Monitoring Zenithal Total Delays over the three different climatic zones from IGS GPS final products:A comparison between the use of the VMF1 and GMF mapping functions
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摘要
The International GNSS Service(IGS) final products(ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring. In this study, we investigate if there is a systematic difference coming from the choice between the Vienna Mapping Function 1(VMF1) and the Global Mapping Function(GMF) for the modeling of Zenith Total Delay(ZTD) estimates, as well as the Integrated Precipitable Water Vapor(IPWV) estimates that are deduced from them. As ZTD estimates cannot be fully separated from coordinate estimates, we also investigated the coordinate repeatability between subsequent measurements.For this purpose, we monitored twelve GNSS stations on a global scale, for each of the three climatic zones(polar, mid-latitudes and tropical), with four stations on each zone. We used an automated processing based on the Bernese GNSS Software Version 5.2 by applying the Precise Point Positioning(PPP)approach, L3 Ionosphere-free linear combination, 7 cutoff elevation angle and 2 h sampling. We noticed an excellent agreement with the ZTD estimates and coordinate repeatability for all the stations w.r.t to CODE(the Center for Orbit Determination in Europe) and USNO(US Naval Observatory) products, except for the Antarctic station(Davis) which shows systematic biases for the GMF related results. As a final step, we investigated the effect of using two mapping functions(VMF1 and GMF) to estimate the IPWV,w.r.t the IPWV estimates provided by the Integrated Global Radiosonde Archive(IGRA). The GPS-derived IPWV estimates are very close to the radiosonde-derived IPWV estimates, except for one station in the tropics(Tahiti).
The International GNSS Service(IGS) final products(ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring. In this study, we investigate if there is a systematic difference coming from the choice between the Vienna Mapping Function 1(VMF1) and the Global Mapping Function(GMF) for the modeling of Zenith Total Delay(ZTD) estimates, as well as the Integrated Precipitable Water Vapor(IPWV) estimates that are deduced from them. As ZTD estimates cannot be fully separated from coordinate estimates, we also investigated the coordinate repeatability between subsequent measurements.For this purpose, we monitored twelve GNSS stations on a global scale, for each of the three climatic zones(polar, mid-latitudes and tropical), with four stations on each zone. We used an automated processing based on the Bernese GNSS Software Version 5.2 by applying the Precise Point Positioning(PPP)approach, L3 Ionosphere-free linear combination, 7 cutoff elevation angle and 2 h sampling. We noticed an excellent agreement with the ZTD estimates and coordinate repeatability for all the stations w.r.t to CODE(the Center for Orbit Determination in Europe) and USNO(US Naval Observatory) products, except for the Antarctic station(Davis) which shows systematic biases for the GMF related results. As a final step, we investigated the effect of using two mapping functions(VMF1 and GMF) to estimate the IPWV,w.r.t the IPWV estimates provided by the Integrated Global Radiosonde Archive(IGRA). The GPS-derived IPWV estimates are very close to the radiosonde-derived IPWV estimates, except for one station in the tropics(Tahiti).
The International GNSS Service(IGS) final products(ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring. In this study, we investigate if there is a systematic difference coming from the choice between the Vienna Mapping Function 1(VMF1) and the Global Mapping Function(GMF) for the modeling of Zenith Total Delay(ZTD) estimates, as well as the Integrated Precipitable Water Vapor(IPWV) estimates that are deduced from them. As ZTD estimates cannot be fully separated from coordinate estimates, we also investigated the coordinate repeatability between subsequent measurements.For this purpose, we monitored twelve GNSS stations on a global scale, for each of the three climatic zones(polar, mid-latitudes and tropical), with four stations on each zone. We used an automated processing based on the Bernese GNSS Software Version 5.2 by applying the Precise Point Positioning(PPP)approach, L3 Ionosphere-free linear combination, 7 cutoff elevation angle and 2 h sampling. We noticed an excellent agreement with the ZTD estimates and coordinate repeatability for all the stations w.r.t to CODE(the Center for Orbit Determination in Europe) and USNO(US Naval Observatory) products, except for the Antarctic station(Davis) which shows systematic biases for the GMF related results. As a final step, we investigated the effect of using two mapping functions(VMF1 and GMF) to estimate the IPWV,w.r.t the IPWV estimates provided by the Integrated Global Radiosonde Archive(IGRA). The GPS-derived IPWV estimates are very close to the radiosonde-derived IPWV estimates, except for one station in the tropics(Tahiti).
引文
[1]J.M. Duan, GPS meteorology:direct estimation of the absolute value of precipitable water, J. Appl. Meteorol. 35(6)(1996)830-838.
[2]J.L Davis, T.A. Herring, I.I. Shapiro, et al., Geodesy by radio interferometry:effects of atmospheric modeling errors on estimates of baseline length, Radio.Sci. 20(6)(1985)1593-1607.
[3]A.E. Niell, Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI, J. Atmos. Ocean. Technol. 18(6)(1972)830-850.
[4]J. Boehm, B. Werl, H. Schuh, Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data, J. Geophys. Res. Solid. Earth 111(B2)(2006).
[5]J. Boehm, A. Niell, P. Tregoning, et al., Global Mapping Function(GMF):a new empirical mapping function based on numerical weather model data, Geophys. Res. Lett. 33(7)(2006).
[6]J.W. Marini, Correction of satellite tracking data for an arbitrary tropospheric profile, Radio. Sci. 7(2)(2016)223-231.
[7]J. Kouba, Implementation and testing of the gridded Vienna mapping function1(VMF1), J. Geodes. 82(4-5)(2008)193-205.
[8]J. Saastamoinen, Atmospheric Correction for Troposphere and Stratosphere in Radio Ranging of Satellites[C], The Use of Artificial Satellites for Geodesy,1972, pp. 247-251.
[9]M. Bevis, S. Businger, T.A. Herring, et al., GPS meteorology:remote sensing of atmospheric water vapor using the global positioning system, J. Geophys. Res.Atmos. 97(D14)(1992)15787-15801.
[10]F. Zhang, J.-P. Barriot, G. Xu, T.-K. Yeh, Metrology assessment of the accuracy of precipitable water vapor estimates from GPS data acquisition in tropical areas:the Tahiti case, Rem. Sens. 10(5)(2018)758.
[11]J.F. Zumberge, M.B. Heflin, D.C. Jefferson, et al., Precise point positioning for the efficient and robust analysis of GPS data from large networks, J. Geophys.Res. Solid Earth 102(B3)(1997)5005-5017.
[12]M. Rothacher, G. Beutler, Advanced Aspects of Satellite Positioning. Lecture Notes for ENGO 609.90, The University of Calgary, Alberta, Canada, 2002.
[13]Imke Durre, S. Vose Russell, David B. Wuertz, Overview of the integrated global radiosonde archive, J. Clim. 19(1)(2006)53-68.
[14]F.Z. Zhang, J.P. Barriot, M. Keitapu, L. Sichoix, Analysis of the time evolution(1974-2017)of integrated precipitable water from radiosonde data over French polynesia, in:Proceedings of the 2017 International Conference on Earth Observations and Societal Impacts(ICEO&SI 2017), Yilan, Taiwan, 25-27 June 2017.
[15]G. Chen, T.A. Herring, Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data, J. Geophys. Res. Solid. Earth 102(B9)(1997)20489-20502.
[16]G. Deblonde, S. Macpherson, Y. Mireault, et al., Evaluation of GPS predpitable water over Canada and the IGS network, J. Appl. Meteorol. 44(1)(2005)153-166.
[17]Junhong Wang, Liangying Zhang, Systematic errors in global radiosonde precipitable water data from comparisons with ground-based GPS measurements, J. Clim. 21(10)(2008)2218-2238.
[2]J.L Davis, T.A. Herring, I.I. Shapiro, et al., Geodesy by radio interferometry:effects of atmospheric modeling errors on estimates of baseline length, Radio.Sci. 20(6)(1985)1593-1607.
[3]A.E. Niell, Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI, J. Atmos. Ocean. Technol. 18(6)(1972)830-850.
[4]J. Boehm, B. Werl, H. Schuh, Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data, J. Geophys. Res. Solid. Earth 111(B2)(2006).
[5]J. Boehm, A. Niell, P. Tregoning, et al., Global Mapping Function(GMF):a new empirical mapping function based on numerical weather model data, Geophys. Res. Lett. 33(7)(2006).
[6]J.W. Marini, Correction of satellite tracking data for an arbitrary tropospheric profile, Radio. Sci. 7(2)(2016)223-231.
[7]J. Kouba, Implementation and testing of the gridded Vienna mapping function1(VMF1), J. Geodes. 82(4-5)(2008)193-205.
[8]J. Saastamoinen, Atmospheric Correction for Troposphere and Stratosphere in Radio Ranging of Satellites[C], The Use of Artificial Satellites for Geodesy,1972, pp. 247-251.
[9]M. Bevis, S. Businger, T.A. Herring, et al., GPS meteorology:remote sensing of atmospheric water vapor using the global positioning system, J. Geophys. Res.Atmos. 97(D14)(1992)15787-15801.
[10]F. Zhang, J.-P. Barriot, G. Xu, T.-K. Yeh, Metrology assessment of the accuracy of precipitable water vapor estimates from GPS data acquisition in tropical areas:the Tahiti case, Rem. Sens. 10(5)(2018)758.
[11]J.F. Zumberge, M.B. Heflin, D.C. Jefferson, et al., Precise point positioning for the efficient and robust analysis of GPS data from large networks, J. Geophys.Res. Solid Earth 102(B3)(1997)5005-5017.
[12]M. Rothacher, G. Beutler, Advanced Aspects of Satellite Positioning. Lecture Notes for ENGO 609.90, The University of Calgary, Alberta, Canada, 2002.
[13]Imke Durre, S. Vose Russell, David B. Wuertz, Overview of the integrated global radiosonde archive, J. Clim. 19(1)(2006)53-68.
[14]F.Z. Zhang, J.P. Barriot, M. Keitapu, L. Sichoix, Analysis of the time evolution(1974-2017)of integrated precipitable water from radiosonde data over French polynesia, in:Proceedings of the 2017 International Conference on Earth Observations and Societal Impacts(ICEO&SI 2017), Yilan, Taiwan, 25-27 June 2017.
[15]G. Chen, T.A. Herring, Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data, J. Geophys. Res. Solid. Earth 102(B9)(1997)20489-20502.
[16]G. Deblonde, S. Macpherson, Y. Mireault, et al., Evaluation of GPS predpitable water over Canada and the IGS network, J. Appl. Meteorol. 44(1)(2005)153-166.
[17]Junhong Wang, Liangying Zhang, Systematic errors in global radiosonde precipitable water data from comparisons with ground-based GPS measurements, J. Clim. 21(10)(2008)2218-2238.