320332147991324, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
预报业务使用的子午工程数据数据作用发挥的作用/效果
常规预报业务/
网页发布
太阳观测数据提供活动区信息、耀斑爆发信息部分替代国际数据
F10.7指数信息● 可替代国际数据,1 h可更新
● 国际数据1 d更新
磁通门磁力仪观测数据地磁指数实时反演地磁活动状况描述,3 h更新
宇宙线观测数据网页实时数据展示自主宇宙线数据,5 min更新
电离层TEC和闪烁仪观测数据网页实时数据展示提供闪烁/TEC观测,15 min更新
电离层TEC和闪烁仪
观测数据
中国区域TEC地图生成● 提供1°×1°×15 min高分辨率近实时TEC地图
● 国际数据是5°×2.5°×1 h,且延迟1~2 d
中国区域TEC地图预报● 提供1°×1°×1 h分辨率TEC地图,时间提前量
24 h
● 国际提供地图数据是5°×2.5°×1 h,时间提前
量1~2 d
载人航天预报
产品/专线
太阳地磁活动指数空间站轨道预报输入数据替代国际发布的轨道预报输入空间环境
指数,3 h更新
太阳观测数据、地磁、
电离层观测数据
提供给空间天气事件期间的
数据
能够基于自主数据进行空间天气事件的
分析和复盘
先导卫星宇宙线中子/μ子强度提供卫星运行宇宙线信息自主数据
地磁Kp/Ap/AE/Dst指数提供地磁暴信息自主数据替代国际数据
电波通信用户电离层TEC、foF2观测数据提供电离层吸收、电离层暴信息自主数据支持电波环境分析
), ArticleFig(id=1324320332215100189, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1324320319896429258, language=CN, label=表3, caption=

子午工程数据在空间环境预报中的作用和效果

, figureFileSmall=null, figureFileBig=null, tableContent=
预报业务使用的子午工程数据数据作用发挥的作用/效果
常规预报业务/
网页发布
太阳观测数据提供活动区信息、耀斑爆发信息部分替代国际数据
F10.7指数信息● 可替代国际数据,1 h可更新
● 国际数据1 d更新
磁通门磁力仪观测数据地磁指数实时反演地磁活动状况描述,3 h更新
宇宙线观测数据网页实时数据展示自主宇宙线数据,5 min更新
电离层TEC和闪烁仪观测数据网页实时数据展示提供闪烁/TEC观测,15 min更新
电离层TEC和闪烁仪
观测数据
中国区域TEC地图生成● 提供1°×1°×15 min高分辨率近实时TEC地图
● 国际数据是5°×2.5°×1 h,且延迟1~2 d
中国区域TEC地图预报● 提供1°×1°×1 h分辨率TEC地图,时间提前量
24 h
● 国际提供地图数据是5°×2.5°×1 h,时间提前
量1~2 d
载人航天预报
产品/专线
太阳地磁活动指数空间站轨道预报输入数据替代国际发布的轨道预报输入空间环境
指数,3 h更新
太阳观测数据、地磁、
电离层观测数据
提供给空间天气事件期间的
数据
能够基于自主数据进行空间天气事件的
分析和复盘
先导卫星宇宙线中子/μ子强度提供卫星运行宇宙线信息自主数据
地磁Kp/Ap/AE/Dst指数提供地磁暴信息自主数据替代国际数据
电波通信用户电离层TEC、foF2观测数据提供电离层吸收、电离层暴信息自主数据支持电波环境分析
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子午工程数据在空间环境预报中的应用
罗冰显 1, 2 , 陈艳红 1, * , 钟秋珍 1, 2 , 李志涛 2 , 徐寄遥 2 , 刘佳 2 , 杨金月 1, 2
科技导报 | 特色专题 2026,44(18): 45-55
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科技导报 |特色专题 2026 , 44 (18) : 45 -55
子午工程数据在空间环境预报中的应用
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罗冰显1, 2 , 陈艳红1, * , 钟秋珍1, 2, 李志涛2, 徐寄遥2, 刘佳2, 杨金月1, 2
作者信息
  • 1中国科学院国家空间科学中心,北京 100190
  • 2中国科学院大学,北京 101408
通讯作者:
陈艳红(通信作者),研究员,研究方向为空间环境预报,电子信箱:
作者简介:

罗冰显,研究员,研究方向为空间环境预报和应用,电子信箱:

Application of Meridian Project data in space environment forecasting
Bingxian LUO1, 2 , Yanhong CHEN1, * , Qiuzhen ZHONG1, 2, Zhitao LI2, Jiyao XU2, Jia LIU2, Jinyue YANG1, 2
Affiliations
  • 1National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
  • 2University of Chinese Academy of Sciences, Beijing 101408, China
出版时间: 2026-09-28 doi: 10.3981/j.issn.1000-7857.2026.06.00049
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空间环境预报是保障航天任务安全、支撑空间科学探测的重要环节。子午工程作为中国空间环境地基综合监测大科学装置,提供太阳、行星际、地磁、电离层、中高层大气等多圈层多要素连续观测数据,能为空间环境预报保障提供关键数据支撑。按照日地空间环境因果链顺序系统梳理了空间环境预报的核心要素、预报数据和预报模式需求,总结了利用子午工程数据在太阳活动特征识别、地磁指数构建、电离层总电子含量预报、电离层不规则体识别与预报方面的工作,阐述了在空间环境实际业务预报中应用的子午工程数据类型、应用方式和成效。最后分析了当前数据应用和预报保障面临的挑战,并提出未来子午工程数据深化应用的建议,为未来子午工程数据预报价值深入挖掘和空间环境预报能力提升建设提供参考。

子午工程  /  太阳活动  /  地磁暴  /  空间环境预报  /  预报保障

Space environment forecasting is an essential part of ensuring the safety of space engineering and supporting space science exploration. As a national major scientific facility for ground−based integrated monitoring of the space environment, the Chinese Meridian Project (CMP) provides continuous, multi−sphere, multi−parameter observational data covering the sun, interplanetary space, magnetosphere, ionosphere, and middle and upper atmosphere, offering key data support for space environment forecasting and assurance. Following the causal chain of the solar−terrestrial space environment, this paper systematically reviews the main elements of space environment forecasting, requirements of the supporting data, and forecast model requirements of space environment forecasting, summarizes the study work using CMP data in solar activity characteristic identification, geomagnetic index construction, ionospheric TEC forecasting, and ionospheric irregularity identification and forecasting, and further elaborates the types of CMP data used in practical operational space environment forecasting, their application methods, and their effectiveness. Finally, it analyzes the current challenges facing data application and forecasting assurance and proposes suggestions for deepening the future application of CMP data, providing a reference for further tapping the forecasting value of CMP data and for capacity building to enhance space environment forecasting capabilities.

Chinese Meridian Project  /  solar activity  /  geomagnetic storm  /  space environment forecasting  /  forecasting support
罗冰显, 陈艳红, 钟秋珍, 李志涛, 徐寄遥, 刘佳, 杨金月. 子午工程数据在空间环境预报中的应用. 科技导报, 2026 , 44 (18) : 45 -55 . DOI: 10.3981/j.issn.1000-7857.2026.06.00049
Bingxian LUO, Yanhong CHEN, Qiuzhen ZHONG, Zhitao LI, Jiyao XU, Jia LIU, Jinyue YANG. Application of Meridian Project data in space environment forecasting[J]. Science & Technology Review, 2026 , 44 (18) : 45 -55 . DOI: 10.3981/j.issn.1000-7857.2026.06.00049
空间环境是航天器安全运行、通信导航稳定服务、空间科学探测顺利开展的重要影响因素。太阳耀斑、太阳质子事件、地磁暴、高能电子暴、电离层扰动等空间天气事件,可导致卫星故障、通信中断、导航误差增大、航天员辐射风险上升,严重制约航天活动与技术系统的应用效能[1]。中国自1992年开始空间环境预报相关工作,1998年中国科学院空间环境预报中心正式运行[2],近30年来已建立集数据收集—模式运行—预报分析—产品制备—预报服务一体化的业务预报系统,形成现报、警报、日报、周报、月报及中长期预报的完整服务能力,服务载人航天、探月工程、空间科学卫星等重大任务。
然而,当前空间环境预报能力提升仍面临多重制约。在观测数据层面,国外机构共享的太阳、行星际、地磁与电离层等观测数据是开展业务预报的重要数据来源,自主观测体系虽已初具规模,但自主数据向业务化产品转化的深度与广度尚且不足,部分关键空间天气参数的数据供给与自主可控能力仍存在短板,制约着中国空间环境预报业务的独立性与稳健性;在预报技术层面,传统预报手段以经验规则和统计模型为主,对日地空间物理过程的机制刻画尚不完整,预报精度、时效与精细化程度难以完全满足重大工程对高可靠、全时段空间环境保障的要求。在当前空间科学蓬勃发展、人工智能技术深入应用的浪潮下,充分利用自主观测数据与技术手段,提升空间环境预报精度与自主可控能力,已十分迫切。
子午工程是中国自主建设的空间环境地基综合监测网[3],沿100°E、120°E及30°N、40°N的“井”字形布局,实现对太阳活动、行星际扰动、地磁扰动、电离层变化、中高层大气参数的连续协同观测,可为空间环境预报提供高时空分辨率、多要素、长序列观测数据(图1)。在空间科学研究方面,基于子午工程数据正在源源不断产生重要的科学成果[4−6]。相对而言,将科学成果转化为预报能力的工作还不充分,蕴含的关键预报信息远未被有效释放。分析当前空间环境预报业务中的数据需求,总结子午工程已有的预报研究和应用成果,讨论子午工程数据应用面临的挑战和发展方向,为预报研究和自主观测数据向业务预报转化提供思路和方法,对中国空间环境预报能力提升与子午工程数据高效利用具有参考意义。
空间环境预报是对日地空间可能影响天地基技术系统的各类环境状态和事件的预测、预报。太阳爆发活动产生大量的物质和能量,是导致近地空间环境变化的主要因素,也是空间环境短期预报的重要源头。因此,空间环境预报包含了从太阳爆发、行星际扰动传播,以及扰动对磁层、电离层、热层影响等层面的预报。无论是人工经验预报还是模式预报,空间环境业务预报都需要基于当前和历史各类空间环境数据而进行,对空间环境监测数据提出了严格的时效性、完整性与准确性要求。
太阳是空间环境变化的源头,太阳活动水平是最重要的预报要素,以太阳黑子相对数、F10.7指数为代表性指标,反映太阳活动整体强弱及11年周期变化趋势。太阳爆发性活动预报主要包括太阳耀斑预报与太阳质子事件预报两类:前者重点预报X射线耀斑的爆发等级(C/M/X级)及其发生概率,后者则主要预测地球同步轨道上高能质子事件发生的概率与强度。太阳日面活动区、冕洞和日冕物质抛射(coronal mass ejection,CME)是太阳爆发和地磁活动的重要源头,这3类现象的观测和参数是预报中的关键数据。
行星际空间是太阳爆发向地球传播的通道,其预报要素主要包括太阳风速度、密度及行星际磁场,这些参数是反映CME与冕洞高速流传播的关键参数。其中,行星际磁场南向分量主导太阳风能量向磁层的传输效率,其持续南向是磁暴发生的关键条件。
磁层是太阳风能量注入地球空间的关键转换区域,其预报对象涵盖磁场与粒子2个层面。磁场预报以地磁指数预报为主:Kp/Ap指数表征全球地磁活动水平;Dst指数表征磁暴期间赤道环电流强度,是衡量磁暴主相强度的核心指标;AE指数表征极光带东西向电急流强度,是亚暴活动水平的主要监测指标。磁层粒子包括高能电子暴预报,即对地球同步轨道高能电子通量增强事件进行预警,还包括对辐射带结构及粒子分布动态进行预测。在低能粒子方面,需要进行热等离子体注入事件预报,预报磁层亚暴期间热等离子体向内磁层的注入过程,需要对磁层等离子体分布进行预报。此外,磁层顶位形预报对同步轨道卫星掌握所处空间环境同样具有重要作用。
电离层是影响无线电通信与导航定位的重要区域,其预报要素主要包括F2层临界频率foF2、总电子含量(total electron content,TEC)与电子密度等电离层参数;同时还需预报磁暴期间foF2与TEC的剧烈扰动(如负暴或正暴)。电离层闪烁预报亦是电离层预报的重点与难点,主要针对信号幅度闪烁指数和相位闪烁指数开展预警。
中高层大气通过密度变化直接影响低轨航天器的轨道衰减与寿命,其中大气密度是最核心的预报要素;此外,中性温度和水平风场的预报对中性成分输运以及电离层−热层耦合过程亦十分重要。
表1总结了空间环境预报要素的预报内容和国际数据来源,以及子午工程提供的数据及其特点。从表1可以看出,当前空间环境预报需要太阳活动、行星际太阳风、地磁指数、高能粒子、等离子体、电离层电子密度、大气密度等监测数据,来支撑经验预报分析和预报模型的运行。从子午工程的数据类型分析,除了高能粒子无观测数据外,理论上子午工程可以提供其他要素预报需要的部分或全部数据。实际预报应用中,太阳活动区参数、CME参数、太阳风速度等参数的准确性对太阳地磁活动的预报结果十分重要,通过间接反演得到的相关参数在实际中的应用需要不断进行完善和验证。此外,由于空间天气事件具有突发性与快速演变特征,业务预报保障对数据时效性提出了较高的要求。目前,美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)空间天气预报中心(Space Weather Prediction Center,SWPC)所用的太阳动力学天文台(Solar Dynamics Observatory,SDO)、太阳和日球层观测台(Solar and Heliospheric Observatory,SOHO)、全球日振监测网(Global Oscillation Network Group,GONG)、行星际先进成分探测器(Advanced Composition Explorer,ACE)、深空气候观测卫星(Deep Space Climate Observatory,DSCOVR)和地磁活动数据实现了准实时提供。针对中国预报业务和监测数据的实际情况,业务预报中空间环境事件与指数相关数据传输延迟不应超过30 min。对于磁层、电离层和中高层大气的预报,数据延迟至少不超过3 d。而在航天器发射窗口、在轨载荷安全等专项保障时,则需要实时/近实时数据支撑。
空间环境预报技术的发展高度依赖观测数据的积累。20世纪50—60年代,中国电离层观测资料十分有限,仅围绕电波传播需求开展了电离层骚扰预报研究[7]。20世纪60年代,在筹划发射中国第一颗人造地球卫星的过程中,开展了辐射带强度分布研究,并基于国内外观测资料与模型编制了《人造地球卫星环境手册》[8]。20世纪80—90年代,随着空间环境研究与观测的快速发展,中国开始积累空间环境观测数据及国内外经验模型,并成立了第一个专业的空间环境预报机构,初期主要依赖国外数据与经验模型开展预报。进入21世纪后,空间环境预报研究发展迅速,开发了多种要素的预报模式,但空间环境预报仍依赖人工经验,须进行1~3 d短期预报分析,中长期预报则依赖经验/统计模式,其准确性受经验公式及建模参数选取等因素制约。同时,中国科学院国家空间科学中心、地质与地球物理研究所等单位开始发展太阳−行星际、磁层、电离层物理耦合数值模式[9−10],数值模式依赖物理方程推演,在趋势预测上呈现出一定价值,但仍存在计算成本高、实时响应慢、对复杂极端事件预报能力不足等局限(图2)。
近年来,人工智能技术飞速发展,深度学习技术具备从历史观测数据中自动学习、表征和建模的能力,构建数据驱动的预报模型得到了迅速发展[11],也是未来预报技术发展的重要方向。而深度学习建模需要海量长时序历史多源观测数据,实际应用预报需要涵盖太阳、行星际、地磁、电离层、大气等多圈层信息的实时数据,对空间环境观测提出了更高的要求。
子午工程一期、二期融合完成后,282台(套)设备可连续监测40种空间环境要素,为利用自主数据开展研究提供了核心数据支撑。基于子午工程数据开展的区域空间环境特征、太阳风−磁层−电离层−中高层大气耦合过程、基本物理实验验证等方面取得了大量的科学进展[4−5,12−13]。近年来,随着子午数据的不断积累,在空间环境扰动特征提取、自主指数构建、预报建模等方面也有一些初步的成果,这些方法经过逐步转化,将给空间环境预报保障提供新的技术支撑。
子午工程在怀柔桥梓、江苏赣榆两站配备有太阳光球磁场和色球望远镜,在明安图、稻城、荣成等站布设有多种频段的太阳射电观测设备,频段叠加覆盖30 MHz~15 GHz。太阳光球磁场和色球Hα的观测能够给出太阳活动区和耀斑爆发等信息,宽频段的射电观测能够提供多种类型射电暴的信息。中国科学院国家天文台研究团队利用自主开发的太阳领域金乌大模型与子午工程江苏赣榆站全日面矢量磁像仪结合,形成JW−ASTClaw(金乌−龙虾)多智能体[14],是首个部署在实际运行太阳望远镜上、由大语言模型驱动的端到端多智能体自主控制系统。该系统大量利用子午工程太阳观测归档数据开展活动区识别验证,在10个验证日期上,JW−ASTClaw检测到102个活动区,SWPC为100个,说明自主识别的活动区与国际发布的数据吻合性很好。山东大学研究团队利用子午工程荣成站米波射电频谱仪数据,基于YOLO(You Only Look Once)神经网络算法,发展了太阳射电暴的自动识别技术,对III型射电暴和II型射电暴的平均预测精度(交并比为0.5时)达0.746和0.764[15],为进一步利用射电爆发获取日冕物质抛射速度奠定了基础。李冉等[16]利用稻城太阳射电望远镜射电频谱观测数据,依据II型射电暴在频谱中的漂移速度,确定2022年4月17日一次II型太阳射电爆发对应的初始激波速度,激波到达时间的预报值与观测值之间偏差仅为0.66 h。这些射电暴的参数提供了太阳日冕物质抛射的信息,对于日冕物质抛射对地效应的预报具有深远意义。
太阳地磁活动指数是空间环境预报中的常规预报参量,其中地磁指数主要是德国地球科学研究中心(German Research Centre for Geosciences,GFZ)提供。子午工程在31个台站都配备有磁通门磁力仪,能够进行地磁三分量的监测,中国科学院国家空间科学中心基于磁通门磁力仪的地磁观测数据,发展了自主台站地磁扰动K指数的计算方法[17],进一步估算出国际通用的Kp、Ap指数,估值得到的Kp与观测值相关系数达0.89,83%结果的绝对误差小于1.0。在国际数据不及时和缺乏的情况下完全可以替代国际数据进行地磁活动的分析和预测。
Dst指数作为衡量地磁活动的另一项重要指标,可以清晰反映磁暴的发生时间和强度。李晖等[18]选择拉萨、成都、武汉、杭州4个台站2010—2022年的地磁数据,从地磁H分量中消除平均太阳静日变化,得到磁场扰动值。利用4个台站的磁场扰动值和地磁纬度,计算中国地球Dst指数(cDst)并发布数据集,为地磁扰动的判断提供数据支持。
电离层具有显著的区域变化特征,子午工程沿100°E、120°E和30°N、40°N布设多个台站,能够连续获取东亚扇区多纬度、多经度的电离层数据,填补了该区域长期缺乏系统观测的空白,为区域电离层预报模型构建提供关键数据来源。子午工程一期建立以来,在电离层模式研究和预报上有了较大的发展。表2列出了主要的电离层模型及评估结果。可以看出基于自主数据发展的预报模式在分辨率和准确度上优于国际预报产品。
当前,国际上的全球和区域电离层TEC地图都是基于观测采用求解函数、三角函数等方法构建。徐珩等[22]借助子午工程40个台站的全球导航卫星系统(global navigation satellite system,GNSS)观测数据,结合周边其他台站数据,采用非积分球谐函数方法构建中国区域TEC模型,模型能够反映平静和暴时电离层的大尺度变化特征。
为了更精确地反映电离层的变化,阿尔察等[23]基于子午工程GNSS观测构建中国电离层空间天气同化系统,融合美国全球定位系统(global positioning system,GPS)、格洛纳斯、北斗、伽利略4系统多模观测数据,采用三维变分同化方法,可生成覆盖中国及周边区域(15°N~55°N,70°E~140°E)的网格点TEC、差分TEC及不均匀体指数(rate of TEC index,ROTI)产品,时空分辨率达1°×1°×15 min,已在中国科学院空间环境预报中心网站每日常规更新[26]。在二维同化的基础上,结合天地基电子密度观测数据,阿尔察等[27]又进一步构建了三维电子密度同化模型。
在中国区域TEC的预报方面,袁天娇等[24]利用深度递归神经网络(recurrent neural network, RNN)发展了单站的TEC预报模型,RMSE在平静期小于3.2 TECU,暴时小于4.8 TECU。并进一步发展了基于长短期记忆网络(long short−term memory,LSTM)的中国区域电离层TEC预报模型。杨佳玥等[25]进一步将这种预报方法拓展到全球,能够提供全球1~7 d的TEC格点化预报。
子午工程三亚非相干散射雷达、高频雷达、数字测高仪和电离层TEC与闪烁监测仪设备能对电离层不规则体进行不同角度的观测[28]。高彭东等[29]利用时空卷积门循环神经网络与超分辨率增强深度残差网络,建立了针对海南观测站电离图有无扩展F现象的高分辨率短时预测模型,对扩展F发生情况的预测准确率均高于94%。深空实验室研究团队基于子午工程电离层数字测高仪数据,结合天基卫星观测,开发了E区不均匀体的气候学重构模型SELF−ANN(Sporadic E Layer Forecast using Artificial Neural Networks),可提供覆盖2002—2025年完整太阳活动周期的预报信息[30]。
电离层TEC与闪烁监测仪数据能够直接给出闪烁指数的观测,为建立中国区域的电离层闪烁模型提供了较好的数据来源,耿威等[19]利用子午工程一期6个低纬度台站电离层闪烁观测数据,基于克里金法生成近实时电离层闪烁指数S4区域分布图。相较于经验模型得到的结果,该分布图能够更好地体现中国南方地区S4的区域特征及其变化情况。赵秀宽等[21]采用ROTI表征赤道等离子体泡扰动的发生情况,构建了面向区域赤道等离子体泡扰动短时预测的时空深度学习模型,相比传统机器学习模型提升效果显著。高志旭等[20]提出基于动态时空分解框架的ISNet(ionospheric scintillation network)神经网络模型,实现了低纬地区电离层闪烁指数分布提前1 h的预报。上述模型的评估结果如表2所示。
中高层大气重力波与中尺度行进式电离层扰动是空间天气的重要驱动因素,但传统人工分析效率低、主观性强,子午工程研究团队针对子午工程海量气辉图像,构建了基于机器学习的智能识别与参数提取工具[31]:采用卷积神经网络分类模型筛选晴朗夜空图像,羟基(OH)气辉准确率达99%,中性氧原子(OI,I表示中性态)气辉达96.9%。
除了支持空间环境预报模型的开发外,子午工程数据已深度融入空间环境预报全流程,表3总结了当前子午工程数据在预报业务中发挥的作用和效果,表明子午工程数据的作用主要体现在提供自主数据替代、部分数据更新率更高、区域电离层描述分辨率更高等方面。
常规预报是指面向公众发布的空间环境每日预报、周报、月报等服务产品,以及空间环境状况的实时数据展示,通过网页、公众号和手机APP等方式发布。常规预报业务中,预报员需要根据当前和历史数据反映的空间环境状况进行分析,结合模型的预报结果,综合分析给出空间环境预报。子午工程北京怀柔、江苏赣榆等台站提供光球矢量磁场、色球Hα成像等太阳活动观测数据,支持预报员通过太阳活动区参数与耀斑爆发情况进行太阳活动的分析,宇宙线、地磁、电离层数据支持空间环境预报中心网页(www.sepc.ac.cn)空间环境的实况现报。基于子午工程数据构建的Kp/Ap指数提供地磁指数短临预报,实现针对国际预报模型的完全替代。基于子午工程开发的TEC同化模型、提前1~24 h的预报模型以及全球提前1~7 d的预报模型都在空间环境预报中心进行了应用转化,能够常规提供现报和预报产品。
中国科学院国家空间科学中心负责空间站空间环境保障任务,在具体的保障工作中,需要提供太阳地磁活动指数预报、灾害性事件预警以及辐射环境、大气环境和电离层环境参数预报。子午工程地磁数据反演的Kp/Ap指数,以及太阳射电观测提取的F10.7指数,为空间站任务地磁环境的预报、辐射环境、大气环境的计算提供支持。以这些指数为基础进行未来太阳F10.7和地磁Ap指数/Kp指数的中长期预报,为空间站轨道预报提供输入数据。图3给出了当前空间站任务保障中应用的子午工程数据。
当发生重大空间环境事件和重大事件时,子午工程支持向载人空间站空间应用系统提交空间环境事件警报产品,并进一步详细分析太阳活动、行星际、地磁和电离层数据,给出空间环境发展态势预报和影响分析。图4为神舟二十号飞船返回期间基于子午数据的地磁指数预报与实测值以及国际权威机构预测的对比,说明基于自主数据能够较好地支持地磁活动的预报。
在空间科学先导专项卫星工程任务中,目前在轨运行的有先导一期暗物质卫星“悟空”号、硬X射线望远镜“慧眼”号,先导二期引力波暴高能电磁对应体全天监视器“怀柔一号”、先进天基太阳观测台“夸父一号”、爱因斯坦探针“天关”卫星、天基多波段空间变源监视器(Space−based multi−band astronomical Variable Objects Monitor,SVOM)卫星以及2026年5月发射的中欧联合研制的太阳风−磁层相互作用全景成像(Solar wind Magnetosphere Ionosphere Link Explorer,SMILE)“微笑”卫星。这些卫星经历的空间环境包括辐射带、大气环境、等离子体环境等,这些空间环境要素可能会对在轨飞行任务造成不同程度的影响。
首先,先导卫星在轨运行期间需要太阳X射线、行星际太阳风、高能粒子、地磁指数等多种类型的空间环境背景数据,其中,子午工程对宇宙线中子/μ子强度、地磁Kp/Ap/AE/Dst指数、中国地磁台站K指数都提供了重要的数据支持。
其次,根据先导一期和二期各个卫星的轨道、载荷等特征,还需要特定的空间环境态势保障产品。例如为“悟空”“慧眼”等轨道较低的卫星,提供卫星轨道大气密度变化产品,为在轨6颗卫星提供穿越南大西洋异常区预报产品等。针对倾角高的“悟空”号卫星,在发生太阳质子事件时提供太阳质子轨道分布产品,在发生地磁暴时提供卫星表面充电环境产品。这些产品,都得到了子午工程太阳活动和地磁数据的支持,用于地磁暴事件的判断和模型计算的指数输入。例如在2024年5月和10月超级地磁暴事件和超强太阳质子事件期间,子午工程数据分析结果辅助部分卫星采取相关防护措施,保障卫星安全。
除了对空间站和先导卫星的预报保障任务支持,子午工程数据在卫星运管、北斗导航、电波技术系统中也逐渐得到了广泛的应用。在卫星运控的空间环境保障产品中,子午工程数据支持为卫星的在轨运行安全提供太阳活动、地磁活动预报数据支持,基于空间环境数据对星敏复位、存储器异常、中央处理器(central processing unit,CPU)重启等卫星故障开展溯源分析,判定故障与空间环境的关联性。
同时,子午工程数据支持导航定位、短波通信等专项任务的电波信号扰动的溯源诊断,例如2023年5月4日太阳射电爆发,通过子午工程GNSS观测可以识别出北斗卫星信号受射电暴影响,为GNSS用户和相关部门的扰动溯源提供支撑。
子午工程作为目前全球地基观测覆盖范围最广、监测要素最全、综合能力最强的空间环境监测网络,能够提供日地空间环境全链条观测数据。目前子午工程的太阳光学观测、射电10.7 cm流量观测、宇宙线、地磁和电离层监测数据已经在空间环境预报中得到了应用,有效支撑了空间站、先导卫星和其他专项用户的预报保障任务。
然而,子午工程数据在预报业务中的应用仍面临多重挑战:台站分布范围广、运行环境差异显著,导致数据在实时汇聚、连续传输和异常处置等环节存在较大不确定性。为此,应在保障连续性观测的基础上,进一步优化数据分发策略,提高数据接入预报中心业务系统的时效性,为预报分析提供及时支撑。同时,子午工程二期建成时间较短,数据质量仍需在长期运行中进一步验证与改进,应完善数据质控与标定流程,确保数据准确性,并系统整理历史数据,形成可检索、可调用、可复用的数据集。对于二期新建的新设备、新数据,需要进一步挖掘数据在预报中的应用价值,开发处理和分析方法,提升预报业务中自主数据的支撑能力。
另一方面,人工智能虽已成为当前预报技术的重要发展方向,但空间环境数据具有多源异构、模态复杂等特点,且极端事件样本稀缺,致使人工智能模型对扰动事件的预报能力仍显不足;现有模型多基于历史数据构建,对新数据的泛化能力亦有限。未来应加强人工智能模型在常规预报业务中的应用验证,通过持续改进优化,推动智能预报技术在实际预报中落地应用。
总之,尽管面临挑战,但子午工程数据在空间环境预报建模、常规预报业务、重大航天任务、卫星安全运行等专项任务中已经且必将会发挥越来越重要的作用。面向航天强国与空间安全战略需求,未来需进一步深度挖掘子午工程多源观测数据价值,构建智能化预报应用体系,全面支撑中国空间环境服务能力与航天活动安全保障能力跃升。
  • 国家重点研发计划项目(2024YFC2206902)
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doi: 10.3981/j.issn.1000-7857.2026.06.00049
  • 接收时间:2026-06-23
  • 首发时间:2026-11-03
  • 出版时间:2026-09-28
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  • 收稿日期:2026-06-23
  • 修回日期:2026-09-11
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国家重点研发计划项目(2024YFC2206902)
作者信息
    1中国科学院国家空间科学中心,北京 100190
    2中国科学院大学,北京 101408

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陈艳红(通信作者),研究员,研究方向为空间环境预报,电子信箱:
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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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