GNSS水汽层析关键参量普适性确定方法

doi:10.11947/j.AGCS.2025.20240293

测绘学报 ›› 2025, Vol. 54 ›› Issue (3): 410-421.doi: 10.11947/j.AGCS.2025.20240293

GNSS水汽层析关键参量普适性确定方法

赵庆志¹(), 蒋朵朵¹, 郭宏武²^,³, 李祖锋⁴, 刘晨⁵^,⁶, 姚宜斌⁷

^1.西安科技大学测绘科学与技术学院，陕西　西安　710054
^2.中国气象局城市气象重点开放实验室，北京　100081
^3.西安市气象局，陕西　西安　716000
^4.中国电建集团西北勘测设计研究院有限公司，陕西　西安　710065
^5.中国铁路设计集团，天津　300251
^6.天津市轨道交通导航定位及时空大数据技术重点实验室，天津　300251
^7.武汉大学测绘学院，湖北　武汉　430079

收稿日期:2024-07-16 出版日期:2025-04-11 发布日期:2025-04-11
作者简介:赵庆志（1989—），男，博士，教授，研究方向为GNSS数据处理与GNSS气象学研究。　E-mail：zhaoqingzhia@163.com
基金资助:
国家自然科学基金(42274039);中国气象局城市气象重点开放实验室开放基金(LUM-2023-17);天津市轨道交通导航定位及时空大数据技术重点实验室开放课题(TKL2024B03)

A general method for determining the key parameters of GNSS water vapor tomography modeling

Qingzhi ZHAO¹(), Duoduo JIANG¹, Hongwu GUO²^,³, Zufeng LI⁴, Chen LIU⁵^,⁶, Yibin YAO⁷

^1.College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
^2.Key Open Laboratory of Urban Meteorology, China Meteorological Administration, Beijing 100081, China
^3.Meteorological Bureau of Xi'an, Xi'an 716000, China
^4.China Power Construction Northwest Survey and Design Institute Co., Ltd., Xi'an 710065, China
^5.China Railway Design Corporation, Tianjin 300251, China
^6.Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology, Tianjin 300251, China
^7.School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received:2024-07-16 Online:2025-04-11 Published:2025-04-11
About author:ZHAO Qingzhi (1989—), male, PhD, professor, majors in GNSS data processing and GNSS meteorology.　E-mail: zhaoqingzhia@163.com
Supported by:
The National Natural Science Foundation of China(42274039);Open Fund for the Key Open Laboratory of Urban Meteorology of the China Meteorologioal Administration(LUM-2023-17);Tianjin Key Laboratory of Rail Transit Navigation, Positioning and Spatio-temporal Big Data Technology(TKL2024B03)

摘要/Abstract

摘要：

现有GNSS水汽层析在层析高度、垂直分层、水平步长等关键参量确定方面多为经验选取，缺乏普适性确定方法，导致层析结果差异较大、难以实现普适性水汽层析结果的落地应用。针对该现状，本文提出GNSS水汽层析关键参量普适性确定理论方法，解决水汽层析过程中最优建模参量无法确定的难题。首先，提出联合垂直分层廓线资料与水汽密度阈值的层析区域最优高度确定原则；然后，提出垂向分层水汽等权原则的最优垂直分辨率确定方法；最后，综合考虑测站密度和卫星截止高度角等信息，发展联合网格覆盖率最大化及非均匀对称水平网格划分思想的最优水平步长确定方法。选取香港区域2013年5月1—14日共14 d 12个GNSS测站及1个无线电探空站的数据进行试验。与现有经典方法对比，以无线电探空数据为真值，发现本文方法反演水汽密度廓线精度的平均改善率为12%；以欧洲中期天气预报中心（ECMWF）发布的第五代再分析数据集（ERA5）计算的斜路径水汽含量（SWV）为真值，本文方法反演得到SWV精度的平均改善率为29.5%。

关键词: 全球导航卫星系统, 水汽层析, 关键参量, 普适性方法

Abstract:

The existing global navigation satellite system (GNSS) water vapor tomography is mostly empirically selected in the determination of key parameters such as tomography height, vertical stratification, horizontal step size, etc., and lacks a general determination method, resulting in large differences in tomography results and difficulty in realizing the application of general water vapor tomography results. Because of this situation, this study proposes a theoretical method for determining the generality of key parameters of GNSS water vapor tomography to solve the problem that the optimal modeling parameters cannot be determined in the process of water vapor tomography. Firstly, the principle of determining the optimal height of the tomographic region by combining the vertical layered profile data and the water vapor density threshold is proposed. Secondly, the optimal vertical resolution determination method based on the equal weight principle of vertical stratified water vapor is proposed. Thirdly, considering the information on station density and satellite cut-off elevation angle, the optimal horizontal step size determination method based on the idea of joint grid coverage maximization and non-uniform symmetric horizontal grid division is developed. The data of 12 GNSS stations and 1 radiosonde station in Hong Kong from May 1 to 14, 2013 were selected for experiments. Compared with the existing classical methods, compared with radiosonde data, the average improvement rate of the retrieved water vapor density profile is 12%. Compared with the slant water vapor (SWV) calculated by the fifth-generation reanalysis dataset (ERA5) released by the European Centre for medium range weather forecasts (ECMWF), the average improvement rate of SWV obtained by the proposed scheme is 29.5%.

Key words: GNSS, water vapor tomography, key parameters, general method

中图分类号:

P228

赵庆志, 蒋朵朵, 郭宏武, 李祖锋, 刘晨, 姚宜斌. GNSS水汽层析关键参量普适性确定方法[J]. 测绘学报, 2025, 54(3): 410-421.

Qingzhi ZHAO, Duoduo JIANG, Hongwu GUO, Zufeng LI, Chen LIU, Yibin YAO. A general method for determining the key parameters of GNSS water vapor tomography modeling[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(3): 410-421.

图/表 8

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参考文献 42

[1]	张克非, 李浩博, 王晓明, 等. 地基GNSS大气水汽探测遥感研究进展和展望[J]. 测绘学报, 2022, 51(7): 1172-1191. DOI:. doi: 10.11947/j.AGCS.2022.20220149
	ZHANG Kefei, LI Haobo, WANG Xiaoming, et al. Recent progresses and future prospectives of ground-based GNSS water vapor sounding[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1172-1191. DOI:. doi: 10.11947/j.AGCS.2022.20220149
[2]	MERRIKHPOUR M H, RAHIMZADEGAN M. Analysis of temporal and spatial variations of total precipitable water vapor in western Iran using radiosonde and MODIS measurements[J]. Journal of Applied Remote Sensing, 2019, 13(4): 044508.
[3]	ZHAO Qingzhi, YAO Yibin, YAO Wanqiang. Capturing the signature of heavy rainfall events using the 2-d-/ 4-d water vapour information derived from GNSS measurement in Hong Kong[J/OL]. [2024-07-01]. https://doi.org/10.5194/angeo-2018-76.
[4]	TRZCINA E, ROHM W. Estimation of 3D wet refractivity by tomography, combining GNSS and NWP data: first results from assimilation of wet refractivity into NWP[J]. Quarterly Journal of the Royal Meteorological Society, 2019, 145(720): 1034-1051.
[5]	BRAUN J, ROCKEN C, MEERTENS C, et al. Development of a water vapor tomography system using low cost L1 GPS receivers[C]//Proceedings of the 9th ARM Science Team Meeting. San Antonio: NSF, 1999.
[6]	FLORES A, RUFFINI G, RIUS A. 4D tropospheric tomography using GPS slant wet delays[J]. Annales Geophysicae, 2000, 18(2): 223-234.
[7]	PERLER D, GEIGER A, HURTER F. 4D GPS water vapor tomography: new parameterized approaches[J]. Journal of Geodesy, 2011, 85(8): 539-550.
[8]	ZHAO Qingzhi, YAO Yibin, YAO Wanqiang. Troposphere water vapour tomography: a horizontal parameterised approach[J]. Remote Sensing, 2018, 10(8): 1241.
[9]	ZHANG Wenyuan, ZHANG Shubi, MOELLER G, et al. An adaptive-degree layered function-based method to GNSS tropospheric tomography[J]. GPS Solutions, 2023, 27(2): 67.
[10]	BENEVIDES P, CATALÃO J, MIRANDA P. Experimental GNSS tomography study in Lisbon (Portugal)[J]. Física de la Tierra, 2014, 26: 65-79.
[11]	AO Yibin, ZHAO Qingzhi, ZHANG Bao. A method to improve the utilization of GNSS observation for water vapor tomography[J]. Annales Geophysicae, 2016, 34(1): 143-152.
[12]	YAO Yibin, ZHAO Qingzhi. Maximally using GPS observation for water vapor tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 7185-7196.
[13]	胡鹏, 黄观文, 张勤, 等. 顾及边界入射信号的多模水汽层析方法[J]. 测绘学报, 2020, 49(5): 557-568. DOI:. doi: 10.11947/j.AGCS.2020.20190113
	HU Peng, HUANG Guanwen, ZHANG Qin, et al. A multi-GNSS water vapor tomography method considering boundary incident signals[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(5): 557-568. DOI:. doi: 10.11947/j.AGCS.2020.20190113
[14]	赵庆志, 姚宜斌, 罗亦泳. 附加辅助层析区域提高射线利用率的水汽反演方法[J]. 武汉大学学报(信息科学版), 2017, 42(9): 1203-1208,1222.
	ZHAO Qingzhi, YAO Yibin, LUO Yiyong. A method to improve the utilization of observation for water vapor tomography by adding assisted tomographic area[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9): 1203-1208,1222.
[15]	赵庆志, 姚宜斌, 姚顽强. 顾及层析区域外测站的GNSS水汽层析建模方法[J]. 测绘学报, 2021, 50(3): 285-294. DOI:. doi: 10.11947/j.AGCS.2021.20200111
	ZHAO Qingzhi, YAO Yibin, YAO Wanqiang. A method to establish the tomography model considering the data of GNSS stations outside the research area[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3): 285-294. DOI:. doi: 10.11947/j.AGCS.2021.20200111
[16]	LIU Shangyi, ZHANG Kefei, WU Suqin, et al. An improved GNSS tropospheric tomographic model with an extended region and combining virtual signals[J]. Atmospheric Research, 2023, 287: 106703.
[17]	SHANGGUAN Ming, DANG Meng, CHENG Xu. Multi-source water vapor tomography based on ray-tracing technique[C]//Proceedings of 2023 EGU General Assembly Conference Abstracts. Vienna: EGU, 2023.
[18]	宋淑丽, 朱文耀, 丁金才, 等. 上海GPS综合应用网对可降水汽量的实时监测及其改进数值预报初始场的试验[J]. 地球物理学报, 2004, 47(4): 631-638.
	SONG Shuli, ZHU Wenyao, DING Jincai, et al. Real time monitoring of PWV from SGCAN and its application test in numerical weather forecast[J]. Chinese Journal of Geophysics, 2004, 47(4): 631-638.
[19]	于胜杰, 柳林涛. 利用选权拟合法进行GPS水汽层析解算[J]. 武汉大学学报(信息科学版), 2012, 37(2): 183-186.
	YU Shengjie, LIU Lintao. Application of fitting method by selection of the parameter weights on GPS water vapor tomography[J]. Geomatics and Information Science of Wuhan University, 2012, 37(2): 183-186.
[20]	GUO Jiming, YANG Fei, SHI Junbo, et al. An optimal weighting method of global positioning system (GPS) troposphere tomography[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(12): 5880-5887.
[21]	ZHANG Bao, FAN Qingbiao, YAO Yibin, et al. An improved tomography approach based on adaptive smoothing and ground meteorological observations[J]. Remote Sensing, 2017, 9(9): 886.
[22]	ZHAO Qingzhi, YAO Yibin, YAO Wanqiang. A troposphere tomography method considering the weighting of input information[J]. Annales Geophysicae, 2017, 35(6): 1327-1340.
[23]	CAO Yunchang, CHEN Yongqi, LI Pingwha. Wet refractivity tomography with an improved Kalman-filter method[J]. Advances in Atmospheric Sciences, 2006, 23(5): 693-699.
[24]	HIRAHARA K. Local GPS tropospheric tomography[J]. Earth, Planets and Space, 2000, 52(11): 935-939.
[25]	ZHAO Qingzhi, LI Zufeng, YAO Wanqiang, et al. An improved ridge estimation (IRE) method for troposphere water vapor tomography[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2020, 207: 105366.
[26]	YANG Fei, GUO Jiming, SHI Junbo, et al. A GPS water vapour tomography method based on a genetic algorithm[J]. Atmospheric Measurement Techniques, 2020, 13(1): 355-371.
[27]	SORKHABI O M, DJAMOUR Y. 4D modeling of precipitable water vapor to assess flood forecasting by using GPS signals[J]. Natural Hazards, 2024, 120(1): 181-195.
[28]	王维, 王解先. 基于代数重构技术的对流层水汽层析[J]. 计算机应用, 2011, 31(11): 3149-3151.
	WANG Wei, WANG Jiexian. Ground-based GPS water vapor tomography based on algebraic reconstruction technique[J]. Journal of Computer Applications, 2011, 31(11): 3149-3151.
[29]	ZHANG Weixing, LOU Yidong, LIU Wenxuan, et al. Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique[J]. Journal of Geodesy, 2020, 94(8): 76.
[30]	张文渊, 张书毕, 左都美, 等. GNSS水汽层析的自适应代数重构算法[J]. 武汉大学学报(信息科学版), 2021, 46(9): 1318-1327.
	ZHANG Wenyuan, ZHANG Shubi, ZUO Dumei, et al. Adaptive algebraic reconstruction algorithms for GNSS water vapor tomography[J]. Geomatics and Information Science of Wuhan University, 2021, 46(9): 1318-1327.
[31]	LIU Shangyi, ZHANG Kefei, WU Suqin, et al. A two-step projected iterative algorithm for tropospheric water vapor tomography[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 5999-6015.
[32]	夏朋飞, 叶世榕. 一种基于组合重构算法的对流层层析技术[J]. 大地测量与地球动力学, 2017, 37(9): 928-932.
	XIA Pengfei, YE Shirong. A troposphere tomography technique based on combined reconstruction algorithm[J]. Journal of Geodesy and Geodynamics, 2017, 37(9): 928-932.
[33]	TROLLER M, BÜRKI B, COCARD M, et al. 3D refractivity field from GPS double difference tomography[J]. Geophysical Research Letters, 2002, 29(24): 2149.
[34]	赵庆志, 姚宜斌, 辛林洋. 融合ECMWF格网数据的水汽层析精化方法[J]. 武汉大学学报(信息科学版), 2021, 46(8): 1131-1138.
	ZHAO Qingzhi, YAO Yibin, XIN Linyang. A method to sophisticate the water vapor tomography model by combining the ECMWF grid data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1131-1138.
[35]	MIRANDA P M A, MATEUS P. Improved GNSS water vapor tomography with modified mapping functions[J]. Geophysical Research Letters, 2022, 49(18): e2022GL100140.
[36]	ZHAO Qingzhi, YAO Yibin, YAO Wanqiang, et al. An optimal tropospheric tomography approach with the support of an auxiliary area[J]. Annales Geophysicae, 2018, 36(4): 1037-1046.
[37]	王维, 宋淑丽, 王解先, 等. 长三角地区多模GNSS斜路径观测分布及水汽仿真层析[J]. 测绘学报, 2016, 45(2): 164-169,177. DOI:. doi: 10.11947/j.AGCS.2016.20140648
	WANG Wei, SONG Shuli, WANG Jiexian, et al. Distribution analysis of multi GNSS slant delays and simulated water vapor tomography in Yangtze River Delta[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(2): 164-169,177. DOI:. doi: 10.11947/j.AGCS.2016.20140648
[38]	张文渊, 张书毕, 郑南山, 等. 联合GNSS/RS多源数据反演三维大气水汽分布研究[J]. 地球物理学报, 2022, 65(6): 1951-1964.
	ZHANG Wenyuan, ZHANG Shubi, ZHENG Nanshan, et al. Study on the retrieval of 3D atmospheric water vapor distribution using GNSS and RS multi-source data[J]. Chinese Journal of Geophysics, 2022, 65(6): 1951-1964.
[39]	姚秀光, 郭金城, 严梦琪, 等. 基于地基GNSS观测数据的贵州高原地区水汽层析精度分析[J]. 大地测量与地球动力学, 2023, 43(11): 1162-1166.
	YAO Xiuguang, GUO Jincheng, YAN Mengqi, et al. Accuracy analysis of water vapor tomography based on ground-based GNSS observation data in Guizhou Plateau[J]. Journal of Geodesy and Geodynamics, 2023, 43(11): 1162-1166.
[40]	赵庆志. 地基GNSS水汽反演关键技术研究及其应用[D]. 武汉: 武汉大学, 2017.
	ZHAO Qingzhi. Studies on the key technologies in water vapor inversion using ground-based GNSS and its applications[D]. Wuhan: Wuhan University, 2017.
[41]	CHEN Biyan, LIU Zhizhao. Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model[J]. Journal of Geodesy, 2014, 88(7): 691-703.
[42]	CHAROENPHON C, SATIRAPOD C. Improving the accuracy of real-time precipitable water vapour using country-wide meteorological model with precise point positioning in Thailand[J]. Journal of Spatial Science, 2022, 67(2): 313-329.

方案	层析高度/km	垂直分层（由低到高）/km	经纬度方向水平步长/（°）
方案T1^[13]	9.93	0.993、0.993、0.993、0.993、0.993、0.993、0.993、0.993、0.993、0.993	经度：0.06、0.06、0.06、0.06、0.06、0.06、0.06、0.06 纬度：0.06、0.06、0.06、0.06、0.06、0.06
方案T2^[41]	8.50	0.4、0.4、0.4、0.4、0.4、0.5、0.5、0.5、0.5、0.6、0.6、0.6、0.7、1.0、1.0	经度：0.06、0.06、0.06、0.06、0.06、0.06、0.06、0.06 纬度：0.06、0.06、0.06、0.06、0.06、0.06
方案T3^[12]	8.00	0.5、0.5、0.7、0.7、0.7、0.9、0.9、0.9、1.1、1.1	经度：0.06、0.06、0.06、0.06、0.06、0.06、0.06、0.06 纬度：0.06、0.06、0.06、0.06、0.06、0.06
方案G1	9.93	0.26、0.28、0.32、0.35、0.40、0.46、0.57、0.79、1.37、5.13	经度：0.08、0.07、0.05、0.04、0.04、0.05、0.07、0.08 纬度：0.09、0.06、0.03、0.03、0.06、0.09
方案G2	9.93	0.26、0.28、0.32、0.35、0.40、0.46、0.57、0.79、1.37、5.13	经度：0.08、0.07、0.05、0.04、0.04、0.05、0.07、0.08 纬度：0.1、0.05、0.03、0.03、0.05、0.1
方案G3	9.93	0.26、0.28、0.32、0.35、0.40、0.46、0.57、0.79、1.37、5.13	经度：0.09、0.06、0.05、0.04、0.04、0.05、0.06、0.09 纬度：0.09、0.06、0.03、0.03、0.06、0.09
方案G4	9.93	0.26、0.28、0.32、0.35、0.40、0.46、0.57、0.79、1.37、5.13	经度：0.09、0.06、0.05、0.04、0.04、0.05、0.06、0.09 纬度：0.1、0.05、0.03、0.03、0.05、0.1

GNSS水汽层析关键参量普适性确定方法

A general method for determining the key parameters of GNSS water vapor tomography modeling

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