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Article(id=1322506170674295162, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506166672937516, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.08.401, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1724083200000, receivedDateStr=2024-08-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793239642065, onlineDateStr=2026-10-29, pubDate=1755187200000, pubDateStr=2025-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793239642065, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793239642065, creator=13701087609, updateTime=1793239642065, 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=791, endPage=795, ext={EN=ArticleExt(id=1322506171458630011, articleId=1322506170674295162, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=A Bias Correction Model of ZWD Based on RF_Adaboost Neural Network over China, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Previous ZWD models over China only consider the effects of elevation, latitude and meteorological parameters, without taking into account the impact of monsoon climate and ocean water vapor transport on ZWD estimation. To solve this problem, we take bias correction as the breakthrough point, analyze the spatial distribution of RMSE and bias of ERA5-ZWD estimation value obtained by the ERA5 data integration method and inversion method over China from 2016 to 2019. Then, the regional bias correction model over China based on RF_Adaboost neural network is established using the ERA5 and radiosonde data from 2016 to 2018. The results show that the bias of ERA5-ZWD estimation value in 2019 is significantly reduced after correction, and the RMSE is significantly reduced in the eastern coastal areas and southern regions of China.

, authors=Xiangtian PENG1, Lulu ZHANG2, Maijin LIN1, Qingbing XU1, Junwen LIU1, Shaofeng XIE1, *, authorsList=Xiangtian PENG, Lulu ZHANG, Maijin LIN, Qingbing XU, Junwen LIU, Shaofeng XIE, authorCompany=null, correspAuthors=Shaofeng XIE, authorNote=

PENG Xiangtian, postgraduate, majors in GNSS atmospheric sounding, 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=1322506177758474629, articleId=1322506170674295162, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=基于RF_Adaboost神经网络的中国区域ZWD偏差改正模型, 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=

以往的中国区域ZWD模型只考虑高程、纬度、气象参数等因子的影响，未顾及季风气候和海洋水汽输送对ZWD估计的影响。针对该问题，以偏差改正为切入点，首先以探空站积分ZWD为参考值，分析2016—2019年由ERA5数据积分法和反算法得到的ERA5-ZWD估计值在中国区域的均方根误差和偏差的空间分布，然后利用2016—2018年ERA5和探空站数据建立基于RF_Adaboost神经网络的中国区域偏差改正模型。结果表明，2019年ERA5-ZWD估计值经过偏差改正后偏差大幅降低，均方根误差在中国东部沿海地区和南方地区显著降低。

, authors=彭祥天1, 张露露2, 林买金1, 徐庆兵1, 刘俊文1, 谢劭峰1, *, authorsList=彭祥天, 张露露, 林买金, 徐庆兵, 刘俊文, 谢劭峰, authorCompany=null, correspAuthors=谢劭峰, authorNote=

彭祥天，硕士生，主要从事GNSS大气探测研究，E-mail:

, correspAuthorsNote=

谢劭峰，教授，主要从事GNSS气象学研究，E-mail:

XIE Shaofeng, professor, majors in GNSS meteorology, 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=NgCDaC3ON00RFKqO3ipMkw==, magXml=cRIe1y4IDe5ewQVqqKY5EA==, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=hu+2YwmSq+L1qGCCy6MVYQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ptE57OXyLE051nDk/2VZ+A==, mapNumber=null, fund=null)}, authors=[Author(id=1322506179050320269, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506170674295162, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=17786567710@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322506179142594959, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506170674295162, authorId=1322506179050320269, language=EN, stringName=Xiangtian PENG, firstName=Xiangtian, middleName=null, lastName=PENG, 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, 319 Yanshan Street, Guilin 541006, China, bio={"content":"

PENG Xiangtian, postgraduate, majors in GNSS atmospheric sounding, E-mail:

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PENG Xiangtian, postgraduate, majors in GNSS atmospheric sounding, E-mail:

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彭祥天，硕士生，主要从事GNSS大气探测研究，E-mail:

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journalId=1281212831689347082, articleId=1322506170674295162, language=EN, label=Tab. 1, caption=

The overall accuracy before and after bias correction in 2019

, figureFileSmall=null, figureFileBig=null, tableContent=
类型 MAE/mm RMSE/mm
ERA5P_ZWD 12.22 18.63
ERA5PT_ZWD 12.75 19.25
ERA5P_RA_ZWD 11.07 17.25
ERA5PT_RA_ZWD 11.19 17.41
), ArticleFig(id=1322506187774472640, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506170674295162, language=CN, label=表1, caption=

2019年偏差改正前后整体精度

, figureFileSmall=null, figureFileBig=null, tableContent=
类型 MAE/mm RMSE/mm
ERA5P_ZWD 12.22 18.63
ERA5PT_ZWD 12.75 19.25
ERA5P_RA_ZWD 11.07 17.25
ERA5PT_RA_ZWD 11.19 17.41
), ArticleFig(id=1322506187866747329, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506170674295162, language=EN, label=Tab. 2, caption=

ZWD RMSE before and after bias correction

, figureFileSmall=null, figureFileBig=null, tableContent=
站点 ERA5P_ZWD/mm ERA5PT_ZWD/mm ERA5P_RA_ZWD/mm ERA5PT_RA_ZWD/mm
临河 30.86 30.29 21.15 21.10
长春 22.63 22.00 18.67 18.46
临江 19.12 23.53 14.70 15.51
威宁 19.97 19.26 18.58 18.44
腾冲 18.90 19.04 16.12 16.44
思茅 31.69 31.28 21.90 22.09
蒙自 22.89 26.46 20.66 21.08
郑州 14.60 15.66 11.29 11.39
泾河 15.62 16.86 12.75 12.76
宜昌 18.97 19.95 13.84 13.70
郴州 25.34 28.15 21.79 22.62
赣县 33.65 33.50 27.94 28.26
福州 24.98 26.86 21.92 22.73
台北 25.99 27.41 21.43 22.33
厦门 24.90 22.17 21.86 21.35
百色 25.45 26.85 22.39 23.49
梧州 27.37 25.13 24.26 24.10
), ArticleFig(id=1322506188198097346, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506170674295162, language=CN, label=表2, caption=

偏差改正前后ZWD均方根误差

, figureFileSmall=null, figureFileBig=null, tableContent=
站点 ERA5P_ZWD/mm ERA5PT_ZWD/mm ERA5P_RA_ZWD/mm ERA5PT_RA_ZWD/mm
临河 30.86 30.29 21.15 21.10
长春 22.63 22.00 18.67 18.46
临江 19.12 23.53 14.70 15.51
威宁 19.97 19.26 18.58 18.44
腾冲 18.90 19.04 16.12 16.44
思茅 31.69 31.28 21.90 22.09
蒙自 22.89 26.46 20.66 21.08
郑州 14.60 15.66 11.29 11.39
泾河 15.62 16.86 12.75 12.76
宜昌 18.97 19.95 13.84 13.70
郴州 25.34 28.15 21.79 22.62
赣县 33.65 33.50 27.94 28.26
福州 24.98 26.86 21.92 22.73
台北 25.99 27.41 21.43 22.33
厦门 24.90 22.17 21.86 21.35
百色 25.45 26.85 22.39 23.49
梧州 27.37 25.13 24.26 24.10
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基于RF_Adaboost神经网络的中国区域ZWD偏差改正模型
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彭祥天 1 , 张露露 2 , 林买金 1 , 徐庆兵 1 , 刘俊文 1 , 谢劭峰 1, *
大地测量与地球动力学 | 现代大地测量 2025,45(8): 791-795
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大地测量与地球动力学 |现代大地测量 2025 , 45 (8) : 791 -795
基于RF_Adaboost神经网络的中国区域ZWD偏差改正模型
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彭祥天1 , 张露露2, 林买金1, 徐庆兵1, 刘俊文1, 谢劭峰1, *
作者信息
  • 1 桂林理工大学测绘地理信息学院，桂林市雁山街319号，541006
  • 2 桂林理工大学旅游与风景园林学院，桂林市雁山街319号，541006
通讯作者:

谢劭峰，教授，主要从事GNSS气象学研究，E-mail:

XIE Shaofeng, professor, majors in GNSS meteorology, E-mail:

作者简介:

彭祥天，硕士生，主要从事GNSS大气探测研究，E-mail:

A Bias Correction Model of ZWD Based on RF_Adaboost Neural Network over China
Xiangtian PENG1 , Lulu ZHANG2, Maijin LIN1, Qingbing XU1, Junwen LIU1, Shaofeng XIE1, *
Affiliations
  • 1 College of Geomatics and Geoinformation, Guilin University of Technology, 319 Yanshan Street, Guilin 541006, China
  • 2 College of Tourism and Landscape Architecture, Guilin University of Technology, 319 Yanshan Street, Guilin 541006, China

    • PENG Xiangtian, postgraduate, majors in GNSS atmospheric sounding, E-mail:

出版时间: 2025-08-15 doi: 10.14075/j.jgg.2024.08.401
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摘要
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以往的中国区域ZWD模型只考虑高程、纬度、气象参数等因子的影响，未顾及季风气候和海洋水汽输送对ZWD估计的影响。针对该问题，以偏差改正为切入点，首先以探空站积分ZWD为参考值，分析2016—2019年由ERA5数据积分法和反算法得到的ERA5-ZWD估计值在中国区域的均方根误差和偏差的空间分布，然后利用2016—2018年ERA5和探空站数据建立基于RF_Adaboost神经网络的中国区域偏差改正模型。结果表明，2019年ERA5-ZWD估计值经过偏差改正后偏差大幅降低，均方根误差在中国东部沿海地区和南方地区显著降低。

ERA5  /  天顶湿延迟  /  RF_Adaboost  /  偏差改正

Previous ZWD models over China only consider the effects of elevation, latitude and meteorological parameters, without taking into account the impact of monsoon climate and ocean water vapor transport on ZWD estimation. To solve this problem, we take bias correction as the breakthrough point, analyze the spatial distribution of RMSE and bias of ERA5-ZWD estimation value obtained by the ERA5 data integration method and inversion method over China from 2016 to 2019. Then, the regional bias correction model over China based on RF_Adaboost neural network is established using the ERA5 and radiosonde data from 2016 to 2018. The results show that the bias of ERA5-ZWD estimation value in 2019 is significantly reduced after correction, and the RMSE is significantly reduced in the eastern coastal areas and southern regions of China.

ERA5  /  zenith wet delay  /  RF_Adaboost  /  bias correction
彭祥天, 张露露, 林买金, 徐庆兵, 刘俊文, 谢劭峰. 基于RF_Adaboost神经网络的中国区域ZWD偏差改正模型. 大地测量与地球动力学, 2025 , 45 (8) : 791 -795 . DOI: 10.14075/j.jgg.2024.08.401
Xiangtian PENG, Lulu ZHANG, Maijin LIN, Qingbing XU, Junwen LIU, Shaofeng XIE. A Bias Correction Model of ZWD Based on RF_Adaboost Neural Network over China[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (8) : 791 -795 . DOI: 10.14075/j.jgg.2024.08.401
正文
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天顶湿延迟(zenith wet delay, ZWD)是进行GNSS水汽反演的重要参量。虽然ZWD只占天顶对流层延迟(zenith tropospheric delay, ZTD)的10%左右，但大气中的水汽含量变化频繁，导致难以准确估计ZWD。目前主要有两类ZWD估计模型，一类是基于实测气象参数构建的模型，如Hopfield、Saastamoinen和Black模型，这类模型输入特定气象参数能获得cm级ZWD估计值; 另一类是无需气象参数的模型，如GPT系列、UNB系列和EGNOS系列模型，这些模型只需要时间和位置参数就能获得目标位置的ZWD估计值。上述ZWD模型大多是由单一映射函数建立的，难以捕捉ZWD精细的非线性变化。Bi等[1]利用卷积神经网络算法建立基于时空因素和气象参数的ZWD预测模型，该模型在中国区域的精度达到27 mm，展现了神经网络算法强大的非线性拟合能力。黎俊宇等[2]利用广义回归神经网络算法和高质量探空数据实现了GRAPES ZWD和ERA5 ZWD的无偏融合，优化后的GRAPES ZWD与ERA5 ZWD总体偏差趋近于0，说明神经网络算法具备优越的偏差处理能力。Li等[3]在ERA5-ZTD的基础上加入高程改正和残差改正构建IGGZTD-S模型。He等[4]建立基于长短期记忆(long short-term memory, LSTM)神经网络的ZTD残差改正模型，其RMSE达到14.4 mm。
在全球ZWD模型中，由于未考虑中国特殊地理位置给ZWD建模带来的影响，中国区域的ZWD估计精度往往不佳[5-6]。以往的中国区域ZWD建模只考虑纬度和高程等空间因子对ZWD的影响，而季风气候也是水汽及ZWD变化的重要影响因素[7-8]。针对上述问题，本文以偏差为切入点，首先分析2016—2019年由ERA5气象参数计算的2种ZWD估计值与探空站积分计算的ZWD之间的均方根误差和偏差在空间上的分布特征，然后利用2016—2018年ERA5数据、探空站数据和RF_Adaboost神经网络对偏差进行建模，将偏差改正模型应用于2019年ERA5数据计算的ZWD估计值，并以2019年探空站积分ZWD为参考值进行精度评估。
1 数据来源与处理
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美国怀俄明大学可提供时间分辨率为12 h的实测地表及分层探空气象数据。本文使用积分法计算探空站位置ZWD，简称RS_ZWD，探空站分布如图 1所示。
ERA5数据是欧洲中期天气预报中心发布的第五代再分析资料，可提供从1979年至今空间分辨率为0.25°×0.25°的全球地表以及大气分层气象数据，时间分辨率为1 h。利用ERA5单层和分层数据积分计算的ZWD(ERA5P_ZWD)通常精度较高，原因为这种计算方式包含了气象数据在空间中的变化，但过程较为繁杂。通过大气可降水量(precipitable water vapor, PWV)和大气加权平均温度(Tm)反算得到ZWD(ERA5PT_ZWD)的方法更为便捷高效。其中PWV和地表温度(Ts)可直接从ERA5单层数据中下载，Tm可由经验公式获得，相关计算公式如下[9-10]:
$\mathrm{ZWD}=10^{-6} \displaystyle{\int}_h^{\infty} N_{\mathrm{w}} \mathrm{~d} h$
$N_{\mathrm{w}}=k_2 e / T+k_3 e / T^2$
$e=\operatorname{Sh} \cdot P / 0.622$
$T_m=105.45+0.594 T_s$
$\mathrm{PWV}=\mathrm{ZWD} \cdot K$
$K=10^6 / \rho R_v\left(\frac{k_3}{T_m}+k_2^{\prime}\right)$
式中，Nw表示大气湿折射系数; h表示高程; T表示温度; Sh表示比湿; K表示转换系数; ρ表示水密度; 其余为模型系数。
由于探空站与ERA5再分析资料的格网点位置不同，探空站位置处的ZWD可通过附近4个格网点内插得到，在插值前需要将ERA5气象参数的高程与探空站统一以免影响插值精度。其中比湿和温度采用文献[11]中的插值方式，气压改正采用T0气压垂直改正模型[12]。
2 精度评估
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2.1 ERA5P_ZWD和ERA5PT_ZWD精度评估
以RS_ZWD为参考值，分析基于ERA5气象参数计算的2种ZWD估计值(ERA5P_ZWD、ERA5PT_ZWD)在中国区域的偏差分布，结果如图 2(图中选取范围为探空站分布范围，图 3、4同此)所示。
由图 2可知，ERA5P_ZWD和ERA5PT_ZWD的偏差分布存在显著的区域性。西部地区整体偏差最小，原因为西部地区处于内陆，水汽变化较为稳定; 东部地区偏差绝对值较高，这可能是因为东部地区地势较为平坦，容易受到季风气候的影响。从偏差空间分布来看，西藏地区呈现明显的正偏差，而青藏高原地区呈现明显的负偏差，这是因为2个站点间地势的急剧增高阻挡了从海洋方向携带的大量水汽，给两地的ZWD估计带来额外的变量; 30°N以上区域呈现明显的正偏差，30°N以下区域既存在正偏差又存在负偏差，来自印度洋方向和太平洋方向的水汽输送在南方地区交汇，使得ZWD的空间分布更具差异性，同时也更难以估计。总体而言，大多数站点在2016—2019年都存在较为稳定的正偏差或负偏差，这说明利用偏差改正方法提升ZWD估计精度具有巨大潜力。从空间变化趋势来看，从西北到东南方向偏差逐渐增大。
为进一步分析ERA5P_ZWD和ERA5PT_ZWD在中国区域的精度表现，计算2种ZWD估计值在中国区域的均方根误差分布，如图 3所示。
由图 3可知，西部地区和东北地区除少数站点外，大部分站点的均方根误差都小于15 mm; 东部地区的均方根误差相对较大，在15~40 mm之间。从均方根误差的空间分布来看，南方地区的均方根误差均大于25 mm; 相对于内陆地区，沿海地区的均方根误差更大，这是因为沿海地区容易受到海洋水汽的影响。结合图 2、3可知，对于偏差绝对值较大的站点，其均方根误差也较大，这说明东部地区的偏差存在一定的改正空间; 南方地区部分站点对应的偏差较小，但均方根误差仍大于20 mm，进一步说明季风气候会影响ZWD的精确估计。从空间变化趋势来看，中国区域的均方根误差从西北到东南方向逐渐增大。
2.2 基于RF_Adaboost神经网络的偏差改正模型
RF(random forest, RF)算法是一种基于决策树的口袋算法，在处理存在噪声的基础数据时容易出现过拟合现象。此外，当特征过多时，RF模型的效率和精度也会降低。传统的boosting算法为每个弱学习器分配相同的权重，最终的预测结果是重复学习中各弱学习器的共识，易受样本中异常值的影响。Adaboost算法是一种融合多个弱学习器的自适应回归算法，通过为弱学习器分配不同的权重来应对误差损失问题，从而实现逐步优化。在RF_Adaboost神经网络算法中，将RF算法作为Adaboost算法的弱学习器对学习权重进行优化，为错误率较低的学习器分配更高的权重，从而提高了分类准确性和抗噪声能力[13]。本文利用2016—2018年ERA5和探空站数据，建立基于RF_Adaboost神经网络的ZWD偏差改正模型，其输入特征由纬度、经度、高程、ERA5P_ZWD、年积日构成，输出为ERA5P_ZWD与RS_ZWD之间的偏差，ERA5PT_ZWD的偏差模型构建过程与ERA5P_ZWD类似。经过偏差改正后的ZWD估计值为RF_Adaboost偏差模型获取的偏差值与改正前ZWD估计值之和，分别为ERA5P_RA_ZWD和ERA5PT_RA_ZWD。
2.3 偏差改正模型精度评估
本文以2019年中国区域88个探空站数据为参考值，分别计算ERA5P_ZWD、ERA5PT_ZWD、ERA5P_RA_ZWD和ERA5PT_RA_ZWD的平均绝对偏差和均方根误差(表 1)。
由表 1可知，ERA5P_ZWD和ERA5PT_ZWD经过RF_Adaboost偏差改正后平均绝对偏差分别下降9.4%和12.2%，均方根误差分别下降7.4%和9.6%，表明RF_Adaboost偏差改正模型可有效提高ERA5P_ZWD和ERA5PT_ZWD的ZWD估计精度。
为进一步分析偏差改正模型的性能，以2019年探空站积分ZWD为参考值计算ERA5P_RA_ZWD和ERA5PT_RA_ZWD的偏差分布，结果如图 4(a)和4(b)所示; 图 4(c)和4(d)分别为ERA5P_RA_ZWD和ERA5PT_RA_ZWD相对于ERA5P_ZWD和ERA5PT_ZWD的均方根误差提升率。
由图 4可知，ERA5P_RA_ZWD和ERA5PT_RA_ZWD在西部地区的偏差接近于0;东部地区部分站点的偏差绝对值较大，其中正偏差均小于15 mm，负偏差均小于10 mm。相比于ERA5P_ZWD和ERA5PT_ZWD，ERA5P_RA_ZWD和ERA5PT_RA_ZWD在中国区域的偏差绝对值均有所降低，这种偏差优化现象在东部地区及沿海地区更为明显，说明偏差改正有利于减弱复杂水汽环境对ZWD估计的影响。简言之，RF_Adaboost偏差改正模型能弱化偏差分布的区域性，削弱偏差对ZWD估计的影响。
从均方根误差提升率的区域性分布来看，相对于西部地区，东部地区的提升更为显著，尤其是东部沿海地区站点，均方根误差提升率均大于20%;西部地区站点由于偏差较小，偏差改正模型在该地区难以获得理想的改正结果。对于偏差几乎为0的站点，偏差改正后的均方根误差提升率也接近于0;对于西部存在较大偏差的站点，经偏差改正后均方根误差提升率在5%~10%之间。从均方根误差的提升率分布来看，除少数站点均方根误差提升率小于0或接近于0，绝大多数站点均方根误差的提升率均不低于10%，说明偏差改正模型能稳定提升ERA5P_ZWD和ERA5PT_ZWD的精度。
由上述分析可知，偏差改正前的偏差绝对值与偏差改正后的均方根误差具有密切关系。为进一步分析偏差改正前偏差绝对值与偏差改正后均方根误差的关系，将2016—2018年偏差改正前ERA5P_ZWD和ERA5PT_ZWD的偏差绝对值大于9 mm的站点提取出来，统计其2019年偏差改正前后的均方根误差(表 2)。
由表 2可知，偏差改正前ERA5P_ZWD和ERA5PT_ZWD的均方根误差为14.60~33.65 mm，而经过偏差改正后ERA5P_RA_ZWD和ERA5PT_RA_ZWD的均方根误差为11.29~28.26 mm，说明偏差改正模型能显著提升ERA5P_ZWD和ERA5PT_ZWD的精度。总体来说，具有较大偏差的站点经过偏差改正后，均方根误差明显降低，总体提升率为7.6%~32%;临江站、思茅站、宜昌站的均方根误差提升率达到30%，说明偏差改正对存在明显偏差的站点具有较好的优化效果。
3 结语
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本文基于ERA5气象参数，分别以积分法和反算法获取中国区域88个探空站2016—2019年ZWD估计值，并以RS_ZWD为参考值进行精度评估。结果表明，ERA5P_ZWD和ERA5PT_ZWD在中国区域的ZWD估计精度相当; 偏差分布均存在显著的区域性，西部地区偏差较小，而东部地区存在大量高绝对值偏差。
以ERA5P_ZWD和ERA5PT_ZWD与RS_ZWD之间的偏差为研究对象，构建基于RF_Adaboost神经网络的偏差改正模型，并利用未参与建模的2019年RS_ZWD进行模型验证。结果表明，相对于ERA5P_ZWD和ERA5PT_ZWD，ERA5P_RA_ZWD和ERA5PT_RA_ZWD的平均绝对偏差和均方根误差分别降低9.4%、12.2%和7.4%、9.6%。进一步分析显示，偏差改正ZWD提升率与改正前偏差绝对值有关，改正前偏差绝对值较大的站点精度提升明显; 改正前偏差绝对值较小的站点精度无明显提升，甚至个别站点出现负提升现象。整体而言，基于RF_Adaboost神经网络的偏差改正模型可有效提高ERA5P_ZWD和ERA5PT_ZWD的估计精度，尤其在南方地区和东部沿海地区提升效果更明显。
基金
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  • 广西自然科学基金(2023GXNSFAA026434)
文献
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2025年第45卷第8期
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引用本文
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文章信息
doi: 10.14075/j.jgg.2024.08.401
  • 接收时间：2024-08-20
  • 首发时间：2026-10-29
  • 出版时间：2025-08-15
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出版历史
  • 收稿日期：2024-08-20
基金
Guangxi Natural Science Foundation(2023GXNSFAA026434)
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，桂林市雁山街319号，541006
    2 桂林理工大学旅游与风景园林学院，桂林市雁山街319号，541006

通讯作者:

谢劭峰，教授，主要从事GNSS气象学研究，E-mail:

XIE Shaofeng, professor, majors in GNSS meteorology, E-mail:

参考文献
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https://castjournals.cast.org.cn/joweb/ddcl/CN/10.14075/j.jgg.2024.08.401
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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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