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Article(id=1322506419316834508, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.11.504, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1730736000000, receivedDateStr=2024-11-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793239701345, onlineDateStr=2026-10-29, pubDate=1760457600000, pubDateStr=2025-10-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793239701345, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793239701345, creator=13701087609, updateTime=1793239701345, updator=13701087609, issue=Issue{id=1322506335908901129, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='10', pageStart='991', pageEnd='1100', issueExtLink='null', onlineDate='null', pubDate='1760457600000', pubDateStr='2025-10-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793239681460, creator='13701087609', updateTime=1793244369709, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322525999896576487, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322525999896576488, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1033, endPage=1036, ext={EN=ArticleExt(id=1322506419513966797, articleId=1322506419316834508, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=ERA5 PWV Prediction Based on FFT and ConvLSTM, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

This paper utilizes the ERA5 multi-element dataset to predict ERA5 water vapor by integrating the fast Fourier transform(FFT) and convolutional long short-term memory(ConvLSTM) methods. Firstly, the common periods of various meteorological elements are extracted using FFT. Subsequently, multiple meteorological elements with different common periods and individual water vapor data are selected as model inputs, and the ConvLSTM method is employed to train the ERA5 water vapor and construct the prediction model. Finally, the prediction performance of the model for point and spatial water vapor is evaluated using GNSS water vapor and actual ERA5 water vapor. The results indicate that the ERA5 water vapor prediction method based on FFT and ConvLSTM can effectively predict water vapor for the next 120 hours, with high accuracy in both single-point and spatial areas, providing a data foundation for short-term precipitation warnings.

, authors=Yong WANG1, Xiangshun MENG1, Wei DU1, Yanping LIU2, Yubo LIU1, authorsList=Yong WANG, Xiangshun MENG, Wei DU, Yanping LIU, Yubo LIU, authorCompany=null, correspAuthors=null, authorNote=

WANG Yong, PhD, professor, majors in GNSS meteorology, 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=1322506420491239636, articleId=1322506419316834508, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=基于FFT与ConvLSTM的ERA5水汽预测, 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=

利用ERA5多要素数据集，综合FFT与ConvLSTM方法开展ERA5水汽预测。首先采用FFT提取各气象要素的公共周期；然后分别选择不同公共周期的多气象要素数据、单一水汽数据作为模型输入，使用ConvLSTM方法进行训练并构建ERA5水汽预测模型；最后以GNSS水汽、ERA5实测水汽分别开展ERA5点、面水汽的模型预测效果评价。结果表明，基于FFT与ConvLSTM的ERA5水汽预测方法可有效预测未来120 h的水汽，在单点和面空间范围内均有较高精度，可为短临降水预警提供数据基础。

, authors=王勇1, 孟祥顺1, 杜玮1, 刘严萍2, 刘宇博1, authorsList=王勇, 孟祥顺, 杜玮, 刘严萍, 刘宇博, authorCompany=null, correspAuthors=null, authorNote=

王勇，博士，教授，主要研究方向为GNSS气象学，E-mail:

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WANG Yong, PhD, professor, majors in GNSS meteorology, E-mail:

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WANG Yong, PhD, professor, majors in GNSS meteorology, E-mail:

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王勇，博士，教授，主要研究方向为GNSS气象学，E-mail:

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王勇，博士，教授，主要研究方向为GNSS气象学，E-mail:

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Common cycles and corresponding frequencies of each element type

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时间 公共变化周期1/h cpd 公共变化周期2/h cpd 公共变化周期3/h cpd
春季 71 0.34 52 0.46 43 0.56
夏季 73 0.33 60 0.40 53 0.45
秋季 73 0.33 43 0.56 38 0.64
冬季 82 0.29 56 0.43 44 0.55
), ArticleFig(id=1322506425998360839, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506419316834508, language=CN, label=表1, caption=

各类要素的公共周期与对应频率

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时间 公共变化周期1/h cpd 公共变化周期2/h cpd 公共变化周期3/h cpd
春季 71 0.34 52 0.46 43 0.56
夏季 73 0.33 60 0.40 53 0.45
秋季 73 0.33 43 0.56 38 0.64
冬季 82 0.29 56 0.43 44 0.55
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基于FFT与ConvLSTM的ERA5水汽预测
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王勇 1 , 孟祥顺 1 , 杜玮 1 , 刘严萍 2 , 刘宇博 1
大地测量与地球动力学 | 现代大地测量 2025,45(10): 1033-1036
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大地测量与地球动力学 |现代大地测量 2025 , 45 (10) : 1033 -1036
基于FFT与ConvLSTM的ERA5水汽预测
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王勇1 , 孟祥顺1, 杜玮1, 刘严萍2, 刘宇博1
作者信息
  • 1 天津城建大学地质与测绘学院，天津市津静路26号, 300384
  • 2 天津城建大学经济与管理学院，天津市津静路26号, 300384
作者简介:

王勇，博士，教授，主要研究方向为GNSS气象学，E-mail:

ERA5 PWV Prediction Based on FFT and ConvLSTM
Yong WANG1 , Xiangshun MENG1, Wei DU1, Yanping LIU2, Yubo LIU1
Affiliations
  • 1 School of Geology and Geomatics, Tianjin Chengjian University, 26 Jinjing Road, Tianjin 300384, China
  • 2 School of Economics and Management, Tianjin Chengjian University, 26 Jinjing Road, Tianjin 300384, China

    • WANG Yong, PhD, professor, majors in GNSS meteorology, E-mail:

出版时间: 2025-10-15 doi: 10.14075/j.jgg.2024.11.504
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利用ERA5多要素数据集，综合FFT与ConvLSTM方法开展ERA5水汽预测。首先采用FFT提取各气象要素的公共周期；然后分别选择不同公共周期的多气象要素数据、单一水汽数据作为模型输入，使用ConvLSTM方法进行训练并构建ERA5水汽预测模型；最后以GNSS水汽、ERA5实测水汽分别开展ERA5点、面水汽的模型预测效果评价。结果表明，基于FFT与ConvLSTM的ERA5水汽预测方法可有效预测未来120 h的水汽，在单点和面空间范围内均有较高精度，可为短临降水预警提供数据基础。

水汽  /  快速傅里叶变换  /  卷积长短时记忆网络  /  ERA5  /  GNSS

This paper utilizes the ERA5 multi-element dataset to predict ERA5 water vapor by integrating the fast Fourier transform(FFT) and convolutional long short-term memory(ConvLSTM) methods. Firstly, the common periods of various meteorological elements are extracted using FFT. Subsequently, multiple meteorological elements with different common periods and individual water vapor data are selected as model inputs, and the ConvLSTM method is employed to train the ERA5 water vapor and construct the prediction model. Finally, the prediction performance of the model for point and spatial water vapor is evaluated using GNSS water vapor and actual ERA5 water vapor. The results indicate that the ERA5 water vapor prediction method based on FFT and ConvLSTM can effectively predict water vapor for the next 120 hours, with high accuracy in both single-point and spatial areas, providing a data foundation for short-term precipitation warnings.

PWV  /  FFT  /  ConvLSTM  /  ERA5  /  GNSS
王勇, 孟祥顺, 杜玮, 刘严萍, 刘宇博. 基于FFT与ConvLSTM的ERA5水汽预测. 大地测量与地球动力学, 2025 , 45 (10) : 1033 -1036 . DOI: 10.14075/j.jgg.2024.11.504
Yong WANG, Xiangshun MENG, Wei DU, Yanping LIU, Yubo LIU. ERA5 PWV Prediction Based on FFT and ConvLSTM[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (10) : 1033 -1036 . DOI: 10.14075/j.jgg.2024.11.504
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随着全球气候变化与城市化进程的加速，极端降水引起的洪涝灾害频发。自河南郑州“7.20”特大暴雨灾害后2 a，2023-07-28—08-02京津冀地区经历了一场严重的暴雨灾害，此次事件导致区域内388.86万人受灾，45人不幸遇难或失踪，直接经济损失高达958.11亿元[1]。水汽作为大气中重要的温室气体，在推动对流性天气系统的形成与演变方面起着关键作用，对降水、风暴和其他极端天气事件的准确预报具有重要意义[2]。使用GNSS观测值反演大气水汽，具有准实时、高精度、全天候、全球覆盖等优势。GNSS水汽数据已被应用于强降水与热带气旋等极端气象事件的短临预报，并取得了一定成果[3-7]。由于GNSS水汽空间分辨率受到地面站点的空间分布影响，且为单点观测；而ERA5水汽数据为栅格数据集，空间分辨率为0.25°×0.25°，在精度上与GNSS水汽保持一致，相关系数大于0.92，具有较好的应用前景[8]。但ERA5为事后处理数据集，其提供的水汽数据存在5 d(120 h)的时间滞后，使其在实时灾害性气象预警中无法及时提供相关数据。
为解决ERA5水汽数据滞后问题，本文综合考虑气温、气压、风速等气象要素，采用快速傅里叶记忆变换结合卷积长短时神经网络构建ERA5水汽预测模型，在保证预测精度的基础上，预测时效达到120 h，使得预测ERA5水汽满足实时性要求。以GNSS水汽、ERA5实际水汽为标准，开展ERA5水汽的预测精度检验与时效性分析，为气象预报和自然灾害预警提供数据基础。
1 研究区域与研究数据
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1.1 研究区域
研究区域为中国西南地区，范围为102.16°~112.61°E、30.01°~34.04°N。该区域西邻川西高原，北邻秦岭山脉，包含部分四川盆地，分布有高山、丘陵等地形(图 1)。气候上属于亚热带气候，四季变化明显，冬季温和干燥，夏季温暖湿润，降水集中。
1.2 研究数据
1.2.1 ERA5再分析数据
研究使用数据集为ERA5 1940年至今的单层逐小时数据集，使用气象要素包括水汽、地表与距地表 2 m处气温、地表气压及距地表 10 m与100 m处风速。时间范围为2019-03-01—2021-02-29，水平分辨率为0.25°×0.25°，时间分辨率为1 h，其中水汽单位为mm，气温单位为K，气压单位为Pa，风速单位为m/s。
1.2.2 GNSS水汽
将GNSS水汽用于ERA5水汽预测效果评价，研究区涉及3个CMONOC GNSS站点，数据时间为2020-08-27—29共72 h。GNSS观测数据解算使用GAMIT10.61软件，每小时估算一个ZTD。结合气象要素解算获得GNSS测站水汽[6]。
2 基于FFT的水汽与气象要素的公共周期提取
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Liu等[9]研究表明，水汽与其他气象要素(气温、气压、风速)关联密切。选取地表气温、2 m气温、地表气压、10 m U型风和100 m U型风作为ERA5水汽预测模型输入要素，参与模型构建。将水汽与气温、气压、风速等气象要素的时间序列作为时域信号，通过快速傅里叶变换(FFT)方法，把时域信号转换为频域信号，通过频域分析提取各要素的公共周期。气象要素特别是水汽存在明显的季节变化，将ERA5数据按照季节细分，利用FFT方法分别处理2019-03-01—2020-02-29各季节与全年的ERA5数据。选择不同要素波峰相近处频率，用24除以各频率计算相对应的时间周期，结果如表 1所示，表中1 cpd为24 h对应的频率。
3 融合气温、气压、风速的ConvLSTM水汽预测
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在数据输入时对要素进行归一化处理，减少数值间大小差异的影响。使用2019年全年的气象要素数据，春、夏季各为2 208 h，秋、冬季各为2 184 h。将全年与各季度全部输入数据的80%指定为训练集，余下20%数据用于计算每次迭代的训练损失，指定损失率小于0.001时模型达到最优性能。每次输入一个固定周期长度的数据，对所有气象要素进行未来1 h的预测，并将各要素逐小时的预测值作为下一周期的输入数据。将水汽作为标签数据输出，并在120 h的每一个时点上使用这些要素对ERA5水汽进行优化，最终获得120 h的预测值。
为实现120 h的预测时长并满足气象灾害预报的精度要求，综合考虑全年数据与各季节数据作为训练数据、ERA5单一水汽与多种大气要素作为输入要素及数据间不同的公共周期。在使用2020年ERA5水汽数据进行验证时，数据时长为公共周期与120 h预测时长之和，考虑到GNSS数据存在缺失，使用数据时长为公共周期外延72 h。通过使用2019年数据训练多个预测模型，以2020年数据进行外符合精度验证。
3.1 水汽预测的单点精度评价
在研究区按照GNSS站与不同地形选取采样点(图 1)，提取2020-08-27—31期间120 h内的多要素与单要素全年模型及季节模型的预测结果。两者全年模型的公共周期为40 h，多要素与单要素季节模型公共周期分别为43 h与73 h，并提取采样点处的水汽值。不同模型逐小时预测结果与GNSS站点水汽值及ERA5水汽值的对比见图 2。
由图 2可知，单要素模型的预测精度较差，尤其是在复杂地形下无法准确反映水汽的细微变化。多要素全年模型在所有GNSS站点与地形采样点最大RMSE分别为SNMX站与平原的7.1 mm与3.9 mm，所有GNSS站点与地形采样点平均RMSE为4.6 mm与2.2 mm。多要素季节模型最大RMSE也出现在SNMX站与平原，分别为7.5 mm与3.2 mm，GNSS站与地形采样点的平均RMSE为4.6 mm与1.6 mm。
3.2 水汽预测的空间精度
分析空间精度时采用多要素预测结果，图 3为多要素两种模型在研究区域内水汽预测的空间分布与实际水汽的比较，在预测范围内每10 h选取一个预测结果与真实值相减得到差值，时间范围为夏季8月份最后5 d。由图 3可知，季节模型优于全年模型。在整个时段中，季节模型对低水汽值区域的预测更准确，全年模型在研究区西部青藏高原与中部秦岭山脉的低水汽区域预测值始终偏高。对于西南方水汽值较高的盆地区域，季节模型能更好地反映高值区随时间的扩展。
4 结语
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1) 运用FFT获取了研究区域内水汽、气温、气压与风速等要素的公共变化周期。采用多种大气要素进行训练的预测模型在研究区内各地形的单点预测值精度优于采用单一要素进行训练的模型；综合考虑全年模型与季节模型在采样点下的最大及平均RMSE，两者在预测精度上相近，均能取得较好的预测效果。
2) 针对120 h水汽预测，多要素全年模型在春、秋季与季节模型效果相近，而在水汽显著变化的夏、冬季，多要素季节模型表现最佳，有效填补了ERA5数据的空缺。
下一步计划扩大研究区域范围，以证明模型在不同气候与地形条件下的预测性能，并加入ERA5数据外的其他类型数据，继续提高预测模型的精度。
基金
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  • 天津市科技计划(21KPHDRC00070)
  • 国家级大学生创新创业训练计划(202310792005)
文献
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2025年第45卷第10期
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doi: 10.14075/j.jgg.2024.11.504
  • 接收时间：2024-11-05
  • 首发时间：2026-10-29
  • 出版时间：2025-10-15
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  • 收稿日期：2024-11-05
基金
Science and Technology Planning of Tianjin Municipality(21KPHDRC00070)
天津市科技计划(21KPHDRC00070)
National College Students' Innovation and Entrepreneurship Training Program(202310792005)
国家级大学生创新创业训练计划(202310792005)
作者信息
    1 天津城建大学地质与测绘学院，天津市津静路26号, 300384
    2 天津城建大学经济与管理学院，天津市津静路26号, 300384
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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
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