顾及水汽分布的非均匀离散化GNSS水汽层析精化方法

doi:10.11947/j.AGCS.2024.20220534

测绘学报 ›› 2024, Vol. 53 ›› Issue (12): 2282-2294.doi: 10.11947/j.AGCS.2024.20220534

顾及水汽分布的非均匀离散化GNSS水汽层析精化方法

张文渊¹^,²(), 戚铭心³(), 张书毕¹^,²

^1.中国矿业大学环境与测绘学院，江苏　徐州　221116
^2.中国矿业大学自然资源部国土环境与灾害监测重点实验室，江苏　徐州　221116
^3.国家知识产权局专利局专利审查协作江苏中心，江苏　苏州　215163

收稿日期:2022-09-06 出版日期:2025-01-06 发布日期:2025-01-06
通讯作者: 戚铭心 E-mail:zhangwy@cumt.edu.cn;1169680702@qq.com
作者简介:张文渊（1996—），男，博士，副教授，研究方向为GNSS/RS弹性融合水汽探测及气候变化应用。E-mail：zhangwy@cumt.edu.cn
基金资助:
国家自然科学基金(42404016);江苏省自然科学基金(BK20241669);中央高校基本科研业务费专项资金(2024QN11077)

A non-uniform discretization GNSS water vapor tomography refined method considering water vapor distributions

Wenyuan ZHANG¹^,²(), Mingxin QI³(), Shubi ZHANG¹^,²

^1.School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
^2.MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China
^3.Patent Examination Cooperation (Jiangsu) Center of the Patent Office, CNIPA, Suzhou 215163, China

Received:2022-09-06 Online:2025-01-06 Published:2025-01-06
Contact: Mingxin QI E-mail:zhangwy@cumt.edu.cn;1169680702@qq.com
About author:ZHANG Wenyuan (1996—), male, PhD, associate professor, majors in resilient fusion of GNSS/RS for water vapor monitoring and climate change application. E-mail: zhangwy@cumt.edu.cn
Supported by:
The National Natural Science Foundation of China(42404016);The Natural Science Foundation of Jiangsu Province(BK20241669);Fundamental Research Funds for the Central Universities(2024QN11077)

摘要/Abstract

摘要：

GNSS水汽层析技术凭借其高精度、全天候的探测优势，成为反演高时空大气水汽分布的重要手段。现有GNSS水汽层析模型通常采用均匀离散化方式对三维层析区域进行划分，但由于大气水汽的空间异质性，均匀离散化方法并不符合大气水汽在垂直方向的真实分布特点。本文充分考虑大气水汽的垂直变化规律，提出了一种顾及水汽分布的非均匀离散化GNSS水汽层析精化方法。该方法根据水汽含量的垂直递减特征，构造了基于可降水量变化率的垂直非均匀分层方法，并在不同高度层设置了水平非均匀离散化方案，形成了从地表到对流层顶体素分辨率逐渐减小的非均匀离散化水汽层析模型框架。基于2017年7月香港地区的GNSS实测数据、探空数据和ERA5再分析资料开展了水汽层析试验，结果表明：以探空水汽廓线为参考值，非均匀离散化方法的均方根误差相较于传统方法的3种方案分别降低了21.8%、20.9%、20.5%；以ERA5三维水汽数据为参考值，本文方法对应的整个区域层析结果的RMSE值分别降低了15.4%、11.4%、12.6%。此外，在2 km以下的近地区域，本文方法的层析结果精度也要明显优于传统方法，有望为降雨预报提供更高精度和更高分辨率的近地表三维大气水汽产品。

关键词: GNSS水汽层析, 水平非均匀离散化, 垂直非均匀分层, 探空数据, ERA5

Abstract:

GNSS water vapor tomography technique has become a crucial tool for retrieving atmospheric water vapor distributions with high spatiotemporal resolution, owing to its high precision and all-weather availability. The existing GNSS water vapor tomography method divides the three-dimensional (3D) tomography area with a uniform discretization scheme. However, due to the spatial heterogeneity of atmospheric water vapor, this method does not follow the actual distribution of atmospheric water vapor in the vertical direction. Based on the vertical decreasing tendency of atmospheric water vapor, an improved non-uniform discretized GNSS water vapor tomography method that considers water vapor distributions is proposed. The method analyzes the vertical decreasing characteristics of water vapor content and constructs a vertically non-uniform stratification scheme based on the change rate of precipitable water vapor. Furthermore, a horizontal non-uniform discretization scheme at different altitude layers is set up, forming an uneven discretization tomography framework with the decreasing resolution voxels from the surface to the top of the tomography area. Experiments are conducted using actual GNSS measurements, radiosonde data and ERA5 reanalysis in the Hong Kong region in July 2017. Taking radiosonde water vapor profiles as reference, the root mean square errors (RMSE) of the tomography results obtained from the non-uniform discretization approach are reduced by 21.8%, 20.9%, and 20.5% against three traditional schemes, respectively. Compared with ERA5 data, the RMSE values of the proposed method's tomography results are reduced by 15.4%, 11.4%, and 12.6%, respectively. Additionally, in the near-surface tomographic region below 2 km, the accuracy of the tomographic results obtained by the proposed method is significantly superior to that of the traditional method, which highlights that the proposed method is expected to provide higher accuracy and higher resolution near-surface 3D atmospheric water vapor products for rainfall forecasting.

Key words: GNSS water vapor tomography, horizontal non-uniform discretization, vertical non-uniform stratification, radiosonde, ERA5

中图分类号:

P228

张文渊, 戚铭心, 张书毕. 顾及水汽分布的非均匀离散化GNSS水汽层析精化方法[J]. 测绘学报, 2024, 53(12): 2282-2294.

Wenyuan ZHANG, Mingxin QI, Shubi ZHANG. A non-uniform discretization GNSS water vapor tomography refined method considering water vapor distributions[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(12): 2282-2294.

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参考文献 27

[1]	王晓英. 地基GNSS层析对流层水汽若干关键技术研究[D]. 南京: 南京信息工程大学, 2013.
	WANG Xiaoying. Research on some key technologies of ground-based GNSS tomography of tropospheric water vapor[D]. Nanjing: Nanjing University of Information Science & Technology, 2013.
[2]	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. 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. DOI:. doi: 10.11947/j.AGCS.2016.20140648
[3]	夏朋飞. 联合地基GPS及空基COSMIC的对流层水汽三维层析[D]. 长沙: 中南大学, 2013.
	XIA Pengfei. Three-dimensional tomography of tropospheric water vapor combined with ground-based GPS and space-based COSMIC[D]. Changsha: Central South University, 2013.
[4]	DONG Zhounan, JIN Shuanggen. 3D water vapor tomography in Wuhan from GPS, BDS and GLONASS observations[J]. Remote Sensing, 2018, 10(1): 62.
[5]	赵庆志, 姚宜斌, 姚顽强, 等. 利用ECMWF改善射线利用率的三维水汽层析算法[J]. 测绘学报, 2018, 47(9): 1179-1187. DOI:. doi: 10.11947/j.AGCS.2018.20170412
	ZHAO Qingzhi, YAO Yibin, YAO Wanqiang, et al. A method to improve the utilization rate of satellite rays for three-dimensional water vapor tomography using the ECMWF data[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(9): 1179-1187. DOI:. doi: 10.11947/j.AGCS.2018.20170412
[6]	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.
[7]	姚宜斌, 赵庆志, 罗亦泳. 附加虚拟信号精化水汽层析模型的方法[J]. 武汉大学学报(信息科学版), 2017, 42(11): 1658-1664.
	YAO Yibin, ZHAO Qingzhi, LUO Yiyong. An approach of imposing virtual signals to sophisticate water vapor tomographic model[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11): 1658-1664.
[8]	张文渊, 张书毕, 郑南山, 等. GNSS/MODIS信号紧耦合水汽层析算法[J]. 测绘学报, 2021, 50(4): 496-508. DOI:. doi: 10.11947/J.AGCS.2021.20200222
	ZHANG Wenyuan, ZHANG Shubi, ZHENG Nanshan, et al. Tightly coupled water vapor tomography algorithm for combining GNSS and MODIS signals[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(4): 496-508. DOI:. doi: 10.11947/j.AGCS.2021.20200222
[9]	ZHANG Wenyuan, ZHANG Shubi, DING Nan, et al. A tropospheric tomography method with a novel height factor model including two parts: isotropic and anisotropic height factors[J]. Remote Sensing, 2020, 12(11): 1848.
[10]	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, 65(6): 1951-1964.
	ZHANG Wenyuan, ZHANG Shubi, ZHENG Nanshan, et al. 2022. Study on the retrieval of 3D atmospheric water vapor distribution using GNSS and RS multi-source data[J]. Chinese Journal of Geophysics(in Chinese), 65(6): 1951-1964.
[11]	ZHANG Wenyuan, ZHANG Shubi, DING Nan, et al. GNSS-RS tomography: retrieval of tropospheric water vapor fields using GNSS and RS observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 3077083.
[12]	ZHANG Wenyuan, ZHANG Shubi, CHANG Guobin, et al. A new hybrid observation GNSS tomography method combining the real and virtual inverted signals[J]. Journal of Geodesy, 2021, 95(12): 128.
[13]	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
[14]	陈宏斌, 熊永良, 陈志胜, 等. 垂直不均匀分层的地基GPS层析水汽研究[J]. 测绘工程, 2015, 24(5): 11-14, 18.
	CHEN Hongbin, XIONG Yongliang, CHEN Zhisheng, et al. Research on tomography of ground-based GPS water vapor with uneven vertical stratification[J]. Engineering of Surveying and Mapping, 2015, 24(5): 11-14,18.
[15]	范士杰, 陈岩, 彭秀英, 等. 地基GNSS水汽层析的自动垂直非均匀分层方法[J]. 大地测量与地球动力学, 2021, 41(9): 924-928.
	FAN Shijie, CHEN Yan, PENG Xiuying, et al. Automatic vertical non-uniform stratification method for GNSS water vapor tomography[J]. Journal of Geodesy and Geodynamics, 2021, 41(9): 924-928.
[16]	王昊, 丁楠, 张文渊, 等. GNSS水汽层析的自适应非均匀指数分层方法[J]. 测绘学报, 2022, 51(3): 327-339. DOI:. doi: 10.11947/J.AGCS.2022.20210126
	WANG Hao, DING Nan, ZHANG Wenyuan, et al. An adaptive non-uniform vertical stratification for GNSS water vapor tomography[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(3): 327-339. DOI:. doi: 10.11947/J.AGCS.2022.20210126
[17]	YAO Yibin, ZHAO Qingzhi. A novel, optimized approach of voxel division for water vapor tomography[J]. Meteorology and Atmospheric Physics, 2017, 129(1): 57-70.
[18]	YAO Yao, SUN Sun, XU Chaoqian. Applicability of Bevis formula at different height levels and global weighted mean temperature model based on near-earth atmospheric temperature[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(1): 1-11.
[19]	丁楠. 地基GNSS水汽层析关键技术研究[D]. 徐州: 中国矿业大学, 2018.
	DING Nan. Research on key technologies of ground-based GNSS water vapor chromatography[D]. Xuzhou: China University of Mining and Technology, 2018.
[20]	赵庆志, 苏静, 杨鹏飞, 等. 利用GNSS PWV的AOD自适应预测方法[J]. 测绘学报, 2021, 50(10): 1279-1289. DOI:. doi: 10.11947/J.AGCS.2021.20210052
	ZHAO Qingzhi, SU Jing, YANG Pengfei, et al. AOD adaptive prediction method based on GNSS PWV[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10): 1279-1289. DOI:. doi: 10.11947/J.AGCS.2021.20210052
[21]	赵庆志, 杜正, 姚顽强, 等. GNSS约束的MERSI/FY-3A PWV校准方法[J]. 测绘学报, 2022, 51(2): 159-168. DOI:. doi: 10.11947/j.AGCS.2022.20210060
	ZHAO Qingzhi, DU Zheng, YAO Wanqiang, et al. The MERSI/FY-3A PWV correction method based on GNSS[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 159-168. DOI:. doi: 10.11947/j.AGCS.2022.20210060
[22]	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.
[23]	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.
[24]	于胜杰, 柳林涛, 梁星辉. 约束条件对GPS水汽层析解算的影响分析[J]. 测绘学报, 2010, 39(5): 491-496.
	YU Shengjie, LIU Lintao, LIANG Xinghui. Influence analysis of constraint conditions on GPS water vapor tomography[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(5): 491-496.
[25]	WANG Yizhu, LIU Hailei, ZHANG Yong, et al. Validation of FY-4A AGRI layer precipitable water products using radiosonde data[J]. Atmospheric Research, 2021, 253: 105502.
[26]	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.
[27]	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.

高度/km	PWV值/mm	占比/（%）
0.25	5.25	10
0.55	11.02	20
0.85	16.20	30
1.20	21.48	40
1.65	27.43	50
2.10	32.54	60
2.70	37.93	70
3.50	43.30	80
4.80	48.62	90
5.90	51.24	95
7.00	52.58	98
9.00	53.64	99.5
11.00	53.92	100

试验方案	探空数据		ERA5
试验方案	RMSE	MAE	RMSE	MAE
方案Ⅰ	1.40	1.19	1.32	1.09
方案Ⅱ	1.79	1.42	1.56	1.19
方案Ⅲ	1.77	1.38	1.49	1.14
方案Ⅳ	1.76	1.39	1.51	1.16

顾及水汽分布的非均匀离散化GNSS水汽层析精化方法

A non-uniform discretization GNSS water vapor tomography refined method considering water vapor distributions

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