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Article(id=1281933693103091811, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1281909275651969257, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2025.08.295, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1755964800000, receivedDateStr=2025-08-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1783566409611, onlineDateStr=2026-07-09, pubDate=1781452800000, pubDateStr=2026-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783566409611, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783566409611, creator=13701087609, updateTime=1783566409611, updator=13701087609, issue=Issue{id=1281909275651969257, tenantId=1146029695717560320, journalId=1281212831689347082, year='2026', volume='46', issue='6', pageStart='662', pageEnd='789', issueExtLink='null', onlineDate='null', pubDate='1781452800000', pubDateStr='2026-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1783560588038, creator='13701087609', updateTime=1783566454347, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1281933881221812905, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1281909275651969257, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1281933881221812906, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1281909275651969257, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=702, endPage=709, ext={EN=ArticleExt(id=1281933693690294372, articleId=1281933693103091811, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Estimation of PWV and Analysis of Extreme Rainfall in Guangxi Region Based on XGBoost Model, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the limitation of real/near-real-time GNSS PWV retrieval under missing meteorological parameters, three PWV estimation models without the need for measured meteorological parameters were established in the Guangxi region based on the XGBoost model. First, the XGBZ-PWV model was developed with inputs including station time (DOY and HOD), location (Longitude, Latitude, and Height), and GNSS ZTD, and the output feature being GNSS PWV. Then, based on the XGBZ-PWV model, two empirical PWV values were incorporated to establish the XGBZG-PWV and XGBZE-PWV models, respectively. For comparison, the GPT3 model was used to provide pressure and temperature for PWV retrieval based on GNSS ZTD (GPT3-PWV model). The accuracy of the established models was validated using GNSS PWV retrieved from GNSS ZTD, ERA5 surface pressure, and temperature in the Guangxi region in 2022 as the reference value. The results show that, compared to the GPT3-PWV model, the estimation accuracy of the XGBZ-PWV, XGBZG-PWV, and XGBZE-PWV models improved by 22.98%, 29.03%, and 31.45%, respectively, with the XGBZE-PWV model performing the best. During two extreme rainfall events in 2022, the spatiotemporal evolution characteristics of PWV and rainfall were analyzed. The results demonstrate that the XGBZE-PWV model maintains good applicability even under extreme weather conditions.

, authors=Liwei YAN1, Maijin LIN2, Shaofeng XIE1, *, Liangke HUANG1, Xianghong LI3, Qiongyu FANG3, authorsList=Liwei YAN, Maijin LIN, Shaofeng XIE, Liangke HUANG, Xianghong LI, Qiongyu FANG, authorCompany=null, correspAuthors=Shaofeng XIE, 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=1281933697922347121, articleId=1281933693103091811, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=基于XGBoost模型的广西区域PWV估算及极端降雨分析, columnId=1281909276545356011, journalTitle=大地测量与地球动力学, columnName=大地测量学, runingTitle=null, highlight=null, articleAbstract=

针对气象参数缺失情况下实时/近实时GNSS PWV反演受限的问题, 基于XGBoost模型分别建立广西区域3种无需实测气象参数的PWV估算模型。首先建立输入为站点时间(DOY和HOD)、位置(Longitude、Latitude和Height)和GNSS ZTD, 输出特征为GNSS PWV的XGBZ-PWV模型, 然后在XGBZ-PWV模型的基础上分别加入2种经验PWV, 建立XGBZG-PWV模型和XGBZE-PWV模型。同时, 基于GNSS ZTD, 利用GPT3模型提供的气压与温度反演PWV(GPT3-PWV模型), 对所建PWV模型进行对比, 并以2022年广西区域基于GNSS ZTD、ERA5地表气压和温度反演的GNSS PWV作为参考值对所建立的模型进行精度验证。结果表明, 相比于GPT3-PWV模型, XGBZ-PWV、XGBZG-PWV和XGBZE-PWV模型的估算精度分别提升22.98%、29.03%和31.45%, 其中, XGBZE-PWV模型最优。在2022年2次极端降雨过程中, 对PWV与降雨的时空演变特征进行分析, 结果表明, XGBZE-PWV模型在极端天气条件下依然具有较好的适用性。

, authors=闫立伟1, 林买金2, 谢劭峰1, *, 黄良珂1, 李向红3, 方琼玉3, authorsList=闫立伟, 林买金, 谢劭峰, 黄良珂, 李向红, 方琼玉, authorCompany=null, correspAuthors=谢劭峰, authorNote=

闫立伟, 主要研究方向为GNSS大气水汽反演, E-mail:

, correspAuthorsNote=
谢劭峰, 教授, 主要研究方向为GNSS大气水汽监测, 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=ZOrAcMnx+mhSu4tYwTWDSg==, magXml=aifaulPpm01GOUp6yBY40A==, pdfUrl=null, pdf=eMbU2YmQTBE+vzyc4UY6OA==, pdfFileSize=3271388, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=pfEh0Z5Y1nDWy7bcZOP7vQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Hp5U58XgdOzs4/RN7L5z1g==, mapNumber=null, fund=null)}, authors=[Author(id=1281933698404692092, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1281933693103091811, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=13347633621@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281933698484383870, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1281933693103091811, authorId=1281933698404692092, language=EN, stringName=Liwei YAN, firstName=Liwei, middleName=null, lastName=YAN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1 College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281933698580852863, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1281933693103091811, authorId=1281933698404692092, language=CN, stringName=闫立伟, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1 桂林理工大学测绘地理信息学院, 桂林, 541006, bio={"content":"

闫立伟, 主要研究方向为GNSS大气水汽反演, E-mail:

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闫立伟, 主要研究方向为GNSS大气水汽反演, E-mail:

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Detailed information on the three PWV estimation models for Guangxi

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模型 模型输入 模型输出
XGBZ-PWV 时间信息(DOY和HOD)、位置信息(Longitude、Latitude和Height)和GNSS ZTD GNSS PWV
XGBZG-PWV 时间信息(DOY和HOD)、位置信息(Longitude、Latitude和Height)和GNSS ZTD，GAskne_PWV
XGBZE-PWV 时间信息(DOY和HOD)、位置信息(Longitude、Latitude和Height)和GNSS ZTD，GXE_PWV
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广西区域3个PWV估算模型详细信息

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模型 模型输入 模型输出
XGBZ-PWV 时间信息(DOY和HOD)、位置信息(Longitude、Latitude和Height)和GNSS ZTD GNSS PWV
XGBZG-PWV 时间信息(DOY和HOD)、位置信息(Longitude、Latitude和Height)和GNSS ZTD，GAskne_PWV
XGBZE-PWV 时间信息(DOY和HOD)、位置信息(Longitude、Latitude和Height)和GNSS ZTD，GXE_PWV
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The bias and RMSE statistics of the four models for estimated PWV

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模型bias/mm RMSE/mm
最小值 最大值 平均值 最小值 最大值 平均值
GPT3-PWV -1.57 1.33 0.05 1.74 3.38 2.48
XGBZ-PWV -0.28 0.78 0.10 1.42 2.56 1.91
XGBZG-PWV -0.33 0.36 0.03 1.17 2.49 1.76
XGBZE-PWV -0.37 0.70 0.13 1.10 2.47 1.70
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4个模型估计PWV的bias和RMSE统计

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模型bias/mm RMSE/mm
最小值 最大值 平均值 最小值 最大值 平均值
GPT3-PWV -1.57 1.33 0.05 1.74 3.38 2.48
XGBZ-PWV -0.28 0.78 0.10 1.42 2.56 1.91
XGBZG-PWV -0.33 0.36 0.03 1.17 2.49 1.76
XGBZE-PWV -0.37 0.70 0.13 1.10 2.47 1.70
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基于XGBoost模型的广西区域PWV估算及极端降雨分析
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闫立伟 1 , 林买金 2 , 谢劭峰 1, * , 黄良珂 1 , 李向红 3 , 方琼玉 3
大地测量与地球动力学 | 大地测量学 2026,46(6): 702-709
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大地测量与地球动力学 |大地测量学 2026 , 46 (6) : 702 -709
基于XGBoost模型的广西区域PWV估算及极端降雨分析
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闫立伟, 主要研究方向为GNSS大气水汽反演, E-mail:

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闫立伟1 , 林买金2, 谢劭峰1, * , 黄良珂1, 李向红3, 方琼玉3
作者信息
  • 1 桂林理工大学测绘地理信息学院, 桂林, 541006
  • 2 长安大学地质工程与测绘学院, 西安, 710054
  • 3 桂林市气象局, 桂林, 541001
通讯作者:
谢劭峰, 教授, 主要研究方向为GNSS大气水汽监测, E-mail:
作者简介:

闫立伟, 主要研究方向为GNSS大气水汽反演, E-mail:

Estimation of PWV and Analysis of Extreme Rainfall in Guangxi Region Based on XGBoost Model
Liwei YAN1 , Maijin LIN2, Shaofeng XIE1, * , Liangke HUANG1, Xianghong LI3, Qiongyu FANG3
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
  • 3 Guilin Meteorological Bureau, Guilin 541001, China
出版时间: 2026-06-15 doi: 10.14075/j.jgg.2025.08.295
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针对气象参数缺失情况下实时/近实时GNSS PWV反演受限的问题, 基于XGBoost模型分别建立广西区域3种无需实测气象参数的PWV估算模型。首先建立输入为站点时间(DOY和HOD)、位置(Longitude、Latitude和Height)和GNSS ZTD, 输出特征为GNSS PWV的XGBZ-PWV模型, 然后在XGBZ-PWV模型的基础上分别加入2种经验PWV, 建立XGBZG-PWV模型和XGBZE-PWV模型。同时, 基于GNSS ZTD, 利用GPT3模型提供的气压与温度反演PWV(GPT3-PWV模型), 对所建PWV模型进行对比, 并以2022年广西区域基于GNSS ZTD、ERA5地表气压和温度反演的GNSS PWV作为参考值对所建立的模型进行精度验证。结果表明, 相比于GPT3-PWV模型, XGBZ-PWV、XGBZG-PWV和XGBZE-PWV模型的估算精度分别提升22.98%、29.03%和31.45%, 其中, XGBZE-PWV模型最优。在2022年2次极端降雨过程中, 对PWV与降雨的时空演变特征进行分析, 结果表明, XGBZE-PWV模型在极端天气条件下依然具有较好的适用性。

ZTD  /  PWV  /  GNSS  /  XGBoost  /  降雨

Aiming at the limitation of real/near-real-time GNSS PWV retrieval under missing meteorological parameters, three PWV estimation models without the need for measured meteorological parameters were established in the Guangxi region based on the XGBoost model. First, the XGBZ-PWV model was developed with inputs including station time (DOY and HOD), location (Longitude, Latitude, and Height), and GNSS ZTD, and the output feature being GNSS PWV. Then, based on the XGBZ-PWV model, two empirical PWV values were incorporated to establish the XGBZG-PWV and XGBZE-PWV models, respectively. For comparison, the GPT3 model was used to provide pressure and temperature for PWV retrieval based on GNSS ZTD (GPT3-PWV model). The accuracy of the established models was validated using GNSS PWV retrieved from GNSS ZTD, ERA5 surface pressure, and temperature in the Guangxi region in 2022 as the reference value. The results show that, compared to the GPT3-PWV model, the estimation accuracy of the XGBZ-PWV, XGBZG-PWV, and XGBZE-PWV models improved by 22.98%, 29.03%, and 31.45%, respectively, with the XGBZE-PWV model performing the best. During two extreme rainfall events in 2022, the spatiotemporal evolution characteristics of PWV and rainfall were analyzed. The results demonstrate that the XGBZE-PWV model maintains good applicability even under extreme weather conditions.

ZTD  /  PWV  /  GNSS  /  XGBoost  /  rainfall
闫立伟, 林买金, 谢劭峰, 黄良珂, 李向红, 方琼玉. 基于XGBoost模型的广西区域PWV估算及极端降雨分析. 大地测量与地球动力学, 2026 , 46 (6) : 702 -709 . DOI: 10.14075/j.jgg.2025.08.295
Liwei YAN, Maijin LIN, Shaofeng XIE, Liangke HUANG, Xianghong LI, Qiongyu FANG. Estimation of PWV and Analysis of Extreme Rainfall in Guangxi Region Based on XGBoost Model[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (6) : 702 -709 . DOI: 10.14075/j.jgg.2025.08.295
正文
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大气可降水量(PWV)是指垂直大气柱中从地球表面到指定对流层高度的总水蒸气含量，在气候研究及极端天气监测中具有重要意义[1-3]。地基GNSS PWV反演是通过GNSS天顶对流层延迟(ZTD)结合地表气压和温度来实现的，GNSS ZTD可通过精密单点定位(PPP)技术获取。由于大部分GNSS站并未配备气象传感器，获取实时或近实时高精度气象参数困难，限制了GNSS PWV的实时/近实时反演。
因此，有学者研究并建立无需实测气象参数的GNSS PWV反演模型。李黎等[4]、韦云等[5]基于线性模型建立无需气象参数的区域ZTD直接获取PWV，但此类模型不能精确描述ZTD和PWV之间的非线性关系，模型精度较低。随着人工智能算法的发展，深度学习和机器学习被广泛应用于各种回归和分类任务。此类算法具有较强学习数据特征的能力，能够较好地学习数据之间的非线性特征。因此，Zheng等[6]、Zhang等[7]和Huang等[8]先后使用深度学习算法建立中国、全球区域的ZTD，直接转换PWV模型。由于数据驱动模型的估算性能依赖于输入特征对输出特征变化规律的刻画能力和学习性，这些模型仅使用ZTD建模难以精确估计PWV。另有研究表明，PWV不仅是云和降水形成的必要条件，还在强降水、寒潮、台风及严重洪涝灾害等极端天气过程中起主要作用[9-11]。Xiong等[12]通过分析广西“龙舟水”期间的GNSS PWV及其驱动因素，发现GNSS PWV受风速、温度及平均海平面气压的共同影响。江婷等[13]研究表明，在充足的水汽条件下降雨的形成依赖于强烈的水汽辐合和上升作用，结合热动力条件，可以有效预测降水时间和强度。PWV值的变化与降水事件的发生存在显著的相关性，尤其是当PWV值达到一定阈值时，降雨发生的概率明显增加[14-16]。
针对以上模型的局限性，首先建立广西区域仅需输入站点时间(DOY和HOD)、位置(Longitude、Latitude和Height)和GNSS ZTD的XGBZ-PWV模型。然后引入2种经验PWV(GAskne_PWV和GXE_PWV)作为先验信息，其中GAskne_PWV基于GPT3和Askne-Nordius模型获取，GXE_PWV基于经验ERA5格网数据获取；将经验PWV值融入模型，分别建立XGBZG-PWV和XGBZE-PWV模型，并以2022年广西区域基于GNSS ZTD、ERA5地表气压和温度反演的GNSS PWV作为参考值，系统评估模型的估算精度。最后，基于广西2022年2次极端降雨过程，利用GNSS PWV、模型估算的PWV和气象站的降雨数据，分析PWV与极端降雨之间的时空变化特征，并探讨所建立模型在极端气象条件下的适用性。
1 实验数据与方法
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1.1 实验数据
所使用的数据主要包括2020-01-01—2022-12-31的广西地区地基GNSS站数据、气象站数据(图 1)和0.25°×0.25°的逐小时ERA5地表气压和温度数据。基于武汉大学PRIDE团队开发的PRIDE PPP-AR软件对GNSS观测数据进行处理得到小时ZTD数据。基于ERA5提供的地表气压和温度，使用反距离加权和垂直插值模型获取GNSS站点高度的小时气象参数进行GNSS PWV反演[17]。气象站根据“最近原则”向GNSS站提供的逐小时降雨数据[18]。
1.2 GNSS PWV反演原理
GNSS ZTD由天顶静力学延迟(ZHD)和天顶湿延迟(ZWD)组成，见式(1)。其中ZHD可由Saastamoinen模型结合地面压力数据计算获取，见式(2)。
$\mathrm{ZTD}=\mathrm{ZHD}+\mathrm{ZWD}$
$\begin{gathered}\mathrm{ZHD}=(2.2779 \pm 0.0024) P_s / \\(1-0.00266 \cdot \cos 2 \varphi-0.00028 \cdot h)\end{gathered}$
式中，h为站点椭球高；φ为站点纬度；Ps为气压；ZTD、ZHD和ZWD单位均为mm。最后，ZWD结合转换系数Π即可得到PWV，具体公式见式(3)和(4)。
$\mathrm{PWV}=\varPi \cdot \mathrm{ZWD}$
$\varPi=\frac{10^6}{\rho_w R_v\left[\left(k_3 / T_m+k_2^{\prime}\right)\right]}$
式中，Π为水汽转换系数；ρw为液态水密度；Rv为水汽比气体常数，Rv=461.5 J/(kg·K)；k3和$k_2^{\prime}$均为大气折射率常数，其中，k3=375 463 K2/hPa，$k_2^{\prime}$=22.97 K/hPa；Tm为加权平均温度，可由Bevis模型计算获得，此处的ZWD单位为mm。由于广西地区气象站分布较为稀疏，因而在GNSS PWV反演中所需的气压与温度数据均采用ERA5再分析资料获取，以保证GNSS PWV的精度[17]。
1.3 XGBoost算法原理
XGBoost是一种基于梯度提升的算法，通过迭代构建决策树使损失函数最小化。它主要通过集成多个弱学习器形成一个强学习器，提高模型的性能。XGBoost不仅具有较高预测精度，计算效率也较为出色，因而被广泛应用于各种回归任务中。XGBoost模型的目标函数为：
$\left\{\begin{array}{l}L=\sum\limits_{i=1}^n l\left(y_i, \hat{y}_i\right)+\sum\limits_{k=1}^K \varOmega\left(f_k\right) \\\varOmega\left(f_k\right)=r T+\frac{1}{2} \lambda \sum\limits_{j=1}^T \omega_j^2\end{array}\right.$
其中，L为目标函数；l为损失函数；yi、$\hat{y}_i$分别为样本i的实际值和模型估算值；n为样本数量；Ω为正则化项；K为决策树数量; (fk)为第K棵树的复杂度；r为正则化参数；λ为惩罚系数；T为树的叶节点数；ωj2为叶节点j的权重。设置的XGBoost模型树的数量为20，最大深度为7，学习率为0.1，其余参数均使用默认值。
2 模型建立与精度验证
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2.1 模型建立
1) 首先基于XGBoost模型建立一个无需气象参数的广西区域PWV估算模型(XGBZ-PWV)，模型的输入变量为站点时间信息(DOY和HOD)、位置信息(Longitude、Latitude和Height)和GNSS ZTD，输出变量为GNSS PWV。
2) 基于Askne-Nordius模型，联合式(3)和式(4)得到式(6)：
$\mathrm{PWV}=\frac{R_d e}{\rho_w R_v(\lambda+1) g_m}$
式中，e为地表水汽压；λ为水汽压递减因子；gm为重力系数；Rd=287.058 J/(kg·K)为干空气气体常数；其余参数与§1.2相同。GPT3模型可提供1°×1°的λ和e。
将式(6)得到的PWV命名为GAskne_PWV，将其作为PWV先验信息并作为XGBZ-PWV模型的输入，建立XGBZG-PWV模型。
3) 通过对ERA5分层数据进行数值积分可获得地表PWV值，根据式(7)建立一个0.25°×0.25°的广西区域地表经验PWV模型(GXE_PWV):
$\begin{gathered}\mathrm{PWV}^i=A_0^i+A_1^i \cos \left(2 \pi \frac{\mathrm{DOY}}{365.25}\right)+ \\A_2^i \sin \left(2 \pi \frac{\mathrm{DOY}}{365.25}\right)+A_3^i \cos \left(4 \pi \frac{\mathrm{DOY}}{365.25}\right)+ \\A_4^i \sin \left(4 \pi \frac{\mathrm{DOY}}{365.25}\right)\end{gathered}$
式中，i为格网点数量；PWVi为第i个格网点的PWV值；$A_0^i$为第i个格网的年均值；$A_1^i、A_2^i$为第i个格网点的年周期振幅系数；$A_3^i、A_4^i$为第i个格网点的半年周期振幅系数；DOY为年积日。
利用林买金等[19]建立的C-GPWV模型可将地表经验PWV值改正到GNSS站点高度。基于XGBZ-PWV模型，将GXE_PWV模型得到的经验PWV值作为模型先验值参与建模，建立XGBZE-PWV模型。3个模型的详细信息见表 1。
以上3个模型均使用2020-01-01—2021-12-31共2 a的数据进行建模，2022-01-01—12-31数据用于模型验证，数据的时间分辨率均为1 h。同时，基于GNSS获得的ZTD，利用GPT3模型提供的气压和温度由式(1)、式(2)、式(3)和式(4)反演PWV(称为GPT3-PWV模型)，与所建PWV模型进行对比。
2.2 精度评价指标
使用bias与均方根误差(RMSE)评价模型精度：
$\operatorname{bias}=\frac{1}{n} \sum\limits_{i=1}^n\left(y_i-\hat{y}_i\right)$
$\mathrm{RMSE}=\sqrt{\frac{1}{n} \sum\limits_{i=1}^n\left(y_i-\hat{y}_i\right)^2}$
式中，yi为GNSS PWV实际值；$\hat{y}_i$为模型PWV值; n为样本数量。
2.3 精度评估
以2022年广西区域基于GNSS ZTD、ERA5地表气压和温度反演的GNSS PWV作为参考值，验证所建立的XGBZ-PWV、XGBZG-PWV和XGBZE-PWV 3个模型的估算精度，并与GPT3-PWV模型进行对比。分别计算4个模型的平均bias和RMSE(表 2)，并绘制站点误差空间分布(图 2)。
由表 2可知，相比于GPT3-PWV模型，新建立的3个模型的估算精度均有所提升。从bias来看，GPT3-PWV模型的bias范围变化较大，而XGBZ-PWV、XGBZG-PWV和XGBZE-PWV三个模型的bias变化范围较小。从RMSE来看，所建立3个模型的平均RMSE远小于GPT3-PWV模型。相比于GPT3-PWV模型，XGBZ-PWV、XGBZG-PWV和XGBZE-PWV估算精度分别提升22.98%、29.03%和31.45%，其中XGBZE-PWV提升幅度最大。相比于XGBZ-PWV模型，XGBZG-PWV和XGBZE-PWV由于增加了先验条件，从而提高了模型的PWV估算能力。
由图 2可知，GPT3-PWV模型存在较大的偏差，且地域性分布明显，主要表现为东部和南部地区为正bias，而北部和西部地区表现为负bias。XGBZ-PWV、XGBZG-PWV和XGBZE-PWV整体表现为较小的正bias，分布较稳定。从RMSE分布可知，GPT3-PWV模型的整体RMSE均较大，所建立3个模型的整体RMSE均较小。整体上，低海拔区域的PWV估算精度要高于高海拔区域。相比于XGBZ-PWV模型，XGBZG-PWV和XGBZE-PWV在大多数站点具有更小的RMSE，主要表现在部分中部地区以及东南部区域，说明在建模过程中加入经验PWV是有必要的。XGBZG-PWV和XGBZE-PWV在空间分布上相差较小，均较稳定。
为分析4个模型在不同季节的估算性能，分别计算各模型在春季、夏季、秋季和冬季的平均bias和RMSE，结果见图 3。
由图 3可知，GPT3-PWV模型在4个季节的绝对bias均最大，相比之下, XGBZ-PWV、XGBZG-PWV和XGBZE-PWV整体bias较小，其中XGBZG-PWV和XGBZE-PWV较稳定。由RMSE的季节性变化可知，GPT3-PWV模型均表现出较大的RMSE。在夏季，XGBZE-PWV和XGBZG-PWV模型引入了先验PWV信息，经验PWV的时间和空间分辨率有限，并且只能反映长时间PWV的变化趋势，不能刻画详细的PWV变化，对于强对流天气反而会引入数据误差，从而在复杂气象条件下增加模型的拟合难度。但整体上XGBZG-PWV和XGBZE-PWV模型表现较好。基于XGBoost模型建立的3个估算模型均表现出更好的性能，其中XGBZE-PWV模型在每个季节的估算性能都更优，说明此模型能够较好地学习PWV的季节性变化。
以上结果表明，XGBoost模型能够较好地学习ZTD和PWV之间的非线性特征，并且在建模过程中通过增加经验PWV先验值，可提高无气象参数情况下PWV的估算精度。
3 GNSS PWV与降雨的时空分析
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由§2.3可知，XGBZE-PWV模型表现更优异，因此后续使用该模型估算的PWV参与降雨分析。基于GNSS PWV、XGBZE-PWV估算的PWV和最近的气象站提供的降雨数据分析2个时间段(广西“龙舟水”期间(2022-06-15—20)和台风“暹芭”期间(2022-07-01—05))PWV与降雨之间的关系(图 4)。
由图 4可知，JZ95与JZ18在06-16、06-17和06-20期间降雨密集，PWV表现为高频上升。JZ33与JZ05在7月初的极端降雨事件中，PWV分别上升至75~80 mm，显示长时间水汽积累与极端降水的直接关联。在降雨峰值区，PWV主要呈现上升趋势，并且PWV峰值与降雨峰值之间存在时间差，这也验证了大气水汽积累是降雨形成的主要前提。降雨过程中或降雨后，PWV出现短暂下降，主要是因为降雨过程消耗大气中的大部分水汽。从4个子图可以看出，XGBZE-PWV模型的PWV估算值与GNSS PWV数值上有差异，但其时序变化基本一致，峰值区间也基本相同，这也表明XGBZE-PWV模型估算的PWV在极端环境下仍然具有较好的性能。
为分析PWV和降雨之间的短期空间变化，分别选择2022-06-15、2022-06-17和2022-06-20，2022-07-01、2022-07-03和2022-07-05作为分析时间，并使用自然邻域插值获取该时间段的GNSS PWV、XGBZE-PWV模型估算的PWV及气象站提供的降水数据的连续PWV和降雨量空间图。结果见图 5(“龙舟水”期间)和图 6(台风“暹芭”期间)。
由图 5可知，XGBZE-PWV模型估算的PWV与GNSS PWV具有较强的相似性，主要表现在PWV高值区域及水汽梯度变化。由3 d的PWV变化情况可知，在广西中部, PWV值达60~72 mm。并且随着时间的推移，水汽从东南逐渐向中部和北部移动。广西地势呈现西北高、东南低的走势，而水汽也主要集中在东南地区，西北区域值较低，这说明地形对水汽具有阻滞与聚集作用。降雨量的空间分布主要表现为“点-片”特征，并且主要降雨区域从东南区域逐渐向中部和北部移动。PWV高值区主要对应降雨的核心区，其降雨量主要在5~6 mm。降雨主要持续在东南地区和中部，这些区域3 d的PWV值均较高，表明充足的水汽条件是维持降水过程的重要前提。
由图 6可知，虽然3 d XGBZE-PWV估算的PWV值略低于GNSS PWV，但模型PWV值与GNSS PWV空间分布较为一致。07-01，除了南部与东南地区PWV值略高，其余地方PWV均较小，并无明显的降雨区。07-03，除西北部，其余区域PWV值均较高，在东南和中部地区值最大，且此时降雨的核心区域与PWV高值区相同，表明水汽充足的区域对应降雨集中区。07-05，PWV高值区主要集中在东南区域，降雨区与PWV高值区有差异，这说明天气系统的演变仍然是影响降水空间分布和强度变化的重要原因。
整体来看，XGBZE-PWV模型估算的PWV和GNSS PWV空间分布上较为一致，说明模型在极端降雨环境下精度相对较好，可用于极端环境下的水汽分析。
4 结语
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针对气象参数缺失情况下实时/近实时PWV反演受限的问题，基于XGBoost算法分别构建XGBZ-PWV模型、XGBZG-PWV模型和XGBZE-PWV模型，并评价模型的精度；后续分析极端降雨环境下模型估算的PWV、GNSS PWV和降雨的时空变化。主要结论如下：
1) 3个模型相比于GPT3-PWV模型均较为优异，XGBZ-PWV、XGBZG-PWV和XGBZE-PWV估算精度分别提升22.98%、29.03%和31.45%。XGBZG-PWV和XGBZE-PWV由于增加了经验PWV建模，提升了模型的精度，其中，XGBZE-PWV精度最高。
2) 在2022年2次极端降雨过程中，对PWV与降雨的时空演变特征进行分析。结果表明，水汽积累是降雨形成的主要前提，而地形对水汽具有阻滞与聚集作用。XGBZE-PWV模型估算的PWV与GNSS PWV具有较好的时空一致性，在极端水汽条件下依然具有较高的适用性。
基金
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  • 广西自然科学基金(2023GXNSFAA026434)
文献
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2026年第46卷第6期
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doi: 10.14075/j.jgg.2025.08.295
  • 接收时间：2025-08-24
  • 首发时间：2026-07-09
  • 出版时间：2026-06-15
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  • 收稿日期：2025-08-24
基金
广西自然科学基金(2023GXNSFAA026434)
作者信息
    1 桂林理工大学测绘地理信息学院, 桂林, 541006
    2 长安大学地质工程与测绘学院, 西安, 710054
    3 桂林市气象局, 桂林, 541001

通讯作者:

谢劭峰, 教授, 主要研究方向为GNSS大气水汽监测, E-mail:
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