时空加权与再分析资料相结合的GNSS PWV时序填补方法

doi:10.11947/j.AGCS.2023.20220241

测绘学报 ›› 2023, Vol. 52 ›› Issue (10): 1661-1668.doi: 10.11947/j.AGCS.2023.20220241

时空加权与再分析资料相结合的GNSS PWV时序填补方法

赵庆志¹, 杜正², 姚宜斌², 姚顽强¹

1. 西安科技大学测绘科学与技术学院, 陕西西安 710054;
2. 武汉大学测绘学院, 湖北武汉 430079

收稿日期:2022-04-13 修回日期:2023-05-20 发布日期:2023-10-31
通讯作者: 姚宜斌 E-mail:ybyao@whu.edu.cn
作者简介:赵庆志(1989-),男,博士,副教授,研究方向为GNSS数据处理及GNSS气象学。E-mail:zhaoqingzhia@163.com
基金资助:
国家自然科学基金(42274039);陕西省创新能力支撑计划(2023KJXX-050);陕西省教育厅服务地方专项科研计划(22JE012);国家大坝安全工程技术研究中心开放基金(CX2022B01)

Combining spatio-temporal weighting with reanalysis data for filling in GNSS PWV time series

ZHAO Qingzhi¹, DU Zheng², YAO Yibin², YAO Wanqiang¹

1. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China;
2. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received:2022-04-13 Revised:2023-05-20 Published:2023-10-31
Supported by:
The National Natural Science Foundation of China (No. 42274039);Shaaxi Provincial Innovation Capacity Support Plan Project (No. 2023KJXX-050);Local Special Scientific Research Plan Project of Shaanxi Provincial Department of Education (No. 22JE012);Open Fund Project of National Dam Safety Engineering Technology Research Center (No. CX2022B01)

摘要/Abstract

摘要： PWV是对流层中的重要参数之一,长时序连续的PWV对长期气候变化研究具有重要影响。但受外界因素影响,目前GNSS获取的PWV长时序数据缺失严重或分辨率较差,无法满足长时序分析的应用需求。针对该问题,本文提出了一种顾及时空加权的PWV长时序填补方法(STW)。该方法引入再分析资料,同时顾及GNSS站点上空水汽在空间和时间上的变化特征,并根据测站位置给予PWV时空变化不同的权值,选取中国地壳运动观测网络的实测数据和欧洲中尺度天气预报中心第五代全球大气再分析数据集进行试验。结果表明,本文提出的STW方法在不同时间分辨率的数据缺失和PWV长时序重采样方面均优于经典的线性插值和模型替换方法,可以得到更加可靠、精确和完整的PWV长时序集。

关键词: GNSS, 时空加权, PWV, 时序填补

Abstract: Precipitable water vapor (PWV) is one of the most critical parameters in the troposphere, and the long time series of continuous PWV has an essential impact on long-term climate change studies. However, due to the influence of external factors, the current long time series of PWV acquired by GNSS have missing data or poor resolution, which cannot meet the application needs of long time series analysis. This paper proposes a spatio-temporal weighted (STW) method to fill in the PWV long time series to address this problem. This method considers both the spatio-temporal variability of water vapor over GNSS stations and assigns different weights to the spatio-temporal variability of PWV according to the station locations. An experiment tested the STW using the Crustal Movement Observation Network of China (CMONOC) and the ECMWF 5th generation global atmospheric reanalysis dataset ERA5. The results show that the proposed STW method outperforms the traditional linear interpolation and period model methods in terms of missing data and PWV long time series resampling at different time scale resolutions and can obtain more reliable, accurate, and complete PWV long time series.

Key words: GNSS, spatio-temporal weighting, PWV, time-series filling

中图分类号:

P228

赵庆志, 杜正, 姚宜斌, 姚顽强. 时空加权与再分析资料相结合的GNSS PWV时序填补方法[J]. 测绘学报, 2023, 52(10): 1661-1668.

ZHAO Qingzhi, DU Zheng, YAO Yibin, YAO Wanqiang. Combining spatio-temporal weighting with reanalysis data for filling in GNSS PWV time series[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(10): 1661-1668.

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时空加权与再分析资料相结合的GNSS PWV时序填补方法

Combining spatio-temporal weighting with reanalysis data for filling in GNSS PWV time series

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