Bevis公式在不同高度面的适用性以及基于近地大气温度的全球加权平均温度模型
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摘要
加权平均温度(T_m)是全球卫星导航系统(GNSS)反演可降水量(PWV)过程中的关键参量。利用Bevis公式和地表温度可以方便地得到地表附近的高精度T_m估计值。然而,不少研究指出,Bevis公式在高海拔地区存在较大误差。本文对Bevis公式在不同高度面的适用性进行研究后发现,Bevis公式在海拔较低时精度较高,随着海拔升高,精度逐渐降低。为了解决Bevis公式在高海拔地区适用性较低的问题,本文对近地空间范围内(本文指0～10 km的高程范围)的T_m与大气温度的关系展开了研究,发现两者在全球范围内都拥有很高的相关性,由此本文构建了基于近地大气温度的全球加权平均温度模型。对模型的检验结果表明,该模型在近地空间范围内的任意高度面上都可以提供高精度的T_m估计值。
Weighted mean temperature is a critical parameter in GNSS technology to retrieve precipitable water vapor(PWV). It is convenient to obtain high-accuracy T_m estimation near surface utilizing Bevis formula and surface temperature. However, some researches pointed out that the Bevis formula has large uncertainties in high-altitude regions. This paper researches the applicability of Bevis formula at different height levels and finds that the Bevis formula has relatively high precision when the altitude is low, while with altitude increasing, the precision decreases gradually. To solve the problem, this paper studies the relationship between T_m and atmospheric temperature of the near-earth space range(the height range between 0～10 km) and finds that they have high correlation on a global scale. Accordingly, this paper builds a global weighted mean temperature model based on near-earth atmospheric temperature. Validation results of the model show that this model can provide high-accuracy T_m estimation at any height level in the near-earth space range.
Weighted mean temperature is a critical parameter in GNSS technology to retrieve precipitable water vapor(PWV). It is convenient to obtain high-accuracy T_m estimation near surface utilizing Bevis formula and surface temperature. However, some researches pointed out that the Bevis formula has large uncertainties in high-altitude regions. This paper researches the applicability of Bevis formula at different height levels and finds that the Bevis formula has relatively high precision when the altitude is low, while with altitude increasing, the precision decreases gradually. To solve the problem, this paper studies the relationship between T_m and atmospheric temperature of the near-earth space range(the height range between 0～10 km) and finds that they have high correlation on a global scale. Accordingly, this paper builds a global weighted mean temperature model based on near-earth atmospheric temperature. Validation results of the model show that this model can provide high-accuracy T_m estimation at any height level in the near-earth space range.
引文
[1] ROCKEEN C, VAN HOVE T, WARE R. Near real-time GPS sensing of atmospheric water vapor[J]. Geophysical Research Letters, 1997, 24(24): 3221-3224.
[2] ALLAN R P. The role of water vapour in earth’s energy flows[J]. Surveys in Geophysics, 2012, 33(3-4): 557-564.
[3] BEVIS M, BUSINGER S, CHISWELL S, et al. GPS meteorology: mapping zenith wet delays onto precipitable water[J]. Journal of Applied Meteorology, 1994, 33(3): 379-386.
[4] SAASTAMOINEN J. Atmospheric correction for the troposphere and stratosphere in radio ranging satellites[M]. Washington, D.C.: American Geophysical Union, 1972(15): 247-251.
[5] HOPFIELD H S. Tropospheric effect on electromagnetically measured range: prediction from surface weather data[J]. Radio Science, 1971, 6(3): 357-367.
[6] ASKNE J, NORDIUS H. Estimation of tropospheric delay for microwaves from surface weather data[J]. Radio Science, 1987, 22(3): 379-386.
[7] BEVIS M, BUSINGER S, HERRING T A, et al. GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system[J]. Journal of Geophysical Research: Atmospheres, 1992, 97(D14): 15787-15801.
[8] WANG Xiaoming, ZHANG Kefei, WU Suqin, 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): 833-852.
[9] DING Maohua. A neural network model for predicting weighted mean temperature[J]. Journal of Geodesy, 2018, 92(10): 1187-1198.
[10] ROSS R J, ROSENFELD S. Estimating mean weighted temperature of the atmosphere for global positioning system applications[J]. Journal of Geophysical Research: Atmospheres, 1997, 102(D18): 21719-21730.
[11] DUAN Jingping, BEVIS M, FANG Peng, et al. GPS meteorology: direct estimation of the absolute value of precipitable water[J]. Journal of Applied Meteorology, 1996, 35(6): 830-838.
[12] WANG Junhong, ZHANG Liangying, DAI Aiguo. Global estimates of water-vapor-weighted mean temperature of the atmosphere for GPS applications[J]. Journal of Geophysical Research: Atmospheres, 2005, 110(D21): D21101.
[13] YAO Yibin, ZHANG Bao, XU Chaoqian, et al. Analysis of the global Tm-Ts correlation and establishment of the latitude-related linear model[J]. Chinese Science Bulletin, 2014, 59(19): 2340-2347.
[14] YAO Y, ZHANG B, XU C, et al. Improved one/multi-parameter models that consider seasonal and geographic variations for estimating weighted mean temperature in ground-based GPS meteorology[J]. Journal of Geodesy, 2014, 88(3): 273-282.
[15] YAO Yibin, ZHU Shuang, YUE Shunqiang. A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere[J]. Journal of Geodesy, 2012, 86(12): 1125-1135.
[16] ZHANG Hongxing, YUAN Yunbin, LI Wei, et al. GPS PPP-derived precipitable water vapor retrieval based on Tm/Ps from multiple sources of meteorological data sets in China[J]. Journal of Geophysical Research: Atmospheres, 2017, 122(8): 4165-4183.
[17] 于胜杰, 柳林涛. 水汽加权平均温度回归公式的验证与分析[J]. 武汉大学学报(信息科学版), 2009, 34(6): 741-744. YU Shengjie, LIU Lintao. Validation and analysis of the water-vapor-weighted mean temperature from Tm-Ts relationship[J]. Geomatics and Information Science of Wuhan University, 2009, 34(6): 741-744.
[18] DAVIS J L, HERRING T A, SHAPIRO I I, et al. Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length[J]. Radio Science, 1985, 20(6): 1593-1607.
[19] BEVIS M, BUSINGER S, CHISWELL S. Earth-based GPS meteorology: an overview, american geophysical union 1995 fall meeting[J]. EOS, 1995(76): 46.
[20] SIMMONS A, UPPALA S, DEE D. ERA-interim: new ECMWF reanalysis products from 1989 onwards[J]. ECMWF Newsletter, 2006(110): 25-36.
[21] DEE D P, UPPALA S M, SIMMONS A J, et al. The ERA-interim reanalysis: configuration and performance of the data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(656): 553-597.
[22] B?HM J, M?LLER G, SCHINDELEGGER M, et al. Development of an improved empirical model for slant delays in the troposphere (GPT2w)[J]. GPS Solutions, 2015, 19(3): 433-441.
[23] LAGLER K, SCHINDELEGGER M, B?HM J, et al. GPT2: empirical slant delay model for radio space geodetic techniques[J]. Geophysical Research Letters, 2013, 40(6): 1069-1073.
[24] YAO Yibin, ZHANG Bao, YUE Shunqiang, et al. Global empirical model for mapping zenith wet delays onto precipitable water[J]. Journal of Geodesy, 2013, 87(5): 439-448.
[25] ACKERMAN S, KNOX J A. Meteorology: understanding the atmosphere[M]. Pacific Grove, CA: Thomson Learning,2006.
[2] ALLAN R P. The role of water vapour in earth’s energy flows[J]. Surveys in Geophysics, 2012, 33(3-4): 557-564.
[3] BEVIS M, BUSINGER S, CHISWELL S, et al. GPS meteorology: mapping zenith wet delays onto precipitable water[J]. Journal of Applied Meteorology, 1994, 33(3): 379-386.
[4] SAASTAMOINEN J. Atmospheric correction for the troposphere and stratosphere in radio ranging satellites[M]. Washington, D.C.: American Geophysical Union, 1972(15): 247-251.
[5] HOPFIELD H S. Tropospheric effect on electromagnetically measured range: prediction from surface weather data[J]. Radio Science, 1971, 6(3): 357-367.
[6] ASKNE J, NORDIUS H. Estimation of tropospheric delay for microwaves from surface weather data[J]. Radio Science, 1987, 22(3): 379-386.
[7] BEVIS M, BUSINGER S, HERRING T A, et al. GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system[J]. Journal of Geophysical Research: Atmospheres, 1992, 97(D14): 15787-15801.
[8] WANG Xiaoming, ZHANG Kefei, WU Suqin, 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): 833-852.
[9] DING Maohua. A neural network model for predicting weighted mean temperature[J]. Journal of Geodesy, 2018, 92(10): 1187-1198.
[10] ROSS R J, ROSENFELD S. Estimating mean weighted temperature of the atmosphere for global positioning system applications[J]. Journal of Geophysical Research: Atmospheres, 1997, 102(D18): 21719-21730.
[11] DUAN Jingping, BEVIS M, FANG Peng, et al. GPS meteorology: direct estimation of the absolute value of precipitable water[J]. Journal of Applied Meteorology, 1996, 35(6): 830-838.
[12] WANG Junhong, ZHANG Liangying, DAI Aiguo. Global estimates of water-vapor-weighted mean temperature of the atmosphere for GPS applications[J]. Journal of Geophysical Research: Atmospheres, 2005, 110(D21): D21101.
[13] YAO Yibin, ZHANG Bao, XU Chaoqian, et al. Analysis of the global Tm-Ts correlation and establishment of the latitude-related linear model[J]. Chinese Science Bulletin, 2014, 59(19): 2340-2347.
[14] YAO Y, ZHANG B, XU C, et al. Improved one/multi-parameter models that consider seasonal and geographic variations for estimating weighted mean temperature in ground-based GPS meteorology[J]. Journal of Geodesy, 2014, 88(3): 273-282.
[15] YAO Yibin, ZHU Shuang, YUE Shunqiang. A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere[J]. Journal of Geodesy, 2012, 86(12): 1125-1135.
[16] ZHANG Hongxing, YUAN Yunbin, LI Wei, et al. GPS PPP-derived precipitable water vapor retrieval based on Tm/Ps from multiple sources of meteorological data sets in China[J]. Journal of Geophysical Research: Atmospheres, 2017, 122(8): 4165-4183.
[17] 于胜杰, 柳林涛. 水汽加权平均温度回归公式的验证与分析[J]. 武汉大学学报(信息科学版), 2009, 34(6): 741-744. YU Shengjie, LIU Lintao. Validation and analysis of the water-vapor-weighted mean temperature from Tm-Ts relationship[J]. Geomatics and Information Science of Wuhan University, 2009, 34(6): 741-744.
[18] DAVIS J L, HERRING T A, SHAPIRO I I, et al. Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length[J]. Radio Science, 1985, 20(6): 1593-1607.
[19] BEVIS M, BUSINGER S, CHISWELL S. Earth-based GPS meteorology: an overview, american geophysical union 1995 fall meeting[J]. EOS, 1995(76): 46.
[20] SIMMONS A, UPPALA S, DEE D. ERA-interim: new ECMWF reanalysis products from 1989 onwards[J]. ECMWF Newsletter, 2006(110): 25-36.
[21] DEE D P, UPPALA S M, SIMMONS A J, et al. The ERA-interim reanalysis: configuration and performance of the data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(656): 553-597.
[22] B?HM J, M?LLER G, SCHINDELEGGER M, et al. Development of an improved empirical model for slant delays in the troposphere (GPT2w)[J]. GPS Solutions, 2015, 19(3): 433-441.
[23] LAGLER K, SCHINDELEGGER M, B?HM J, et al. GPT2: empirical slant delay model for radio space geodetic techniques[J]. Geophysical Research Letters, 2013, 40(6): 1069-1073.
[24] YAO Yibin, ZHANG Bao, YUE Shunqiang, et al. Global empirical model for mapping zenith wet delays onto precipitable water[J]. Journal of Geodesy, 2013, 87(5): 439-448.
[25] ACKERMAN S, KNOX J A. Meteorology: understanding the atmosphere[M]. Pacific Grove, CA: Thomson Learning,2006.
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