Article(id=1322506167230779949, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506166672937516, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.12.552, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1733241600000, receivedDateStr=2024-12-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793239641243, onlineDateStr=2026-10-29, pubDate=1755187200000, pubDateStr=2025-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793239641243, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793239641243, creator=13701087609, updateTime=1793239641243, updator=13701087609, issue=Issue{id=1322506166672937516, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='8', pageStart='771', pageEnd='880', issueExtLink='null', onlineDate='null', pubDate='1755187200000', pubDateStr='2025-08-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793239641110, creator='13701087609', updateTime=1793239641110, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=771, endPage=780, ext={EN=ArticleExt(id=1322506169021747758, articleId=1322506167230779949, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=GNSS-Retrieved PWV in the Greenland Region, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Based on hourly-resolution zenith tropospheric delay(ZTD) data from 35 GNSS stations in the Greenland region spanning the period from 2010 to 2021, combined with ERA5 reanalysis datasets and Tm model, precipitable water vapor(PWV) over the region was retrieved. Subsequently, the principal seasonal and diurnal variation characteristics, including annual, semi-annual, diurnal, and semi-diurnal cycles, were investigated. The results indicate that the GNSS stations in Greenland exhibit a certain degree of autocorrelation, with the summer season showing weaker autocorrelation compared to other seasons. The amplitude of the annual component ranges from 2 to 7 mm, while that of the semi-annual component ranges from 0 to 3 mm, with relatively minor interannual variations in their phases. The maximum values occur in summer, and the minimum values in winter. The diurnal component is only pronounced at a few stations, with amplitudes around 0.08 mm. After removing the annual, semi-annual, and diurnal components from the time series to obtain the residual time series, the maximum RMSE ranges from 1.30 to 3.65 mm, with an average RMSE of 2.08 mm. Notably, the RMSE values are relatively higher in the northern region and lower in the southern region.

, authors=Shengkai ZHANG1, 2, Jie MA1, 2, 3, Li GONG1, 2, Jintao LEI4, Wenhao LI5, Feng XIAO1, 2, *, authorsList=Shengkai ZHANG, Jie MA, Li GONG, Jintao LEI, Wenhao LI, Feng XIAO, authorCompany=null, correspAuthors=Feng XIAO, authorNote=

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

, 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=1322506174004580939, articleId=1322506167230779949, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=格陵兰地区GNSS反演PWV分析, columnId=1322506169105633839, journalTitle=大地测量与地球动力学, columnName=现代大地测量, runingTitle=null, highlight=null, articleAbstract=

以2010—2021年格陵兰地区的35个GNSS测站的小时分辨率的天顶对流层延迟(ZTD)数据为基础,结合ERA5再分析资料和Tm模型,对该区域的大气可降水量(PWV)进行求解。随后,对其周年、半周年、周日、半周日等主要季节和日变化特征进行研究。结果表明,格陵兰地区测站存在一定程度的自相关性,且夏季自相关程度弱于其他季节。周年项振幅范围为2~7 mm,半周年项振幅范围为0~3 mm,其相位在年际间变化较小,最高值出现在夏季,最低值出现在冬季。仅有少数几个测站周日项比较明显,振幅在0.08 mm左右。去除时间序列中的周年、半周年、周日项得到残差时间序列,最大的RMSE范围为1.30~3.65 mm,平均RMSE为2.08 mm,其中,北部地区RMSE偏大,南部地区较小。

, authors=张胜凯1, 2, 马杰1, 2, 3, 龚力1, 2, 雷锦韬4, 李文浩5, 肖峰1, 2, *, authorsList=张胜凯, 马杰, 龚力, 雷锦韬, 李文浩, 肖峰, authorCompany=null, correspAuthors=肖峰, authorNote=

张胜凯,教授,博士生导师,主要研究方向为极地大地测量学与地球物理学,E-mail:

, correspAuthorsNote=

肖峰,副研究员,主要研究方向为极地冰盖和海冰动态变化监测,E-mail:

XIAO Feng, associate researcher, majors in polar ice sheet and sea ice dynamic monitoring, E-mail:

, copyrightStatement=版权所有,未经授权,不得转载。, copyrightOwner=大地测量与地球动力学编辑部, extLink=null, articleAbsUrl=null, sourceXml=QA7yZnTvrtmkw0l/+3WnGA==, magXml=yjAPRD+7sPR0t2fWwwKCAw==, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=H0zzNpbRpT2FlZao70QlcA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=85LdnyJdZIT2+Vig+jv5Sg==, mapNumber=null, fund=null)}, authors=[Author(id=1322506175753605725, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506167230779949, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zskai@whu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322506176089150048, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506167230779949, authorId=1322506175753605725, language=EN, stringName=Shengkai ZHANG, firstName=Shengkai, middleName=null, lastName=ZHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=1 Chinese Antarctic Center of Surveying and Mapping, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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图(c) 中, 圆内数字为ZTD振幅, 单位mm; 图(d) 中, 圆内数字为PWV振幅, 单位mm

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图(b) 中, 圆内数字为PWV振幅, 单位mm

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Statistical results of ADF test of time series data for various stations in Greenland

, figureFileSmall=null, figureFileBig=null, tableContent=
测站 统计量 q值 检验结果 测站 统计量 q值 检验结果
VFDG -5.322 4.936×10-6 平稳 JWLF -5.271 6.288×10-6 平稳
KAGA -5.505 2.032×10-6 平稳 JGBL -4.965 2.599×10-5 平稳
MARG -4.776 6.037×10-5 平稳 KUAQ -5.908 2.675×10-7 平稳
KMOR -5.424 3.013×10-6 平稳 KBUG -7.980 2.632×10-12 平稳
WTHG -4.720 7.728×10-5 平稳 GROK -4.510 1.885×10-4 平稳
SCOR -5.030 1.937×10-5 平稳 RINK -4.896 3.553×10-5 平稳
LBIB -4.875 3.893×10-5 平稳 MSVG -5.097 1.422×10-5 平稳
KSNB -5.739 6.342×10-7 平稳 MIK2 -6.833 1.877×10-9 平稳
HEL2 -6.274 3.933×10-8 平稳 YMER -4.912 3.301×10-5 平稳
PLPK -5.876 3.155×10-7 平稳 HMBG -5.124 1.257×10-5 平稳
DANE -4.480 2.134×10-4 平稳 LYNS -7.115 3.860×10-10 平稳
ASKY -5.119 1.286×10-5 平稳 KMJP -5.380 3.739×10-6 平稳
HRDG -5.261 6.587×10-6 平稳 THU2 -4.751 6.749×10-5 平稳
NRSK -4.658 1.008×10-4 平稳 HJOR -5.762 5.649×10-7 平稳
SRMP -4.982 2.409×10-5 平稳 QAQ1 -4.709 8.090×10-5 平稳
GMMA -4.611 1.235×10-4 平稳 DGJG -5.074 1.584×10-5 平稳
BLAS -4.664 9.817×10-5 平稳 LEFN -4.455 2.366×10-4 平稳
TIMM -5.713 7.231×10-7 平稳
), ArticleFig(id=1322506193071887019, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506167230779949, language=CN, label=表1, caption=

格陵兰地区各测站时间序列数据ADF检验统计结果

, figureFileSmall=null, figureFileBig=null, tableContent=
测站 统计量 q值 检验结果 测站 统计量 q值 检验结果
VFDG -5.322 4.936×10-6 平稳 JWLF -5.271 6.288×10-6 平稳
KAGA -5.505 2.032×10-6 平稳 JGBL -4.965 2.599×10-5 平稳
MARG -4.776 6.037×10-5 平稳 KUAQ -5.908 2.675×10-7 平稳
KMOR -5.424 3.013×10-6 平稳 KBUG -7.980 2.632×10-12 平稳
WTHG -4.720 7.728×10-5 平稳 GROK -4.510 1.885×10-4 平稳
SCOR -5.030 1.937×10-5 平稳 RINK -4.896 3.553×10-5 平稳
LBIB -4.875 3.893×10-5 平稳 MSVG -5.097 1.422×10-5 平稳
KSNB -5.739 6.342×10-7 平稳 MIK2 -6.833 1.877×10-9 平稳
HEL2 -6.274 3.933×10-8 平稳 YMER -4.912 3.301×10-5 平稳
PLPK -5.876 3.155×10-7 平稳 HMBG -5.124 1.257×10-5 平稳
DANE -4.480 2.134×10-4 平稳 LYNS -7.115 3.860×10-10 平稳
ASKY -5.119 1.286×10-5 平稳 KMJP -5.380 3.739×10-6 平稳
HRDG -5.261 6.587×10-6 平稳 THU2 -4.751 6.749×10-5 平稳
NRSK -4.658 1.008×10-4 平稳 HJOR -5.762 5.649×10-7 平稳
SRMP -4.982 2.409×10-5 平稳 QAQ1 -4.709 8.090×10-5 平稳
GMMA -4.611 1.235×10-4 平稳 DGJG -5.074 1.584×10-5 平稳
BLAS -4.664 9.817×10-5 平稳 LEFN -4.455 2.366×10-4 平稳
TIMM -5.713 7.231×10-7 平稳
), ArticleFig(id=1322506193147384492, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506167230779949, language=EN, label=Tab. 2, caption=

Statistical results of the seasonal MK test of PWV in Greenland

, figureFileSmall=null, figureFileBig=null, tableContent=
测站 Z q值 斜率/mm·a-1 检验结果 测站 Z q值 斜率/mm·a-1 检验结果
VFDG -1.806 0.070 89 -0.010 52 没有趋势 JWLF 0.358 0.720 04 0.001 91 没有趋势
KAGA -3.637 0.000 28 -0.028 06 下降 JGBL -1.033 0.301 78 -0.004 37 没有趋势
MARG -1.650 0.098 93 -0.007 16 没有趋势 KUAQ -5.461 0.000 00 -0.041 71 下降
KMOR 5.589 0.000 00 0.021 46 上升 KBUG -1.735 0.082 70 -0.020 11 没有趋势
WTHG -1.664 0.096 06 -0.009 66 没有趋势 GROK -0.280 0.779 22 -0.001 13 没有趋势
SCOR 0.706 0.480 09 0.007 30 没有趋势 RINK -3.204 0.001 35 -0.015 27 下降
LBIB -1.537 0.124 41 -0.006 43 没有趋势 MSVG -0.578 0.562 99 -0.004 57 没有趋势
KSNB -3.289 0.001 00 -0.029 63 下降 MIK2 -3.219 0.001 29 -0.033 00 下降
HEL2 -4.063 0.000 05 -0.042 03 下降 YMER -1.107 0.268 23 -0.006 28 没有趋势
PLPK -2.551 0.010 73 -0.035 56 下降 HMBG -3.925 0.000 09 -0.015 87 下降
DANE 1.856 0.063 47 0.015 91 没有趋势 LYNS -1.430 0.152 70 -0.021 10 没有趋势
ASKY -4.056 0.000 05 -0.020 28 下降 KMJP -0.685 0.493 43 -0.003 17 没有趋势
HRDG -1.451 0.146 68 -0.005 50 没有趋势 THU2 -0.295 0.768 35 -0.001 95 没有趋势
NRSK -0.770 0.441 28 -0.005 08 没有趋势 HJOR -1.160 0.245 90 -0.013 67 没有趋势
SRMP -2.523 0.011 64 -0.015 27 下降 QAQ1 -2.452 0.014 21 -0.030 19 下降
GMMA 0.241 0.809 32 0.001 70 没有趋势 DGJG -1.104 0.269 77 -0.005 52 没有趋势
BLAS -2.402 0.016 29 -0.011 73 下降 LEFN -3.843 0.000 12 -0.017 04 下降
TIMM -2.402 0.016 29 -0.030 85 下降
), ArticleFig(id=1322506193227076269, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506167230779949, language=CN, label=表2, caption=

格陵兰地区PWV季节性MK检验统计结果

, figureFileSmall=null, figureFileBig=null, tableContent=
测站 Z q值 斜率/mm·a-1 检验结果 测站 Z q值 斜率/mm·a-1 检验结果
VFDG -1.806 0.070 89 -0.010 52 没有趋势 JWLF 0.358 0.720 04 0.001 91 没有趋势
KAGA -3.637 0.000 28 -0.028 06 下降 JGBL -1.033 0.301 78 -0.004 37 没有趋势
MARG -1.650 0.098 93 -0.007 16 没有趋势 KUAQ -5.461 0.000 00 -0.041 71 下降
KMOR 5.589 0.000 00 0.021 46 上升 KBUG -1.735 0.082 70 -0.020 11 没有趋势
WTHG -1.664 0.096 06 -0.009 66 没有趋势 GROK -0.280 0.779 22 -0.001 13 没有趋势
SCOR 0.706 0.480 09 0.007 30 没有趋势 RINK -3.204 0.001 35 -0.015 27 下降
LBIB -1.537 0.124 41 -0.006 43 没有趋势 MSVG -0.578 0.562 99 -0.004 57 没有趋势
KSNB -3.289 0.001 00 -0.029 63 下降 MIK2 -3.219 0.001 29 -0.033 00 下降
HEL2 -4.063 0.000 05 -0.042 03 下降 YMER -1.107 0.268 23 -0.006 28 没有趋势
PLPK -2.551 0.010 73 -0.035 56 下降 HMBG -3.925 0.000 09 -0.015 87 下降
DANE 1.856 0.063 47 0.015 91 没有趋势 LYNS -1.430 0.152 70 -0.021 10 没有趋势
ASKY -4.056 0.000 05 -0.020 28 下降 KMJP -0.685 0.493 43 -0.003 17 没有趋势
HRDG -1.451 0.146 68 -0.005 50 没有趋势 THU2 -0.295 0.768 35 -0.001 95 没有趋势
NRSK -0.770 0.441 28 -0.005 08 没有趋势 HJOR -1.160 0.245 90 -0.013 67 没有趋势
SRMP -2.523 0.011 64 -0.015 27 下降 QAQ1 -2.452 0.014 21 -0.030 19 下降
GMMA 0.241 0.809 32 0.001 70 没有趋势 DGJG -1.104 0.269 77 -0.005 52 没有趋势
BLAS -2.402 0.016 29 -0.011 73 下降 LEFN -3.843 0.000 12 -0.017 04 下降
TIMM -2.402 0.016 29 -0.030 85 下降
), ArticleFig(id=1322506193336128174, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506167230779949, language=EN, label=Tab. 3, caption=

BIC statistics of 17 models from 35 stations in Greenland

, figureFileSmall=null, figureFileBig=null, tableContent=
测站 WN FNWN PLWN GGMWN RWFNWN AR1 AR2 AR3 AR4
VFDG 15 906.3 14 380.6 14 387.8 14 062.6 14 389.0 14 069.4 14 050.7 14 037.5 14 043.2
KAGA 18 155.0 16 840.0 16 847.4 16 555.5 16 848.3 16 563.8 16 543.1 16 533.8 16 542.1
MARG 14 024.5 12 049.6 12 030.2 11 767.8 12 058.0 11 759.2 11 754.5 11 745.4 11 753.7
KMOR 14 510.9 12 487.5 12 482.5 12 268.3 12 495.8 12 257.4 12 252.4 12 222.3 12 225.3
WTHG 16 453.0 14 758.1 14 762.9 14 502.9 14 766.5 14 490.0 14 492.6 14 494.6 14 502.3
SCOR 21 374.8 19 168.8 19 123.5 18 769.8 19 177.2 18 780.4 18 757.5 18 758.5 18 766.6
LBIB 14 250.7 12 968.0 12 969.7 12 761.2 12 976.4 12 746.6 12 750.6 12 741.0 12 749.3
KSNB 18 227.5 16 348.9 16 350.4 16 085.9 16 357.3 16 083.7 16 070.6 16 031.1 16 036.2
HEL2 20 643.2 18 710.4 18 707.4 18 433.9 18 718.8 18 430.2 18 418.6 18 382.7 18 389.9
PLPK 22 234.6 20 160.7 20 134.3 19 767.6 20 169.1 19 789.9 19 752.2 19 727.2 19 734.7
DANE 19 310.0 17 212.4 17 184.4 16 870.3 17 220.7 16 867.5 16 858.2 16 856.9 16 864.4
ASKY 15 804.5 14 117.8 14 124.9 13 904.9 14 126.1 13 895.3 13 891.6 13 878.7 13 879.3
HRDG 13 290.0 11 855.8 11 861.2 11 650.6 11 864.1 11 640.9 11 638.9 11 629.7 11 637.0
NRSK 17 765.6 15 764.1 15 734.1 15 368.0 15 772.5 15 385.5 15 356.0 15 354.1 15 361.6
SRMP 17 316.5 15 700.0 15 707.6 15 440.7 15 708.4 15 441.9 15 428.1 15 410.6 15 418.9
GMMA 18 004.4 15 961.8 15 926.0 15 528.3 15 970.2 15 558.0 15 514.1 15 504.5 15 512.1
BLAS 16 137.0 14 297.6 14 295.6 13 977.1 14 306.0 13 988.5 13 964.0 13 947.7 13 956.0
TIMM 22 864.1 21 084.0 21 085.2 20 750.7 21 092.4 20 765.6 20 734.9 20 685.9 20 693.5
JWLF 14 909.7 13 196.6 13 204.5 12 985.6 13 205.0 12 973.5 12 973.3 12 961.9 12 970.0
JGBL 14 477.9 12 789.9 12 798.1 12 595.6 12 798.3 12 580.2 12 584.6 12 577.5 12 585.7
KUAQ 18 668.7 16 816.8 16 814.8 16 509.5 16 825.2 16 513.0 16 496.5 16 481.0 16 488.6
KBUG 22 219.6 20 537.9 20 545.1 20 239.1 20 546.3 20 250.6 20 224.3 20 184.8 20 192.9
GROK 16 164.9 14 342.5 14 341.6 14 025.0 14 350.9 14 031.8 14 012.6 14 001.6 14 009.9
RINK 14 194.9 12 448.4 12 448.6 12 179.7 12 456.8 12 173.5 12 168.8 12 172.9 12 181.0
MSVG 19 566.6 17 328.8 17 254.3 16 832.9 17 337.2 16 863.2 16 823.1 16 831.0 16 839.2
MIK2 20 389.7 18 327.6 18 301.5 17 953.1 18 335.9 17 967.1 17 938.9 17 918.3 17 926.2
YMER 14 846.4 13 466.6 13 471.1 13 249.4 13 475.0 13 238.6 13 237.7 13 222.4 13 229.9
HMBG 13 445.7 12 150.7 12 152.8 11 957.0 12 159.1 11 940.6 11 947.8 11 948.4 11 956.7
LYNS 23 148.5 21 195.6 21 189.5 20 873.6 21 204.0 20 884.5 20 858.2 20 813.9 20 821.8
KMJP 15 438.1 13 710.9 13 719.0 13 516.9 13 719.3 13 501.7 13 506.2 13 505.7 13 509.2
THU2 16 211.6 14 221.6 14 203.7 13 962.9 14 230.0 13 950.9 13 948.5 13 926.3 13 934.6
HJOR 21 623.1 19 835.1 19 836.1 19 515.8 19 843.5 19 525.9 19 501.2 19 462.3 19 470.6
QAQ1 22 440.2 20 996.5 21 004.7 20 691.0 21 004.9 20 699.1 20 678.6 20 666.1 20 674.4
DGJG 14 858.8 13 302.2 13 310.2 13 045.3 13 310.5 13 036.0 13 034.6 13 035.3 13 043.7
LEFN 14 968.8 13 375.6 13 383.9 13 145.3 13 384.0 13 136.7 13 132.5 13 110.2 13 117.1
测站 AR1WN AR2WN AR3WN AR4WN ARMA1WN ARMA2WN ARMA3WN ARMA4WN
VFDG 14 077.8 14 059.1 14 045.9 14 051.6 14 051.2 14 041.6 14 049.7 14 058.1
KAGA 16 572.2 16 551.4 16 542.1 16 550.4 16 544.2 16 542.1 16 550.2 16 557.8
MARG 11 767.5 11 762.8 11 753.7 11 762.1 11 759.7 11 757.7 11 761.6 11 764.6
KMOR 12 265.7 12 260.7 12 225.3 12 233.3 12 255.6 12 246.2 12 225.5 12 242.0
WTHG 14 498.4 14 501.0 14 503.0 14 510.6 14 500.2 14 502.2 14 510.5 14 518.9
SCOR 18 788.8 18 765.8 18 766.7 18 775.0 18 762.3 18 767.8 18 771.6 18 783.1
LBIB 12 755.0 12 758.9 12 749.4 12 757.6 12 757.6 12 751.5 12 757.5 12 766.0
KSNB 16 092.1 16 079.0 16 036.3 16 044.5 16 069.7 16 057.2 16 044.3 16 051.1
HEL2 18 438.6 18 427.0 18 390.0 18 398.3 18 418.8 18 405.2 18 398.3 18 406.7
PLPK 19 798.3 19 760.6 19 734.6 19 742.9 19 747.7 19 745.3 19 742.9 19 751.3
DANE 16 875.9 16 866.6 16 865.3 16 872.8 16 863.7 16 864.6 16 872.8 16 881.1
ASKY 13 903.6 13 900.0 13 878.3 13 884.4 13 896.8 13 895.3 13 873.9 13 882.2
HRDG 11 649.2 11 647.3 11 637.3 11 645.2 11 644.4 11 641.4 11 626.0 11 632.6
NRSK 15 393.9 15 364.4 15 362.5 15 370.0 15 358.6 15 360.5 15 368.8 15 376.8
SRMP 15 450.3 15 436.5 15 419.0 15 427.3 15 429.6 15 420.0 15 426.6 15 435.7
GMMA 15 566.4 15 522.5 15 512.9 15 520.5 15 512.2 15 513.2 15 520.6 15 528.9
BLAS 13 996.9 13 972.4 13 956.1 13 964.4 13 963.5 13 958.4 13 964.3 13 970.1
TIMM 20 774.0 20 743.3 20 694.3 20 701.8 20 723.8 20 698.8 20 701.6 20 701.9
JWLF 12 981.9 12 981.7 12 970.1 12 978.4 12 979.3 12 973.5 12 961.6 12 981.8
JGBL 12 588.5 12 593.0 12 585.8 12 594.1 12 592.0 12 585.7 12 593.4 12 593.2
KUAQ 16 521.4 16 504.9 16 489.4 16 497.0 16 497.8 16 488.3 16 495.6 16 503.9
KBUG 20 259.0 20 232.7 20 193.2 20 201.3 20 217.5 20 200.1 20 200.8 20 207.9
GROK 14 040.2 14 021.0 14 009.9 14 018.3 14 014.5 14 013.5 14 018.2 14 026.7
RINK 12 181.9 12 177.1 12 180.9 12 188.7 12 175.8 12 180.3 12 188.6 12 188.9
MSVG 16 871.6 16 831.5 16 839.2 16 847.6 16 831.3 16 839.4 16 847.6 16 856.0
MIK2 17 975.5 17 947.3 17 926.7 17 934.6 17 936.7 17 928.4 17 934.2 17 942.4
YMER 13 247.0 13 246.1 13 229.6 13 237.1 13 243.0 13 240.5 13 236.2 13 243.7
HMBG 11 948.9 11 956.1 11 956.7 11 965.1 11 955.9 11 957.7 11 964.9 11 973.3
LYNS 20 892.9 20 866.5 20 822.2 20 830.2 20 851.4 20 834.2 20 829.9 20 837.1
KMJP 13 510.0 13 514.5 13 508.0 13 516.3 13 513.9 13 516.9 13 496.1 13 504.5
THU2 13 959.2 13 956.8 13 934.6 13 943.0 13 953.0 13 941.8 13 943.0 13 951.2
HJOR 19 534.3 19 509.6 19 470.6 19 479.0 19 495.5 19 478.8 19 479.0 19 487.2
QAQ1 20 707.5 20 687.0 20 674.4 20 682.7 20 679.3 20 678.6 20 682.7 20 691.1
DGJG 13 044.4 13 043.0 13 043.7 13 052.1 13 041.4 13 044.1 13 050.3 13 060.5
LEFN 13 145.1 13 140.8 13 118.5 13 125.5 13135.9 13 122.0 13 124.7 13 133.4
), ArticleFig(id=1322506193445180079, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506167230779949, language=CN, label=表3, caption=

格陵兰地区35个测站的17种模型BIC统计

, figureFileSmall=null, figureFileBig=null, tableContent=
测站 WN FNWN PLWN GGMWN RWFNWN AR1 AR2 AR3 AR4
VFDG 15 906.3 14 380.6 14 387.8 14 062.6 14 389.0 14 069.4 14 050.7 14 037.5 14 043.2
KAGA 18 155.0 16 840.0 16 847.4 16 555.5 16 848.3 16 563.8 16 543.1 16 533.8 16 542.1
MARG 14 024.5 12 049.6 12 030.2 11 767.8 12 058.0 11 759.2 11 754.5 11 745.4 11 753.7
KMOR 14 510.9 12 487.5 12 482.5 12 268.3 12 495.8 12 257.4 12 252.4 12 222.3 12 225.3
WTHG 16 453.0 14 758.1 14 762.9 14 502.9 14 766.5 14 490.0 14 492.6 14 494.6 14 502.3
SCOR 21 374.8 19 168.8 19 123.5 18 769.8 19 177.2 18 780.4 18 757.5 18 758.5 18 766.6
LBIB 14 250.7 12 968.0 12 969.7 12 761.2 12 976.4 12 746.6 12 750.6 12 741.0 12 749.3
KSNB 18 227.5 16 348.9 16 350.4 16 085.9 16 357.3 16 083.7 16 070.6 16 031.1 16 036.2
HEL2 20 643.2 18 710.4 18 707.4 18 433.9 18 718.8 18 430.2 18 418.6 18 382.7 18 389.9
PLPK 22 234.6 20 160.7 20 134.3 19 767.6 20 169.1 19 789.9 19 752.2 19 727.2 19 734.7
DANE 19 310.0 17 212.4 17 184.4 16 870.3 17 220.7 16 867.5 16 858.2 16 856.9 16 864.4
ASKY 15 804.5 14 117.8 14 124.9 13 904.9 14 126.1 13 895.3 13 891.6 13 878.7 13 879.3
HRDG 13 290.0 11 855.8 11 861.2 11 650.6 11 864.1 11 640.9 11 638.9 11 629.7 11 637.0
NRSK 17 765.6 15 764.1 15 734.1 15 368.0 15 772.5 15 385.5 15 356.0 15 354.1 15 361.6
SRMP 17 316.5 15 700.0 15 707.6 15 440.7 15 708.4 15 441.9 15 428.1 15 410.6 15 418.9
GMMA 18 004.4 15 961.8 15 926.0 15 528.3 15 970.2 15 558.0 15 514.1 15 504.5 15 512.1
BLAS 16 137.0 14 297.6 14 295.6 13 977.1 14 306.0 13 988.5 13 964.0 13 947.7 13 956.0
TIMM 22 864.1 21 084.0 21 085.2 20 750.7 21 092.4 20 765.6 20 734.9 20 685.9 20 693.5
JWLF 14 909.7 13 196.6 13 204.5 12 985.6 13 205.0 12 973.5 12 973.3 12 961.9 12 970.0
JGBL 14 477.9 12 789.9 12 798.1 12 595.6 12 798.3 12 580.2 12 584.6 12 577.5 12 585.7
KUAQ 18 668.7 16 816.8 16 814.8 16 509.5 16 825.2 16 513.0 16 496.5 16 481.0 16 488.6
KBUG 22 219.6 20 537.9 20 545.1 20 239.1 20 546.3 20 250.6 20 224.3 20 184.8 20 192.9
GROK 16 164.9 14 342.5 14 341.6 14 025.0 14 350.9 14 031.8 14 012.6 14 001.6 14 009.9
RINK 14 194.9 12 448.4 12 448.6 12 179.7 12 456.8 12 173.5 12 168.8 12 172.9 12 181.0
MSVG 19 566.6 17 328.8 17 254.3 16 832.9 17 337.2 16 863.2 16 823.1 16 831.0 16 839.2
MIK2 20 389.7 18 327.6 18 301.5 17 953.1 18 335.9 17 967.1 17 938.9 17 918.3 17 926.2
YMER 14 846.4 13 466.6 13 471.1 13 249.4 13 475.0 13 238.6 13 237.7 13 222.4 13 229.9
HMBG 13 445.7 12 150.7 12 152.8 11 957.0 12 159.1 11 940.6 11 947.8 11 948.4 11 956.7
LYNS 23 148.5 21 195.6 21 189.5 20 873.6 21 204.0 20 884.5 20 858.2 20 813.9 20 821.8
KMJP 15 438.1 13 710.9 13 719.0 13 516.9 13 719.3 13 501.7 13 506.2 13 505.7 13 509.2
THU2 16 211.6 14 221.6 14 203.7 13 962.9 14 230.0 13 950.9 13 948.5 13 926.3 13 934.6
HJOR 21 623.1 19 835.1 19 836.1 19 515.8 19 843.5 19 525.9 19 501.2 19 462.3 19 470.6
QAQ1 22 440.2 20 996.5 21 004.7 20 691.0 21 004.9 20 699.1 20 678.6 20 666.1 20 674.4
DGJG 14 858.8 13 302.2 13 310.2 13 045.3 13 310.5 13 036.0 13 034.6 13 035.3 13 043.7
LEFN 14 968.8 13 375.6 13 383.9 13 145.3 13 384.0 13 136.7 13 132.5 13 110.2 13 117.1
测站 AR1WN AR2WN AR3WN AR4WN ARMA1WN ARMA2WN ARMA3WN ARMA4WN
VFDG 14 077.8 14 059.1 14 045.9 14 051.6 14 051.2 14 041.6 14 049.7 14 058.1
KAGA 16 572.2 16 551.4 16 542.1 16 550.4 16 544.2 16 542.1 16 550.2 16 557.8
MARG 11 767.5 11 762.8 11 753.7 11 762.1 11 759.7 11 757.7 11 761.6 11 764.6
KMOR 12 265.7 12 260.7 12 225.3 12 233.3 12 255.6 12 246.2 12 225.5 12 242.0
WTHG 14 498.4 14 501.0 14 503.0 14 510.6 14 500.2 14 502.2 14 510.5 14 518.9
SCOR 18 788.8 18 765.8 18 766.7 18 775.0 18 762.3 18 767.8 18 771.6 18 783.1
LBIB 12 755.0 12 758.9 12 749.4 12 757.6 12 757.6 12 751.5 12 757.5 12 766.0
KSNB 16 092.1 16 079.0 16 036.3 16 044.5 16 069.7 16 057.2 16 044.3 16 051.1
HEL2 18 438.6 18 427.0 18 390.0 18 398.3 18 418.8 18 405.2 18 398.3 18 406.7
PLPK 19 798.3 19 760.6 19 734.6 19 742.9 19 747.7 19 745.3 19 742.9 19 751.3
DANE 16 875.9 16 866.6 16 865.3 16 872.8 16 863.7 16 864.6 16 872.8 16 881.1
ASKY 13 903.6 13 900.0 13 878.3 13 884.4 13 896.8 13 895.3 13 873.9 13 882.2
HRDG 11 649.2 11 647.3 11 637.3 11 645.2 11 644.4 11 641.4 11 626.0 11 632.6
NRSK 15 393.9 15 364.4 15 362.5 15 370.0 15 358.6 15 360.5 15 368.8 15 376.8
SRMP 15 450.3 15 436.5 15 419.0 15 427.3 15 429.6 15 420.0 15 426.6 15 435.7
GMMA 15 566.4 15 522.5 15 512.9 15 520.5 15 512.2 15 513.2 15 520.6 15 528.9
BLAS 13 996.9 13 972.4 13 956.1 13 964.4 13 963.5 13 958.4 13 964.3 13 970.1
TIMM 20 774.0 20 743.3 20 694.3 20 701.8 20 723.8 20 698.8 20 701.6 20 701.9
JWLF 12 981.9 12 981.7 12 970.1 12 978.4 12 979.3 12 973.5 12 961.6 12 981.8
JGBL 12 588.5 12 593.0 12 585.8 12 594.1 12 592.0 12 585.7 12 593.4 12 593.2
KUAQ 16 521.4 16 504.9 16 489.4 16 497.0 16 497.8 16 488.3 16 495.6 16 503.9
KBUG 20 259.0 20 232.7 20 193.2 20 201.3 20 217.5 20 200.1 20 200.8 20 207.9
GROK 14 040.2 14 021.0 14 009.9 14 018.3 14 014.5 14 013.5 14 018.2 14 026.7
RINK 12 181.9 12 177.1 12 180.9 12 188.7 12 175.8 12 180.3 12 188.6 12 188.9
MSVG 16 871.6 16 831.5 16 839.2 16 847.6 16 831.3 16 839.4 16 847.6 16 856.0
MIK2 17 975.5 17 947.3 17 926.7 17 934.6 17 936.7 17 928.4 17 934.2 17 942.4
YMER 13 247.0 13 246.1 13 229.6 13 237.1 13 243.0 13 240.5 13 236.2 13 243.7
HMBG 11 948.9 11 956.1 11 956.7 11 965.1 11 955.9 11 957.7 11 964.9 11 973.3
LYNS 20 892.9 20 866.5 20 822.2 20 830.2 20 851.4 20 834.2 20 829.9 20 837.1
KMJP 13 510.0 13 514.5 13 508.0 13 516.3 13 513.9 13 516.9 13 496.1 13 504.5
THU2 13 959.2 13 956.8 13 934.6 13 943.0 13 953.0 13 941.8 13 943.0 13 951.2
HJOR 19 534.3 19 509.6 19 470.6 19 479.0 19 495.5 19 478.8 19 479.0 19 487.2
QAQ1 20 707.5 20 687.0 20 674.4 20 682.7 20 679.3 20 678.6 20 682.7 20 691.1
DGJG 13 044.4 13 043.0 13 043.7 13 052.1 13 041.4 13 044.1 13 050.3 13 060.5
LEFN 13 145.1 13 140.8 13 118.5 13 125.5 13135.9 13 122.0 13 124.7 13 133.4
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格陵兰地区GNSS反演PWV分析
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张胜凯 1, 2 , 马杰 1, 2, 3 , 龚力 1, 2 , 雷锦韬 4 , 李文浩 5 , 肖峰 1, 2, *
大地测量与地球动力学 | 现代大地测量 2025,45(8): 771-780
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大地测量与地球动力学 |现代大地测量 2025 , 45 (8) : 771 -780
格陵兰地区GNSS反演PWV分析
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张胜凯1, 2 , 马杰1, 2, 3, 龚力1, 2, 雷锦韬4, 李文浩5, 肖峰1, 2, *
作者信息
  • 1 武汉大学中国南极测绘研究中心,武汉市珞喻路129号,430079
  • 2 武汉大学极地环境监测与公共治理教育部重点实验室,武汉市珞喻路129号,430079
  • 3 武汉大学测绘学院,武汉市珞喻路129号,430079
  • 4 广西大学土木建筑工程学院,南宁市大学东路100号,530004
  • 5 南京工业大学测绘科学与技术学院,南京市浦珠南路30号,211816
通讯作者:

肖峰,副研究员,主要研究方向为极地冰盖和海冰动态变化监测,E-mail:

XIAO Feng, associate researcher, majors in polar ice sheet and sea ice dynamic monitoring, E-mail:

作者简介:

张胜凯,教授,博士生导师,主要研究方向为极地大地测量学与地球物理学,E-mail:

GNSS-Retrieved PWV in the Greenland Region
Shengkai ZHANG1, 2 , Jie MA1, 2, 3, Li GONG1, 2, Jintao LEI4, Wenhao LI5, Feng XIAO1, 2, *
Affiliations
  • 1 Chinese Antarctic Center of Surveying and Mapping, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
  • 2 Key Laboratory of Polar Environment Monitoring and Public Governance, Ministry of Education, Whuhan University, 129 Luoyu Road, Wuhan 430079, China
  • 3 School of Geodesy and Geomatics, Wuhan Universuty, 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
  • ZHANG Shengkai, professor, PhD supervisor, majors in polar geodesy and geophysics, E-mail:

出版时间: 2025-08-15 doi: 10.14075/j.jgg.2024.12.552
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以2010—2021年格陵兰地区的35个GNSS测站的小时分辨率的天顶对流层延迟(ZTD)数据为基础,结合ERA5再分析资料和Tm模型,对该区域的大气可降水量(PWV)进行求解。随后,对其周年、半周年、周日、半周日等主要季节和日变化特征进行研究。结果表明,格陵兰地区测站存在一定程度的自相关性,且夏季自相关程度弱于其他季节。周年项振幅范围为2~7 mm,半周年项振幅范围为0~3 mm,其相位在年际间变化较小,最高值出现在夏季,最低值出现在冬季。仅有少数几个测站周日项比较明显,振幅在0.08 mm左右。去除时间序列中的周年、半周年、周日项得到残差时间序列,最大的RMSE范围为1.30~3.65 mm,平均RMSE为2.08 mm,其中,北部地区RMSE偏大,南部地区较小。

格陵兰  /  大气可降水量  /  插值  /  GNSS  /  Tm  /  天顶对流层延迟

Based on hourly-resolution zenith tropospheric delay(ZTD) data from 35 GNSS stations in the Greenland region spanning the period from 2010 to 2021, combined with ERA5 reanalysis datasets and Tm model, precipitable water vapor(PWV) over the region was retrieved. Subsequently, the principal seasonal and diurnal variation characteristics, including annual, semi-annual, diurnal, and semi-diurnal cycles, were investigated. The results indicate that the GNSS stations in Greenland exhibit a certain degree of autocorrelation, with the summer season showing weaker autocorrelation compared to other seasons. The amplitude of the annual component ranges from 2 to 7 mm, while that of the semi-annual component ranges from 0 to 3 mm, with relatively minor interannual variations in their phases. The maximum values occur in summer, and the minimum values in winter. The diurnal component is only pronounced at a few stations, with amplitudes around 0.08 mm. After removing the annual, semi-annual, and diurnal components from the time series to obtain the residual time series, the maximum RMSE ranges from 1.30 to 3.65 mm, with an average RMSE of 2.08 mm. Notably, the RMSE values are relatively higher in the northern region and lower in the southern region.

Greenland  /  precipitable water vapor  /  interpolation  /  GNSS  /  Tm  /  zenith tropospheric delay
张胜凯, 马杰, 龚力, 雷锦韬, 李文浩, 肖峰. 格陵兰地区GNSS反演PWV分析. 大地测量与地球动力学, 2025 , 45 (8) : 771 -780 . DOI: 10.14075/j.jgg.2024.12.552
Shengkai ZHANG, Jie MA, Li GONG, Jintao LEI, Wenhao LI, Feng XIAO. GNSS-Retrieved PWV in the Greenland Region[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (8) : 771 -780 . DOI: 10.14075/j.jgg.2024.12.552
近年来,温室效应加剧推动全球变暖,两极冰盖加速消融。尽管格陵兰冰储量低于南极,但其质量损失近年已超后者[1]。降水作为冰盖物质平衡的关键输入,使格陵兰气候与全球气候及海平面变化紧密关联[2]。受限于极地恶劣的环境,长期实地观测难以开展,GNSS数据在气候研究中的价值愈发凸显。
自20世纪70年代起,GNSS气象学逐步发展: 早期研究建立了对流层延迟模型[3]与加权平均温度(Tm)[4]理论框架,提出PWV与天顶湿延迟(ZWD)的转换关系[5]; 后续研究验证了GNSS反演PWV的可行性[6-11]。随着全球GNSS长期观测站网络的扩展,部分站点已积累超过20 a的观测数据,为探究PWV长期变化规律提供了宝贵资源。此外,基于气象与地面数据的再分析模型进一步丰富了全球气候研究的数据基础[12-14]。
目前,GNSS反演PWV已成为分析全球水汽变化的关键数据源[15],并在极地气候研究中取得显著成果[16-19]。格陵兰地区丰富的GNSS观测数据蕴含了重要的气候信息,本文将围绕该区域GNSS反演PWV展开分析,揭示其气候响应特征。
选取NGL(Nevada Geodetic Laboratory)提供的35个测站(图 1),测站分布于格陵兰岛沿海地区,其中东侧密度大于西侧。由于测站的观测时间并不连续,而在对GNSS位置序列的后续分析处理中,主成分分析(PCA)要求数据无缺失,所以使用随机森林插值算法对ZTD缺失值进行插值,其中缺失值较多的几个站的插值结果见图 2。插值后,时间序列在2010-01-01—2022-01-01之间均有数据。
ERA5是欧洲中期天气预报中心(ECMWF)的最新一代的再分析产品[20],相较于老一代的ERA-Interim产品,更新了采用的模型,包含更多的数据来源,能提供更高的空间分辨率(0.25°)和时间分辨率(1 h)。本文采用ERA5提供的地面气压数据来计算天顶静力学延迟(ZHD)。
GPT3模型[12]是一种常用来计算对流层延迟、Tm等的格网经验模型,表达式如下:
$\begin{gathered}T_m=A_0+ \\A_1 \cos \left(\frac{\text { doy }}{365.25} 2 \mathsf{π} \right)+B_1 \sin \left(\frac{\text { doy }}{365.25} 2 \mathsf{π} \right)+ \\A_2 \cos \left(\frac{\text { doy }}{365.25} 2 \mathsf{π} \right)+B_2 \sin \left(\frac{\text { doy }}{365.25} 2 \mathsf{π} \right)\end{gathered}$
式中,Tm为所采用的DEM的表面的加权平均温度; doy为年积日; A0为年平均振幅; A1、A2、B1、B2为模型系数。对GNSS数据进行解算时,先解算ZTD,再根据Saastamoinen模型计算ZHD,将其从ZTD中减去,得到ZWD。
$\left\{\begin{aligned}\mathrm{ZHD} & =\frac{2.2768 P_S}{1-0.002~6 \cos (2 \varphi)-0.000~28 h} \\\mathrm{ZWD} & =\mathrm{ZTD}-\mathrm{ZHD}\end{aligned}\right.$
式中,PS为地面测站气压,单位hPa; h为测站大地高,单位km; φ为测站地理纬度。
PWV由ZWD与PWV转换系数Π计算得到:
$\left\{\begin{array}{l}\mathrm{PWV}=\varPi * \mathrm{ZWD} \\\varPi=\frac{10^8}{\rho_v R_v\left[\left(k_3 / T_m\right)+k_2^{\prime}\right]}\end{array}\right.$
式中,ρv为液态水密度; Rv=461.495 J/(kg ·K)为水汽气体常数; k′2、k3为大气物理参数,取22.13±2.20 K/hPa、(3.739±0.012)×105 K/hPa。
PCA经常用于减少数据集的维数,同时可以保留数据集中对方差贡献最大的特征,这样低维主成分往往能够保留数据的最重要部分。GPT3模型可以分为线性部分和周期部分:
$T(t)=\operatorname{linear}(t)+\operatorname{period}(t)$
对周期项进行PCA分解,PCA的Tm公式转换为:
$T(t)=\operatorname{linear}(t)+\sum\limits_{i=1}^k T_k^{\prime} * V_{k t}^{\prime}$
式中,T′为空间响应; V′为时间分量。考虑到崎岖地形的高程变化对Tm的影响比较大,因此在模型中引入高程变化率:
$\left\{\begin{array}{l}\beta(\text { lon }, \text { lat }, t)=\sum\limits_{i=1}^k B(\text { lon }, \text { lat })_k^{\prime} * V b_{k t}^{\prime} \\T(\text { lon }, \text { lat }, t, h)=\text { linear }(t)+\sum\limits_{i=1}^k T(\text { lon, lat })_k^{\prime} * \\V(t)_k^{\prime}+h(\text { lon, lat }) * \beta(\text { lon, lat }, t)\end{array}\right.$
式中,lon和lat分别为经度和纬度,单位(°); β为Tm的高程变化率; h为大地高。通过主成分进行逆变换可以得到可能包含更多信息的Tm时间序列,比仅保留周期项信息的方法更具优势。
GNSS时间序列普遍存在一定的缺失,而PCA要求无缺失数据。本文利用GNSS测站间的空间相关性来插值,采用随机森林算法,通过多个决策树进行集成学习提高模型的拟合能力,进一步提升了GNSS时间序列的插值效果。具体到GNSS时间序列插值中来看,其中的数据集就是各个站点的GNSS时间序列的观测矩阵X,p为观测历元数,n为测站数(图 3)。其中,黄色、蓝色、绿色为观测值或之前的插值,红色为待插值的观测数据。可以使用随机森林学习黄色到蓝色的关系,然后使用绿色来得到红色的缺失值。具体的方法为,首先对于黄色和蓝色的训练集合,通过不纯度划分为子集,在本文中,使用有观测值的测站的观测值进行划分。若选择si测站作为划分站,以其观测值值域中的一个值xs作为划分值,则可以划分为集合P1(测站si的观测值xs, p<xs)和P2(测站si的观测值xs, p>xs),划分后的不纯度的加权和G(si, xj)可用式(7)表示:
$\left\{\begin{array}{l}G_P=\sum\limits_{i \in P}\left(y_i-\bar{y}\right)^2 \\G\left(s_i, x_j\right)=\frac{n_1}{n} G_{P_1}+\frac{n_2}{n} G_{P_2} \\\arg \min _{s_i, x_j} G\left(s_i, x_j\right)\end{array}\right.$
式中,n1和n2为划分后左右节点观测数; n为划分前观测总数。在每次划分中需要寻找最佳的测站和观测值使函数G最小,然后不断迭代划分,直到左右节点的个数小于阈值。
首先使用随机森林插值算法对ZTD时间序列进行插值,采用ERA5再分析数据和Saastamoinen模型计算ZHD,然后可以得到ZWD数据(图 4(a)),ZWD的范围多在0.1 m以下,使用PCA-Tm模型计算最终的PWV数据(图 4)。限于篇幅,仅展示7个缺失值最多的站的PWV数据。对比ZWD(图 4(a))和PWV(图 4(b))发现,ZWD相较于PWV数据更加不规律。PWV变化范围为0~25 mm,呈现出明显的年周期规律,不同站之间在最大值上略有差异。
为分析各测站在空间上是否具有一定的相关性,采用莫兰指数探究变量分布是否和空间相关。零假设为变量在空间分布随机,计算公式为:
$\left\{\begin{array}{l}I=\frac{N}{W} \frac{\sum\limits_i \sum\limits_j w_{i j}\left(x_i-\bar{x}\right)\left(x_j-\bar{x}\right)}{\sum\limits_i\left(x_i-\bar{x}\right)^2} \\W=\sum\limits_i \sum\limits_j w_{i j}\end{array}\right.$
式中,N为测站的数量; xi和xj分别为第N个测站的观测值; ${\bar x}$为所有测站观测值的平均值; wij为空间权矩阵中的因素,表示第i个空间单元和第j个空间单元之间的空间关系,若i和j相邻,则wij=1,若i和j不相邻,则wij=0。
使用距离衰减函数来分配权重。莫兰指数的值在[-1, 1]之间,大于0表示正相关,越趋近于1,空间相关性越明显; 小于0表示负相关,越趋近于-1,空间差异越大,越趋近于随机分布。
计算格陵兰地区35个测站2010—2021年的莫兰指数的年际变化和月变化,见图 5。总体而言,莫兰指数约在0.14±0.02之间,说明格陵兰岛的测站之间存在着一定的自相关,其中,2011年和2015年出现2个局部极小值。
在月变化结果中,其季节变化明显,其中6—8月莫兰指数最低,空间相关性最差,这可能是因为夏季气候变化较大,水汽较为丰富,由于夏季气温高,对流层对流和扰乱程度较大,导致测站空间自相关性较其他季节低。
时间序列估计的平稳性是其他时间序列处理方法的前提。若时间序列的均值和方差随时间变化是常数,且其不同滞后之间的协方差是一定的,则认为该时间序列估计是平稳的。检测时间序列平稳性的主要方法之一是单位根法,常用ADF(augmented Dickey-Fuller)检验法。假设待检测时间序列为自回归模型:
$\begin{gathered}\Delta X_t=\alpha+\beta t+ \\\gamma X_t \varphi_1 \Delta X_{t-1}+\cdots+\varphi_{q-1} \Delta X_{t-q+1} \varepsilon_t\end{gathered}$
式中,α、β为时间序列趋势的系数; q为自回归的滞后阶数。若α=0、β=0,则为随机游走; 若α≠0、β=0,则是带偏移的随机游走。因为引入了滞后阶数q,所以在确定q时需要使用AIC(Akaike information criterion)或BIC(Bayesian information criterion)信息准则进行q的判断。ADF的零假设是有一个单位根,另一个假设是没有单位根。如果q值高于临界值,那么不能拒绝存在单位根,即不平稳; 如果低于临界值,则拒绝存在单位根的假设,即时间序列平稳。其统计量DFτ为:
$\mathrm{DF}_\tau=\frac{\hat{\gamma}}{\mathrm{SE}(\hat{\gamma})}$
式中,$\hat{\gamma}$表示回归模型中单位根参数γ的估计值; SE($\hat{\gamma}$)表示$\hat{\gamma}$的标准差。
使用ADF法对格陵兰地区35个测站的PWV时间序列进行平稳性检验。在显著性水平α=0.05下,所有测站的q值均小于α(表 1),拒绝存在单位根的原假设,表明所有测站的PWV时间序列平稳。
对PWV进行趋势检验时使用季节性Mann-Kendall(MK)检验法,相比于传统MK检验通过计算统计量S来量化单调趋势,季节性MK检验在此基础上增加了对数据周期波动的考量。此方法将完整的时间序列按周期分段,分别计算各个季节段的趋势贡献,然后整合各分段统计量得出整体趋势判断,从而有效消除季节性波动对趋势分析的干扰。该方法作为一种非参数统计方法,适用于所有分布,即数据不需要满足正态假设,但数据不应具有序列相关性,否则可能会导致显著性检验结果q值产生偏差。MK的统计量S为:
$S_k=\sum\limits_{k=1}^{n-1} \sum\limits_{j=k+1}^n \operatorname{sign}\left(X_j-X_k\right)$
$\operatorname{sign}\left(X_j-X_k\right)=\left\{\begin{array}{l}-1,\left(X_j-X_k\right)<0 \\0,\left(X_j-X_k\right)=0 \\1,\left(X_j-X_k\right)>0\end{array}\right.$
季节性MK的统计量为:
$Z_{s k}=\left\{\begin{array}{l}\frac{S_k-1}{\sigma_{s k}}, S_k>0 \\0, S_k=0 \\\frac{S_k+1}{\sigma_{s k}}, S_k<0\end{array}\right.$
式中,$\sigma_{s k}=\sqrt{\sum\limits_{i=1}^m\left(n_i / 18\right)\left(n_i-1\right)\left(2 n_i+5\right)}$, 其中ni为第i周期中观测数; Xj和Xk是PWV观测值。
使用显著性水平α=0.05进行趋势检验,若q≥α则无显著趋势; 若q<α则存在显著趋势。Z>0代表趋势为正; Z<0代表趋势为负。计算结果见表 2。可以看出,大部分测站没有趋势,仅有15个站有一定趋势,且其中只有1个为上升趋势。表明绝大多数测站的可降水量存在一定的减少趋势。
所有测站PWV趋势分布结果见图 6(a),其中黑色是没有趋势的站点。可以看出,PWV呈现南部降低、北部上升的趋势,这和Chen等[15]计算的2000—2014年趋势一致。表明在2014年之后,北冰洋区域PWV还存在一定的上升趋势,而大西洋区域和格陵兰南部PWV依然存在减小趋势。
图 6(b)为上PWV上升和下降趋势最大的2个站的时间序列,其中KMOR为北部靠近北冰洋的测站,HEL2为南部靠近北大西洋的测站。可以看出,HEL2站波动稍大,有略微下降趋势,相较于整体趋势而言较小; KMOR站波动较小,上升趋势较小。KMOR站冬季和夏季的PWV小于HEL2站; 2个测站夏季差距较小,冬季差距变大。
为分析ZTD和PWV之间的差异,分别对两者的时间序列进行频谱分析(图 7)。可以看出,两者均有较为明显的年周期,ZTD的半年周期不够明显,PWV中有一个明显的半年周期,说明其水汽变化还有一定的半周年项,但水汽变化对ZTD的影响不是特别大,可能和水汽半年周期项的幅度较小有关,需要进一步分析。
对每年的ZTD和PWV进行最小二乘拟合,拟合其周年项和半周年项,并对系数进行绘图,见图 7(c)和7(d)。可以看出,PWV周年项振幅范围为2~7 mm,半周年项振幅范围为0~3 mm。ZTD时间序列的相位变化在年际间较大,振幅也在年际间变化,其半年周期项相较于年周期项振幅较小,因此可能在频谱图上观测不到。PWV序列年际间差距较小,相位偏移较小,其各测站之间的振幅差距较大,半年周期项和年周期项之间振幅相对差距较小,因此能在频谱图上观察到。
对每个站的PWV按月进行平均,取其中7个站,见图 8。可以看出,大部分测站的PWV振幅变化都是夏季高、冬季低,PWV振幅最大值出现在6—8月,最小值出现在1—3月,这是由于夏季的太阳热辐射大,水汽蒸发较多,导致空气中的水汽含量较大。但各测站PWV均值的变化范围有所不同,其中,GMMA站冬季振幅接近0 mm,其他测站均在5 mm附近。
由于使用的ZTD时间序列的时间分辨率为1 h,因此还可以进一步对其进行日变化特征的分析。首先使用高通滤波剔除时间序列中的一些低频信号,仅保留周期为1.2 d及以下的高频信号,然后对其进行频谱分析,见图 9(a)。通过过滤周年项、半周年项和其他低频信号,可以发现明显的周日变化。使用最小二乘拟合所有测站PWV周日和半周日项,见图 9(b)。可见,并不是所有的测站都有很强的周日项信号,因此,图 9(a)中的周日项信号可能是少数几个测站的叠加。
从原始的PWV时间序列中去除提取出的周期项和趋势项,得到各个测站的残差项,见图 10(a)。计算残差项的RMSE可得,RMSE最大的测站为QAQ1(3.65 mm),最小的测站为HRDG(1.30 mm),平均为2.08 mm。从空间分布来看,南部靠近北大西洋部分测站的RMSE较北部测站的RMSE明显偏大,这是由于格陵兰地区南部气候情况和温度变化比北部更复杂(南部温度较高,造成水汽蒸发、凝结等)。从东部沿海测站来看,近海测站较远海测站的RMSE更大,这可能是受海风等海陆作用的影响。结合图 10(b)可知,沿海测站PWV较远海测站偏大,这可能是由于海面蒸发的水汽,经过海风扩散导致的(较大的水汽总量会导致更大的变化)。
使用Hector软件,采用不同的噪声模型对测站的时间序列进行分析(表 3)。由于Hector软件建议自回归噪声模型的自回归阶数应小于5,所以采用自回归模型AR(1~4)、自回归模型AR(1~4)+白噪声、自回归滑动平均ARMA(1~4)+白噪声。
使用BIC对模型进行比较,见表 3。其中29个站的最优模型为AR3,剩余6个测站中WTHG、HMBG的最优模型为AR1,SCOR、RINK、MSVG、DGJG的最优模型为AR2。说明在格陵兰地区,3阶自回归模型为最优噪声模型。但也会有极少数测站存在一定的白噪声,上述6个测站中,RINK站分布在格陵兰西部,其余5个测站分布在格陵兰东北部,北部地区的白噪声成分相较于其他区域偏大。
使用日平均PWV计算2010—2021年每年各站点的累积PWV,见图 11。可以看出,所有测站年累积PWV范围为468.90~3 629.48 mm,其中最小的测站一直为KSNB,最大的测站为KAGA(2010—2013年、2017年、2020年)、PLPK(2014—2016年、2018年、2019年)、QAQ1(2021年)。还可以发现,纬度越低,年累积PWV越高,但极小值却出现在研究区中东部地区,测站年际之间变化较小。沿海站的年累积PWV大于内陆站,这是因为沿海地区受海洋海风的影响和内陆地区海拔较高。
1) 格陵兰地区测站存在一定程度的自相关性,且夏季的自相关程度要弱于其他季节。格陵兰地区所有测站的时间序列均是平稳的,其中有15个测站存在着一定的趋势,仅有1个北部地区测站存在上升趋势,其余14个测站均存在下降趋势,但下降趋势均偏小,小于0.05 mm/a。
2) 格陵兰地区PWV时间序列主要周期项为周年、半周年和周日项,周年项振幅范围为2~7 mm,半周年项振幅范围为0~3 mm,其相位在年际间变化较小,最高值出现在夏季,最低值出现在冬季。仅有几个测站周日项比较明显,振幅在0.08 mm左右。
3) 去除掉PWV时间序列中的周年、半周年、周日项得到残差时间序列,其中最大的RMSE范围为1.30~3.65 mm,平均RMSE为2.08 mm,且北部地区RMSE偏大,南部地区较小。对残差序列进行噪声成分分析发现,大部分测站最优噪声模型为AR3,仅有6个测站为AR(1, 2)模型,且多分布在北部地区。
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2025年第45卷第8期
26
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doi: 10.14075/j.jgg.2024.12.552
  • 接收时间:2024-12-04
  • 首发时间:2026-10-29
  • 出版时间:2025-08-15
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  • 收稿日期:2024-12-04
基金
National Key Research and Development Program of China(2023YFC2809103)
国家重点研发计划(2023YFC2809103)
National Key Research and Development Program of China(2024YFC2813505)
国家重点研发计划(2024YFC2813505)
Fundamental Research Funds for the Central Universities(042023gf0012)
中央高校基本科研业务费专项(042023gf0012)
Fundamental Research Funds for the Central Universities(2042022dx0001)
中央高校基本科研业务费专项(2042022dx0001)
作者信息
    1 武汉大学中国南极测绘研究中心,武汉市珞喻路129号,430079
    2 武汉大学极地环境监测与公共治理教育部重点实验室,武汉市珞喻路129号,430079
    3 武汉大学测绘学院,武汉市珞喻路129号,430079
    4 广西大学土木建筑工程学院,南宁市大学东路100号,530004
    5 南京工业大学测绘科学与技术学院,南京市浦珠南路30号,211816

通讯作者:

肖峰,副研究员,主要研究方向为极地冰盖和海冰动态变化监测,E-mail:

XIAO Feng, associate researcher, majors in polar ice sheet and sea ice dynamic monitoring, E-mail:

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2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
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