不同饱和水汽压模型对GNSS反演可降水量的影响分析

李得宴; 杨维芳; 高志钰; 李蓉蓉

doi:10.13442/j.gnss.1008-9268.2020.06.008

不同饱和水汽压模型对GNSS反演可降水量的影响分析

doi: 10.13442/j.gnss.1008-9268.2020.06.008

李得宴^1,2,3,
杨维芳^1,2,3, ,,
高志钰^1,2,3,4,
李蓉蓉^1,2,3

1. 兰州交通大学测绘与地理信息学院, 兰州 730070;
2. 地理国情监测技术应用国家地方联合工程研究中心, 兰州 730070;
3. 甘肃省地理国情监测工程实验室, 兰州 730070;
4. 中国地震局地质研究所, 北京 100029

基金项目:

国家重点研发计划（2016YFB0501802）；中国博士后科学基金资助项目（2019M660091XB）；兰州交通大学

青年科学基金资助项目（2019003）；兰州交通大学优秀平台（201806）；国家自然科学基金（42061076）

详细信息

作者简介:
李得宴(1995-),男,硕士研究生,主要研究方向为GNSS水汽反演.

杨维芳(1970-),女,教授,博士,主要研究方向为测绘仪器检定方法;测量数据处理;测绘仪器内置程序研发;测量仪器在工程中的应用.

高志钰(1994-),男,博士研究生,主要研究方向为高精度GNSS数据处理及应用.

李蓉蓉(1995-),女,硕士研究生,主要研究方向为InSAR数据处理及应用.

通讯作者:
杨维芳 E-mail:99903217@qq.com

中图分类号: P228.49
计量
- 文章访问数: 527
- HTML全文浏览量: 125
- PDF下载量: 38
- 被引次数: 0
出版历程
- 收稿日期: 2020-06-24
- 网络出版日期: 2021-04-09

Analysis of influence of different saturated water vapor pressure models on GNSS inversion precipitable water

LI Deyan^1,2,3,
YANG Weifang^{1,2,3
, ,},
GAO Zhiyu^1,2,3,4,
LI Rongrong^1,2,3

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China;
2. Nation-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China;
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China;
4. Institute of Geology, China Earthquake Administration, Beijing 100029, China

摘要

摘要: 地基全球卫星导航系统（GNSS）水汽反演过程中需要大气加权平均温度T_m的参与，而饱和水汽压E_s作为T_m计算过程中的一个重要变量影响着T_m，因此E_s将会间接地影响大气可降水量（PWV）的反演精度.针对目前地基GNSS水汽反演研究中普遍采用的三种不同的饱和水汽压模型（Magnus-Tetens模型、BUCK模型、Goff-Gratch模型），本文就不同的饱和水汽压模型参与反演是否会引起水汽反演结果的差异进行了研究.以香港为研究区域，利用GAMIT解算了2016年旱雨两季（2、7月）的天顶湿延迟（ZWD），同时利用king's park探空站的探空数据通过数值积分计算得到旱雨两季（2、7月）的T_m，然后严格参照反演步骤编程模拟计算旱雨两季（2、7月）每天的PWV.最后对比并分析了不同饱和水汽压模型参与计算对T_m和PWV的影响及原因，结果表明：三种饱和水汽压模型参与计算得到的PWV与真值（探空站计算得到的PWV）之间不存在具有统计意义的显著性差异，因此均可被用来提供计算T_m时所用到的饱和水汽压E_s，但是通过对比分析发现部分研究人员将BUCK模型中的变量T当作露点温度而非大气温度进行计算会使T_m产生较大的误差，进而对该误差进行了不合理性分析.本文的分析将会对后续地基GNSS水汽反演研究中的处理提供一定的参考.
- 地基GNSS水汽反演 /
- 加权平均温度 /
- 饱和水汽压 /
- 模型对比
Abstract: Atmospheric weighted mean temperature (T_m) participation is required for ground-based GNSS water vapor inversion, and saturated water vapor (E_s) is an important variable in the calculation process of T_m that effects T_m, so e_swill be indirectly affect the inversion accuracy of Precipitable Water Vapor (PWV). In view of the three saturated water vapor pressure models (Magnus-Tetens model, BUCK model, Goff-Gratch model) established by different researchers commonly used in the research of ground-based GNSS water vapor inversion, this paper will research different saturated water vapor pressure models participate in the inversion Whether cause differences in results. Taking Hong Kong as the research area, using GAMIT to solve the Zenith Wet Delay (ZWD) of the dry and rainy season (February and July) in 2016, meanwhile using the sounding data of the King's park sounding station to calculate The T_m of the dry and rainy seasons (February and July) through the way of integrate numerical, and then calculating the PWV of the dry and rainy seasons (February and July) through programing with reference to the inversion steps.Thorough comparing and analyzing to get the effects and reasons of different saturated water vapor pressure models participating in the calculation on T_m and PWV. The results show that between the PWV calculated by the three saturated water vapor pressure models and the true value (PWV calculated by the sounding station) have no statistically significant differences, so all of them can be used to provide the saturated water vapor pressure e_s in the calculation of T_m, However, through comparative analysis, it is found that some researchers use the variable T in the BUCK model as the dew point temperature instead of the atmospheric temperature to make T_m produce a larger error. The analysis in this paper will provide a certain reference for the treatment of T_m in the subsequent research of ground-based GNSS water vapor inversion.
- ground-based GNSS water vapor inversion /
- weighted mean temperature /
- saturated water vapor pressure model /
- model comparison

HTML全文

参考文献(24)

[1]	李国平.地基GPS气象学[M].北京:科学出版社,2010:130-133.
[2]	BEVIS M,BUSINGER S,HERRING T A,et al.GPS meteorology:remote sensing of atmospheric watervapor using the global positioning system[J].Journal of geophysical research atmospheres,1992,97(D14):15787-15801.DOI: 10.1029/92JD01517.
[3]	MOORE B E,MOLINERO V.Structural transformation in supercooled water controls the crystallization rate of ice[J].Nature,2011,479(7374):506-508.DOI: 10.1038/nature10586.
[4]	KAMPFER,NIKLAUS.Monitoring atmospheric water vapour:ground-based remote sensing and in-situ methods[M].New York:Springer,2013:315-323.
[5]	ALDUCHOV O A,ESKRIDGE R E.Improved magnus form approximation of saturation vapor pressure[J].Journal of applied meteorology,1996,35(4):601-609.DOI: 10.2172/548871.
[6]	TETENS O.Vber einige meteorologische begriffe[J].Zeitschrift fur geophys,1930(6):207-309.
[7]	MURRAY F W.On the computation of saturation vapor pressure[J].Journal of applied meteorology,1967,6(1):203-204.DOI: 10.1175/1520-0450(1967)006<0203:OTCOSV>2.0.CO;2.
[8]	HUANGL L K,LIU L L,CHEN H,et al.An improved atmospheric weighted mean temperature model and its impact on GNSS precipitable water vapor estimates for China[J].GPS solutions,2019,23(2).DOI: 10.1007/s10291-019-0843-1.
[9]	WANG X M,ZHANG K F, WU S Q, et al.Water vapor-weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend[J].Journal of geophysical research atmospheres,2016,121(2).DOI:10.1002/2015 JD024181.
[10]	GOFF J A.Saturation pressure of water on the new Kelvin temperature scale[C]//Transactions of the american society of heating and ventilating engineers,1957:347-354
[11]	General meteorological standards and recommended practices[S/OL].(2019-12-2)[2020-2-24].https://library.wmo.int/doc_num.php?explnum_id=10113.
[12]	单九生,邹海波,刘熙明,等.GPS/MET水汽反演中Tm模型的本地化研究[J].气象与减灾研究,2012,35(1):42-46.
[13]	孙菲浩,郑南山,杜飞.香港市域大气加权平均温度模型构建及其应用[J].气象科技,2019,47(3):508-512.
[14]	BUCK A L.New equations for computing vapor pressure and enhancement factor[J].Journal of applied meteorology,1981,20(12):1527-1532.DOI: 10.1175/1520-0450(1981)0202.0.CO;2.
[15]	龚绍琦.中国区域大气加权平均温度的时空变化及模型[J].应用气象学报,2013,24(3):332-341.
[16]	李黎,田莹,谢威,等.基于探空资料的湖南地区加权平均温度本地化模型研究[J].大地测量与地球动力学,2017,37(3):282-286,291.
[17]	何琦敏,张克非.加权平均温度的非线性回归研究[C]//第九届中国卫星导航学术年会论文集,2018:1-7.
[18]	李星光.极端天气条件下大气可降水量地基GPS反演研究[D].徐州:中国矿业大学,2015:34-35.
[19]	董慧涵,黄洁玫,朱红梅.香港气候的基本特征[J].广州师院学报(自然科学版),1994(1):42-49.
[20]	香港天文台.香港气象及潮水观测摘要[EB/OL].[2009-08] [2020-02-24].https://www.isd.gov.hk/eng/bookorder.htm.
[21]	WAGNER W,PRUβ A.The IAPWS formulation 1995 for the thermodynamic properties of ordinary water substance for general and scientific use[J].Journal of physical and chemical reference data,2002,31(2):387.DOI: 10.1063/1.1461829.
[22]	SAUL A,WANGER W.International equations for the saturation properties of ordinary water substance[J].Journal of physical and chemical reference,1987,16(4):893-901.DOI: 10.1063/1.555787.
[23]	王洪,曹云昌,郭启云,等.利用探空资料计算水汽压[J].气象科技,2013,41(5):847-851.
[24]	World meterological organization,Guide to meteorological instruments and methods of observation[EB/OL].(2008-01-01)[2020-02-24].https://www.weather.gov/media/epz/mesonet/CWOP-WMO8.pdf.