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Retrieval of the change of precipitable water vapor with zenith tropospheric delay in the Chinese mainland
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Advances in Space Research 43 (2009) 82–88 www.elsevier.com/locate/asr
Retrieval of the change of precipitable water vapor with zenith tropospheric delay in the Chinese mainland Yong Wang a,b,*, Yanping Liu a, Lintao Liu b, Zengzhang Guo c, Xiaosan Ge d, Houze Xu b a
College of Traffic and Surveying, Hebei Polytechnic University, 46th, Xinhua West Road, Tangshan, Hebei 063009, China b Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, Hubei 430077, China c Department of Survey Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, China d Wuhan University, Wuhan, Hubei 430073, China Received 27 October 2006; received in revised form 13 July 2007; accepted 27 July 2007
Abstract As a preliminary step for assessing the impact of global positioning system (GPS) refractive delay data in numerical weather prediction (NWP) models, the GPS zenith tropospheric delays (ZTD) are analyzed from 28 permanent GPS sites in the Chinese mainland. The objectives are to estimate the GPS ZTD and their variability in this area. The differences between radiosonde precipitable water vapor (PWV) and GPS PWV have a standard deviation of 4 mm in delay, a bias of 0.24 mm in delay, and a correlation coefficient of 0.94. The correlation between GPS ZTD and radiosonde PWV amounts to 0.89, indicating that the variety of tropospheric zenith delay can reflect the change of precipitable water vapor. The good agreement also guarantees that the information provided by GPS will benefit the NWP models. The time series of GPS ZTD, which were derived continuously from 2002 to 2004, are used to analyze the change of precipitable water vapor in Chinese mainland. It shows that the general trend of GPS ZTD is diminishing from the south-east coastland to the northwest inland, which is in accordance with the distribution of Chinese annual amount of rainfall. The temporal distribution of GPS ZTD in the Chinese mainland is that the GPS ZTD reaches maximum in summer, and it reaches minimum in winter. The long term differences between the observational data sources require further study before GPS derived data become useful for climate studies. Crown copyright Ó 2008 Published by Elsevier Ltd. on behalf of COSPAR. All rights reserved. Keywords: Global positioning system; Precipitable water vapor; Zenith tropospheric delay; Zenith wet delay
1. Introduction Water vapor is a highly variable parameter in atmospheric processes and it plays a crucial role in atmospheric motions on a wide range of scales in space and time. Limitations in humidity observation accuracy, such as temporal and spatial coverage, often lead to problems in numerical weather prediction, in particular, in the prediction of clouds and precipitation. The verification of water vapor simulations in operational weather forecasts and cli*
Corresponding author. Address: College of Traffic and Surveying, Hebei Polytechnic University, 46th, Xinhua West Road, Tangshan, Hebei 063009, China. E-mail addresses: [email protected], [email protected] (Y. Wang).
mate modeling is also difficult because of the lack of high temporal and spatial resolution data. Ground-based GPS networks have been proposed as a possible data source (Bevis et al., 1992) to improve both model validation and the initial model state in the weather forecasts, because the GPS receivers are portable and economic and the GPS measurements are not affected by rain and clouds. Although the GPS receivers do not provide the profile of water vapor as radiosondes, they are advantages in providing automated continuous data whereas radiosondes only provide 2 or 4 measurements per day. Other ground-based measurements such as water vapor radiometers or photometers are affected by rain and clouds. Many authors carried out studies to increase the accuracy of the technique for GPS-based PWV estimation, typically using a small number of stations. Rocken et al. (1995)
0273-1177/$34.00 Crown copyright Ó 2008 Published by Elsevier Ltd. on behalf of COSPAR. All rights reserved. doi:10.1016/j.asr.2007.07.050
Y. Wang et al. / Advances in Space Research 43 (2009) 82–88
was the first to demonstrate the agreement between WVR and GPS derived relative estimates of integrated water vapor (IWV), with a level of agreement of about 1 kg/m2. Emardson et al., 2000 detected instrumental biases due to antenna radomes and the resulting contamination of network solutions. This study used the data independent from the other instruments such as water vapor radiometers to demonstrate the accuracy of the data. It has been demonstrated that the integrated water vapor can be retrieved using ground-based GPS observations with the same level of accuracy as radiosondes and microwave radiometers (Elgered et al., 1997; Duan et al., 1996). Currently some authors derived zenith measurements of water vapor from ground-based GPS for more accurate GPS positioning and weather forecasting. The National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory began research in 1994 to determine the benefit of incorporating GPS zenith integrated measurements of water vapor over stations in the United States into weather forecasting models. Recently, NOAA started to include these measurements into their forecast models. Also, work performed by Galina Dick et al. (2001) shows the benefit of using zenith GPS-derived water vapor measurements over a dense network of receivers in Germany for the assimilation in forecasting models. In addition, the SuomiNet network of receivers provides real-time global estimates of the water vapor from the zenith water vapor measurements (Randolph et al., 2001). Usually an interpolation scheme is used to determine water vapor between receivers when zenith measurements are taken. Integrated measurements of water vapor have been taken with the help of the GPS during the extreme weather events such as Typhoon (Yuei-An Liou et al., 2000). While previous studies have concentrated on demonstrating the quality of GPS-derived IWV, we here concentrate on evaluating the ZTD as the final product rather than PWV or IWV. The ZTD is the direct product of the raw GPS observations, free of errors due to pressure sensors and uncertainties in the derivation of tropospheric temperature. Secondly, the ZTD is expected to become the preferred measure in modern NWP models using 3D or 4D variational data assimilation. Such systems combine different types of observations with a recent forecast field in an optimal way, resulting in statistically the best estimate of the current state of the atmosphere. In such systems, a precise statistical description is necessary for the errors of each type of observation as well as the errors of the forecast variables. If supplementary observations at the GPS site exist, it is beneficial to assimilate them in terms of ZTD in the NWP systems, rather than in terms of IWV. This study estimates the GPS ZTD in the Chinese mainland and analyzes the change of PWV during 2002–2004. 2. Zenith tropospheric delay retrieval The GPS processing software must resolve or model the orbital parameters of the satellites, the transmitter and recei-
83
ver positions, ionospheric delays, phase cycle ambiguities and the clock drifts in addition to solving for the tropospheric delay parameters of interest. This requires the same type of GPS data processing software used for high precision geodetic measurements. We use the GAMIT software, which solves for the ZTD and other parameters using a constrained batch least squares inversion procedure. The GAMIT software parameterizes ZTD as a stochastic variation from the Saastamoinen model, with piecewise linear interpolation between solution epochs. GAMIT is very flexible because it allows a priori constrains of varying degrees of uncertainty. The variation from the hydrostatic delay is constrained to be a Gauss-Markov process which is referred to below as the ‘‘zenith tropospheric parameter constraint”. The ZTD can be divided into two components, ZHD (zenith hydrostatic delay) and ZWD (Zenith Wet Delay). The wet component of the delay, ZWD can be written as ZTD ¼ ZHD þ ZWD
ð1Þ
The ZHD is calculated and the ZTD are transformed routinely into PWV for validation purposes in the following manner. An expression for the ZHD is a function for the gravitational acceleration at the center of mass of the atmospheric column which is a function solely of latitude, h and geodetic height, H: ZHD ¼ ð2:2768 0:0024Þ P s =ð1 0:00266 cos 2h 0:00028H Þ
ð2Þ
where P s is surface pressure in hPa. The definition of precipitable water vapor, PWV is defined as PWV ¼ P ZWD
ð3Þ
where P denotes the transform coefficient and it is generally considered as 0.15. 3. GPS station distribution and data process The data processing carried out for the Crust Monitor Observation Network of China (CMONOC) project includes data from 28 permanent GPS stations. Fig. 1 shows the GPS station distribution. The GPS data are collected daily in RINEX format (Receiver Independent Exchange Format) with a 30 s sampling rate. Surface meteorological data taken at the GPS sites (pressure, temperature) are also acquirable where they are available. The RINEX data are quality checked and stored until the final precise IGS orbits become available, approximately 2 weeks later. In the process of GPS data analysis, the ZTD is absolute estimate values if the baselines’ length is above 500 km; otherwise it is a relatively estimate value (Duan et al., 1996; Herring et al., 2006; Li and Huang, 2005; Li et al., 2003). The ZTD of CMONOC is absolute estimate values for the most of the baselines are longer than 500 km.
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4. Comparison with radiosonde data Radiosondes are the primary operational source of upper air humidity observations, therefore they are a good source of independent validation data for demonstrating the potential value of GPS ZTD data for future assimilation. 4.1. Comparison between GPS ZTD and radiosonde PWV
Fig. 1. GPS station distribution of the CMONOC project.
We should compare GPS ZTD with radiosonde PWV for proving the feasibility of GPS ZTD retrieving PWV in order to retrieve the change of precipitable water vapor by GPS ZTD. Fig. 3 shows the comparison between GPS ZTD and radiosonde PWV. According to Fig. 2, it can be discovered that the trends of GPS ZTD and Radiosonde PWV are consistent to each
Fig. 2. Comparison between GPS ZTD and radiosonde PWV.
Fig. 3. Comparison of the precipitable water vapor between GPS and radiosonde.
Y. Wang et al. / Advances in Space Research 43 (2009) 82–88
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Table 1 GPS ZTD in Chinese mainland from 2002 to 2004 (units: cm) Stations
Win1
Spr1
Sum1
Aut1
Win2
Spr2
Sum2
Aut2
Win3
Spr3
Sum3
Aut3
BJFS BJSH CHUN DLHA DXIN GUAN HLAR HRBN JIXN KMIN LHAS LUZH QION SHAO SUIY TAIN TASH URUM WHJF WUHN WUSH XIAA XIAG XIAM XNIN YANC YONG ZHNZ
235.41 224.62 228.96 163.04 208.98 248.24 218.47
237.6 229.71 230.33 164.79 210.19 257.48 219.12
250.8 240.36 241.67 171.16 215.12 265.09
239.69 229.82 232.04 166.48 212.15 257.87
236.16 189.54 150.57 236.63 243.64 243.06 225.97 229.11 162.6 212.82 242 242.77 199.66 225.44 189.24 245.49 176.73 202.33 249.44 224.62
238.33 194.1 153.5 242.01 251.29 250.39 228.31 232.14 163.44 215.07 246.6 248.91 201.74 229.32 194.01 252.7 179.72 205.1 256.93 229.71
251.38 202.23 161 252.77 258.28 260.26 240.08 243.13 169.09 220.8 261.66 258.25 207.63 238.71 203.62 260.34 186.97 211.93 265.68 240.36
240.31 196.5 155.16 243.34 254.51 248.22 229.95 233.11 165.15 215.11 246.64 246.81 203.2 230.12 196.56 254.59 181.07 206.22 264.14 229.82
236.15 226.42 229.31 162.68 208.96 248.79 218.37 230.62 236.98 189.17 150.57 235.57 245.87 242.56 226.17 229.54 162.24 212.61 242.32 242.46 199.82 225.56 188.69 245.82 176.18 202.25 254.83 226.42
240.38 230.43 231.58 164.72 210.69 258.87 219.57 232.59 240.84 195.24 154.86 242.86 253.79 248.27 229.06 233.38 164.52 215.06 248.06 248.84 202.42 229.61 194.02 254.52 179.64 205.21 260.8 230.43
249.15 242.68 241.89 170.36 215.52 263.94 227.48 243.68 250.05 203.43 162 254.4 257.47 258.85 239.7 244.26 168.12 220.4 259.88 260.44 206.82 240.43 204.15 259.7 186.91 212.71 266.78 242.68
243.72 233.41 233.84 165.59 211.64 255.34 221.46 235.07 242.43 197.07 155.45 245.62 251.73 249.53 231.18 235.91 164.89 215.05 248.54 249 203.52 232.74 196.8 252.66 181.58 206.59 254.36 233.41
234.7 225.37 228.87 162.85 208.88 246.4 218.01 230.29 235.82 188.86 150.41 235.71 244.06 240.82 225.89 228.49 162.69 212.51 240.43 240.61 200.06 224.8 188.72 243.66 176.5 201.86 253.94 225.37
237.68 231.19 230.44 164.7 209.44 257.75 218.9 231.73 238.43 194.85 153.75 242.5 251.82 245.57 227.87 232.1 164.9 214.88 246.53 246.67 202.08 228.02 194.38 253.08 179.27 203.84 258.27 231.19
250.8 242.26 242.11 169.91 214.82 264.35 227.77 242.77 251.28 202.96 161.48 252.61 256.55 256.32 238.87 245.49 168.72 218.82 258.63 259.05 206.31 239.51 203.63 259.66 186.12 212.37 264.29 242.26
241.05 232.53 234.82 167.48 212.35 251.21 221.52 236.25 241.96 198.09 157.67 244.43 248.1 247.45 232.12 234.57 166.82 216.79 246.57 247 204.97 232.55 197.66 249.63 183.59 207.91 255.42 232.53
Fig. 4. Change of ZTD in 2002.
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Y. Wang et al. / Advances in Space Research 43 (2009) 82–88
other. The coefficient between GPS ZTD and radiosonde PWV is up to 0.89. It can be concluded that GPS ZTD not only can be used to retrieve PWV, but also can be reflected the change of PWV. 4.2. Results of the comparison of precipitable water vapor between GPS and radiosonde In order to retrieve the change of GPS PWV, we should verify the reliability of GPS PWV solved with the data of CMONOC project. We calculated the ZTD of the CMONOC project from 190 day (July 9, 2002) to 199 day (July 18, 2002)2002, distilled the ZTD of SHAO station and computed the PWV with the pressure, temperature of the site. We also computed the PWV with the radiosonde data. We compared the PWV between GPS and radiosonde. Fig. 3 shows the result of the comparison. From ten days of data, the differences between radiosonde PWV and GPS PWV have a standard deviation of 4 mm in delay, difference of 0.24 mm in delay, and correlation coefficient of 0.94. So it can be concluded that the accuracy of GPS PWV is sufficed and the GPS PWV of CMONOC is reliable.
5. Seasonal change of GPS ZTD in the Chinese mainland from 2002 to 2004 We calculate the GPS ZTD with the data of CMONOC project. According to the GPS ZTD, we adopted grid method to linearize calculation and plotted the figures of seasonal GPS ZTD change of the Chinese mainland from 2002 to 2004. Table 1 is the GPS ZTD in Chinese Mainland from 2002 to 2004. Fig. 4, Figs. 5 and 6 are the GPS ZTD seasonal change of the Chinese mainland from 2002 to 2004. We analyze all the GPS ZTD changes from 2002 to 2004. The general trend of GPS ZTD change is that it is descended from Southeast coast to Northwest interior, which is identical to the distribution of Chinese annual amount of rainfall. The trend of GPS ZTD change is consistent with the change of atmospheric circumfluence in China. The Aleutian low pressure is significant in spring, autumn and especially in winter. During these seasons the Northern area of China is dominated by Mongolian high pressure, and there is large difference between Aleutian low pressure and Mongolian high pressure. So the atmosphere flows from the mainland to the Northern-pacific. From autumn to spring, the GPS ZTD in the Northern
Fig. 5. Change of ZTD in 2003.
Y. Wang et al. / Advances in Space Research 43 (2009) 82–88
87
Fig. 6. Change of ZTD in 2004.
part is low, and in winter the ZTD is lowest. When the shoot-point of the Sun moves to the north, the Aleutian low pressure and Mongolian high pressure are diminished. Therefore the warm and damp airflow coming from the South substitutes the dry and cold airflow in the Northern China. The GPS ZTD ascends because the season-wind coming from the Southeast carries a lot of water vapor to most areas of China. From spring to summer, the GPS ZTD of the Middle and Northern area in China is high and it is the highest in summer. In autumn, the season-wind withdraws from the Northeast to the Southern China and the GPS ZTD of the Southeast descends. The ZTD of the Southern part are the highest in China, owning to the warm and damp airflow coming from the Southeast. Even so, in summer, due to the influence of typhoon, the GPS ZTD of the Southern coastland is the highest in the year. 6. Conclusions GPS is an effective method of monitoring the PWV change. It can demonstrate continuously the temporal and spatial distribution of the atmospheric water vapor. This study shows the seasonal change of the PWV in different areas in China. The general trend of the change of the GPS ZTD in China is that it is descended from the South-
east coast to the Northwest inland, which is identical to the distribution of China annual amount of rainfall. The GPS ZTD in Chinese mainland reaches maximum in Summer, and minimum in Winter. The values of GPS ZTD in the Southeast coast are the highest, and these in the Northwest interior are the lowest. References Bevis, M., Businger, S., Herring, T.A., et al. GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J. Geophys. Res. (97), 15787–15807, 1992. Duan, J., Bevis, M., Fang, P., et al. GPS meteorology: direct estimation of the absolute value of precipitable water. J. Appl. Meteorol. (35), 830–838, 1996. Elgered, G., Johansson, J.M., Ronnang, B.O., et al. Measuring regional atmospheric water vapor using the Swedish permanent GPS network. Geophys. Res. Lett. (24), 2663–2666, 1997. Emardson, T.R., Johansson, J., Elgered, G. The systematic behavior of water vapor estimates using four years of GPS observations. IEEE Trans. Geosci. Remote Sensing (38), 324–329, 2000. Galina, Dick, Gerd, Gendt, Christoph, Reigber, First experience with near real-time water vapor estimation in a German GPS network. J. Atmos. Solar Terr. Phys. 63, 1295–1304, 2001. Li, Zhenghang, Xu, Xiaohua, Luo, Jia, et al. Using GPS retrieved the distribution and change of the Zenith wet delay of Sanxia area. Geomat. Inform. Sci. Wuhan Univ. (4), 393–396, 2003. Li, Zhenghang, Huang, jinsong, GPS Surveying and Data Processing [M], Wuhan University Publishing Company, p. 3, 2005.
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Randolph H., Warea, David W., Fulker, Seth A., Stein, et al. Real-time national GPS networks for atmospheric sensing, J. Atmos. Solar Terr. Phys. 63, 1315–1330, 2001. Rocken, C., Hove, T., Iohnson, J., et al. GPS/STROM-GPS sensing of atmospheric water vapor for meteorology. J. Atmos. Oceanic Technol. (12), 468–478, 1995.
Herring, T.A., King, R.W., McClusky, S.C., GAMIT Reference Manual. Department of Earth, Atmospheric, and Planetary Sciences, Massachussetts Institute of Technology, 9, 2006. Yuei-An, Liou, Cheng-Yung, Huang, Yu-Tun, Teng, Precipitable water observed by grounded-based GPS receivers and microwave radiometry. Earth Planets Space 52, 445–450, 2000.
Advances in Space Research 43 (2009) 82–88 www.elsevier.com/locate/asr
Retrieval of the change of precipitable water vapor with zenith tropospheric delay in the Chinese mainland Yong Wang a,b,*, Yanping Liu a, Lintao Liu b, Zengzhang Guo c, Xiaosan Ge d, Houze Xu b a
College of Traffic and Surveying, Hebei Polytechnic University, 46th, Xinhua West Road, Tangshan, Hebei 063009, China b Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, Hubei 430077, China c Department of Survey Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, China d Wuhan University, Wuhan, Hubei 430073, China Received 27 October 2006; received in revised form 13 July 2007; accepted 27 July 2007
Abstract As a preliminary step for assessing the impact of global positioning system (GPS) refractive delay data in numerical weather prediction (NWP) models, the GPS zenith tropospheric delays (ZTD) are analyzed from 28 permanent GPS sites in the Chinese mainland. The objectives are to estimate the GPS ZTD and their variability in this area. The differences between radiosonde precipitable water vapor (PWV) and GPS PWV have a standard deviation of 4 mm in delay, a bias of 0.24 mm in delay, and a correlation coefficient of 0.94. The correlation between GPS ZTD and radiosonde PWV amounts to 0.89, indicating that the variety of tropospheric zenith delay can reflect the change of precipitable water vapor. The good agreement also guarantees that the information provided by GPS will benefit the NWP models. The time series of GPS ZTD, which were derived continuously from 2002 to 2004, are used to analyze the change of precipitable water vapor in Chinese mainland. It shows that the general trend of GPS ZTD is diminishing from the south-east coastland to the northwest inland, which is in accordance with the distribution of Chinese annual amount of rainfall. The temporal distribution of GPS ZTD in the Chinese mainland is that the GPS ZTD reaches maximum in summer, and it reaches minimum in winter. The long term differences between the observational data sources require further study before GPS derived data become useful for climate studies. Crown copyright Ó 2008 Published by Elsevier Ltd. on behalf of COSPAR. All rights reserved. Keywords: Global positioning system; Precipitable water vapor; Zenith tropospheric delay; Zenith wet delay
1. Introduction Water vapor is a highly variable parameter in atmospheric processes and it plays a crucial role in atmospheric motions on a wide range of scales in space and time. Limitations in humidity observation accuracy, such as temporal and spatial coverage, often lead to problems in numerical weather prediction, in particular, in the prediction of clouds and precipitation. The verification of water vapor simulations in operational weather forecasts and cli*
Corresponding author. Address: College of Traffic and Surveying, Hebei Polytechnic University, 46th, Xinhua West Road, Tangshan, Hebei 063009, China. E-mail addresses: [email protected], [email protected] (Y. Wang).
mate modeling is also difficult because of the lack of high temporal and spatial resolution data. Ground-based GPS networks have been proposed as a possible data source (Bevis et al., 1992) to improve both model validation and the initial model state in the weather forecasts, because the GPS receivers are portable and economic and the GPS measurements are not affected by rain and clouds. Although the GPS receivers do not provide the profile of water vapor as radiosondes, they are advantages in providing automated continuous data whereas radiosondes only provide 2 or 4 measurements per day. Other ground-based measurements such as water vapor radiometers or photometers are affected by rain and clouds. Many authors carried out studies to increase the accuracy of the technique for GPS-based PWV estimation, typically using a small number of stations. Rocken et al. (1995)
0273-1177/$34.00 Crown copyright Ó 2008 Published by Elsevier Ltd. on behalf of COSPAR. All rights reserved. doi:10.1016/j.asr.2007.07.050
Y. Wang et al. / Advances in Space Research 43 (2009) 82–88
was the first to demonstrate the agreement between WVR and GPS derived relative estimates of integrated water vapor (IWV), with a level of agreement of about 1 kg/m2. Emardson et al., 2000 detected instrumental biases due to antenna radomes and the resulting contamination of network solutions. This study used the data independent from the other instruments such as water vapor radiometers to demonstrate the accuracy of the data. It has been demonstrated that the integrated water vapor can be retrieved using ground-based GPS observations with the same level of accuracy as radiosondes and microwave radiometers (Elgered et al., 1997; Duan et al., 1996). Currently some authors derived zenith measurements of water vapor from ground-based GPS for more accurate GPS positioning and weather forecasting. The National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory began research in 1994 to determine the benefit of incorporating GPS zenith integrated measurements of water vapor over stations in the United States into weather forecasting models. Recently, NOAA started to include these measurements into their forecast models. Also, work performed by Galina Dick et al. (2001) shows the benefit of using zenith GPS-derived water vapor measurements over a dense network of receivers in Germany for the assimilation in forecasting models. In addition, the SuomiNet network of receivers provides real-time global estimates of the water vapor from the zenith water vapor measurements (Randolph et al., 2001). Usually an interpolation scheme is used to determine water vapor between receivers when zenith measurements are taken. Integrated measurements of water vapor have been taken with the help of the GPS during the extreme weather events such as Typhoon (Yuei-An Liou et al., 2000). While previous studies have concentrated on demonstrating the quality of GPS-derived IWV, we here concentrate on evaluating the ZTD as the final product rather than PWV or IWV. The ZTD is the direct product of the raw GPS observations, free of errors due to pressure sensors and uncertainties in the derivation of tropospheric temperature. Secondly, the ZTD is expected to become the preferred measure in modern NWP models using 3D or 4D variational data assimilation. Such systems combine different types of observations with a recent forecast field in an optimal way, resulting in statistically the best estimate of the current state of the atmosphere. In such systems, a precise statistical description is necessary for the errors of each type of observation as well as the errors of the forecast variables. If supplementary observations at the GPS site exist, it is beneficial to assimilate them in terms of ZTD in the NWP systems, rather than in terms of IWV. This study estimates the GPS ZTD in the Chinese mainland and analyzes the change of PWV during 2002–2004. 2. Zenith tropospheric delay retrieval The GPS processing software must resolve or model the orbital parameters of the satellites, the transmitter and recei-
83
ver positions, ionospheric delays, phase cycle ambiguities and the clock drifts in addition to solving for the tropospheric delay parameters of interest. This requires the same type of GPS data processing software used for high precision geodetic measurements. We use the GAMIT software, which solves for the ZTD and other parameters using a constrained batch least squares inversion procedure. The GAMIT software parameterizes ZTD as a stochastic variation from the Saastamoinen model, with piecewise linear interpolation between solution epochs. GAMIT is very flexible because it allows a priori constrains of varying degrees of uncertainty. The variation from the hydrostatic delay is constrained to be a Gauss-Markov process which is referred to below as the ‘‘zenith tropospheric parameter constraint”. The ZTD can be divided into two components, ZHD (zenith hydrostatic delay) and ZWD (Zenith Wet Delay). The wet component of the delay, ZWD can be written as ZTD ¼ ZHD þ ZWD
ð1Þ
The ZHD is calculated and the ZTD are transformed routinely into PWV for validation purposes in the following manner. An expression for the ZHD is a function for the gravitational acceleration at the center of mass of the atmospheric column which is a function solely of latitude, h and geodetic height, H: ZHD ¼ ð2:2768 0:0024Þ P s =ð1 0:00266 cos 2h 0:00028H Þ
ð2Þ
where P s is surface pressure in hPa. The definition of precipitable water vapor, PWV is defined as PWV ¼ P ZWD
ð3Þ
where P denotes the transform coefficient and it is generally considered as 0.15. 3. GPS station distribution and data process The data processing carried out for the Crust Monitor Observation Network of China (CMONOC) project includes data from 28 permanent GPS stations. Fig. 1 shows the GPS station distribution. The GPS data are collected daily in RINEX format (Receiver Independent Exchange Format) with a 30 s sampling rate. Surface meteorological data taken at the GPS sites (pressure, temperature) are also acquirable where they are available. The RINEX data are quality checked and stored until the final precise IGS orbits become available, approximately 2 weeks later. In the process of GPS data analysis, the ZTD is absolute estimate values if the baselines’ length is above 500 km; otherwise it is a relatively estimate value (Duan et al., 1996; Herring et al., 2006; Li and Huang, 2005; Li et al., 2003). The ZTD of CMONOC is absolute estimate values for the most of the baselines are longer than 500 km.
84
Y. Wang et al. / Advances in Space Research 43 (2009) 82–88
4. Comparison with radiosonde data Radiosondes are the primary operational source of upper air humidity observations, therefore they are a good source of independent validation data for demonstrating the potential value of GPS ZTD data for future assimilation. 4.1. Comparison between GPS ZTD and radiosonde PWV
Fig. 1. GPS station distribution of the CMONOC project.
We should compare GPS ZTD with radiosonde PWV for proving the feasibility of GPS ZTD retrieving PWV in order to retrieve the change of precipitable water vapor by GPS ZTD. Fig. 3 shows the comparison between GPS ZTD and radiosonde PWV. According to Fig. 2, it can be discovered that the trends of GPS ZTD and Radiosonde PWV are consistent to each
Fig. 2. Comparison between GPS ZTD and radiosonde PWV.
Fig. 3. Comparison of the precipitable water vapor between GPS and radiosonde.
Y. Wang et al. / Advances in Space Research 43 (2009) 82–88
85
Table 1 GPS ZTD in Chinese mainland from 2002 to 2004 (units: cm) Stations
Win1
Spr1
Sum1
Aut1
Win2
Spr2
Sum2
Aut2
Win3
Spr3
Sum3
Aut3
BJFS BJSH CHUN DLHA DXIN GUAN HLAR HRBN JIXN KMIN LHAS LUZH QION SHAO SUIY TAIN TASH URUM WHJF WUHN WUSH XIAA XIAG XIAM XNIN YANC YONG ZHNZ
235.41 224.62 228.96 163.04 208.98 248.24 218.47
237.6 229.71 230.33 164.79 210.19 257.48 219.12
250.8 240.36 241.67 171.16 215.12 265.09
239.69 229.82 232.04 166.48 212.15 257.87
236.16 189.54 150.57 236.63 243.64 243.06 225.97 229.11 162.6 212.82 242 242.77 199.66 225.44 189.24 245.49 176.73 202.33 249.44 224.62
238.33 194.1 153.5 242.01 251.29 250.39 228.31 232.14 163.44 215.07 246.6 248.91 201.74 229.32 194.01 252.7 179.72 205.1 256.93 229.71
251.38 202.23 161 252.77 258.28 260.26 240.08 243.13 169.09 220.8 261.66 258.25 207.63 238.71 203.62 260.34 186.97 211.93 265.68 240.36
240.31 196.5 155.16 243.34 254.51 248.22 229.95 233.11 165.15 215.11 246.64 246.81 203.2 230.12 196.56 254.59 181.07 206.22 264.14 229.82
236.15 226.42 229.31 162.68 208.96 248.79 218.37 230.62 236.98 189.17 150.57 235.57 245.87 242.56 226.17 229.54 162.24 212.61 242.32 242.46 199.82 225.56 188.69 245.82 176.18 202.25 254.83 226.42
240.38 230.43 231.58 164.72 210.69 258.87 219.57 232.59 240.84 195.24 154.86 242.86 253.79 248.27 229.06 233.38 164.52 215.06 248.06 248.84 202.42 229.61 194.02 254.52 179.64 205.21 260.8 230.43
249.15 242.68 241.89 170.36 215.52 263.94 227.48 243.68 250.05 203.43 162 254.4 257.47 258.85 239.7 244.26 168.12 220.4 259.88 260.44 206.82 240.43 204.15 259.7 186.91 212.71 266.78 242.68
243.72 233.41 233.84 165.59 211.64 255.34 221.46 235.07 242.43 197.07 155.45 245.62 251.73 249.53 231.18 235.91 164.89 215.05 248.54 249 203.52 232.74 196.8 252.66 181.58 206.59 254.36 233.41
234.7 225.37 228.87 162.85 208.88 246.4 218.01 230.29 235.82 188.86 150.41 235.71 244.06 240.82 225.89 228.49 162.69 212.51 240.43 240.61 200.06 224.8 188.72 243.66 176.5 201.86 253.94 225.37
237.68 231.19 230.44 164.7 209.44 257.75 218.9 231.73 238.43 194.85 153.75 242.5 251.82 245.57 227.87 232.1 164.9 214.88 246.53 246.67 202.08 228.02 194.38 253.08 179.27 203.84 258.27 231.19
250.8 242.26 242.11 169.91 214.82 264.35 227.77 242.77 251.28 202.96 161.48 252.61 256.55 256.32 238.87 245.49 168.72 218.82 258.63 259.05 206.31 239.51 203.63 259.66 186.12 212.37 264.29 242.26
241.05 232.53 234.82 167.48 212.35 251.21 221.52 236.25 241.96 198.09 157.67 244.43 248.1 247.45 232.12 234.57 166.82 216.79 246.57 247 204.97 232.55 197.66 249.63 183.59 207.91 255.42 232.53
Fig. 4. Change of ZTD in 2002.
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other. The coefficient between GPS ZTD and radiosonde PWV is up to 0.89. It can be concluded that GPS ZTD not only can be used to retrieve PWV, but also can be reflected the change of PWV. 4.2. Results of the comparison of precipitable water vapor between GPS and radiosonde In order to retrieve the change of GPS PWV, we should verify the reliability of GPS PWV solved with the data of CMONOC project. We calculated the ZTD of the CMONOC project from 190 day (July 9, 2002) to 199 day (July 18, 2002)2002, distilled the ZTD of SHAO station and computed the PWV with the pressure, temperature of the site. We also computed the PWV with the radiosonde data. We compared the PWV between GPS and radiosonde. Fig. 3 shows the result of the comparison. From ten days of data, the differences between radiosonde PWV and GPS PWV have a standard deviation of 4 mm in delay, difference of 0.24 mm in delay, and correlation coefficient of 0.94. So it can be concluded that the accuracy of GPS PWV is sufficed and the GPS PWV of CMONOC is reliable.
5. Seasonal change of GPS ZTD in the Chinese mainland from 2002 to 2004 We calculate the GPS ZTD with the data of CMONOC project. According to the GPS ZTD, we adopted grid method to linearize calculation and plotted the figures of seasonal GPS ZTD change of the Chinese mainland from 2002 to 2004. Table 1 is the GPS ZTD in Chinese Mainland from 2002 to 2004. Fig. 4, Figs. 5 and 6 are the GPS ZTD seasonal change of the Chinese mainland from 2002 to 2004. We analyze all the GPS ZTD changes from 2002 to 2004. The general trend of GPS ZTD change is that it is descended from Southeast coast to Northwest interior, which is identical to the distribution of Chinese annual amount of rainfall. The trend of GPS ZTD change is consistent with the change of atmospheric circumfluence in China. The Aleutian low pressure is significant in spring, autumn and especially in winter. During these seasons the Northern area of China is dominated by Mongolian high pressure, and there is large difference between Aleutian low pressure and Mongolian high pressure. So the atmosphere flows from the mainland to the Northern-pacific. From autumn to spring, the GPS ZTD in the Northern
Fig. 5. Change of ZTD in 2003.
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Fig. 6. Change of ZTD in 2004.
part is low, and in winter the ZTD is lowest. When the shoot-point of the Sun moves to the north, the Aleutian low pressure and Mongolian high pressure are diminished. Therefore the warm and damp airflow coming from the South substitutes the dry and cold airflow in the Northern China. The GPS ZTD ascends because the season-wind coming from the Southeast carries a lot of water vapor to most areas of China. From spring to summer, the GPS ZTD of the Middle and Northern area in China is high and it is the highest in summer. In autumn, the season-wind withdraws from the Northeast to the Southern China and the GPS ZTD of the Southeast descends. The ZTD of the Southern part are the highest in China, owning to the warm and damp airflow coming from the Southeast. Even so, in summer, due to the influence of typhoon, the GPS ZTD of the Southern coastland is the highest in the year. 6. Conclusions GPS is an effective method of monitoring the PWV change. It can demonstrate continuously the temporal and spatial distribution of the atmospheric water vapor. This study shows the seasonal change of the PWV in different areas in China. The general trend of the change of the GPS ZTD in China is that it is descended from the South-
east coast to the Northwest inland, which is identical to the distribution of China annual amount of rainfall. The GPS ZTD in Chinese mainland reaches maximum in Summer, and minimum in Winter. The values of GPS ZTD in the Southeast coast are the highest, and these in the Northwest interior are the lowest. References Bevis, M., Businger, S., Herring, T.A., et al. GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J. Geophys. Res. (97), 15787–15807, 1992. Duan, J., Bevis, M., Fang, P., et al. GPS meteorology: direct estimation of the absolute value of precipitable water. J. Appl. Meteorol. (35), 830–838, 1996. Elgered, G., Johansson, J.M., Ronnang, B.O., et al. Measuring regional atmospheric water vapor using the Swedish permanent GPS network. Geophys. Res. Lett. (24), 2663–2666, 1997. Emardson, T.R., Johansson, J., Elgered, G. The systematic behavior of water vapor estimates using four years of GPS observations. IEEE Trans. Geosci. Remote Sensing (38), 324–329, 2000. Galina, Dick, Gerd, Gendt, Christoph, Reigber, First experience with near real-time water vapor estimation in a German GPS network. J. Atmos. Solar Terr. Phys. 63, 1295–1304, 2001. Li, Zhenghang, Xu, Xiaohua, Luo, Jia, et al. Using GPS retrieved the distribution and change of the Zenith wet delay of Sanxia area. Geomat. Inform. Sci. Wuhan Univ. (4), 393–396, 2003. Li, Zhenghang, Huang, jinsong, GPS Surveying and Data Processing [M], Wuhan University Publishing Company, p. 3, 2005.
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