Estimates of precipitable water vapour from GPS data over the Indian subcontinent

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1 Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) Estimates of precipitable water vapour from GPS data over the Indian subcontinent Sridevi Jade a,, M.S.M. Vijayan a,d, V.K. Gaur a,b, Tushar P. Prabhu b, S.C. Sahu c a CSIR Centre for Mathematical Modelling and Computer Simulation, Bangalore, Karnataka 56 37, India b Indian Institute of Astrophysics, Bangalore, Karnataka 56 34, India c Central Seismological Observatory, Indian Meteorological Department, Shillong, India d Gandhigram Rural Institute, Deemed University, Gandhigram , India Received 26 April 24; received in revised form 3 December 24; accepted 8 December 24 Abstract Water vapour plays a dominant role in the high-energy thermodynamics of the atmosphere, notably, the genesis of storm systems. However, its distribution is difficult to resolve by conventional means, since water vapour exhibits very high spatial and temporal variability. The growing networks of continuously operating GPS systems, however, offer the possibility of estimating the integrated water vapour (IWV) or, equivalently precipitable water vapour (PW). These estimates constitute critical inputs in operational weather forecasting and fundamental research to model atmospheric storm systems, atmospheric chemistry, and the hydrological cycle. This paper presents the results of IWV estimates from GPS data from continuously operating GPS stations established by C-MMACS at Bangalore, Kodaikanal, Hanle and Shillong over the 3-year period (21 23). These are the first results of such an endeavor, towards the study of PW at four different geographical locations in the Indian subcontinent. r 25 Elsevier Ltd. All rights reserved. Keywords: Integrated water vapour (IWV); GPS geodesy; Tropospheric delay; GPS meteorology and weather forecast 1. Introduction Corresponding author. Tel.: ; fax: address: sridevi@cmmacs.ernet.in (S. Jade). Water vapour distribution in the atmosphere is of central importance in several ways: it plays a major role in the balance of planetary radiation; it influences and responds to atmospheric motions; and it plays a key role in many aspects of atmospheric processes that act over a wide range of spatial and temporal scales. In view of the potential effects on climate change, it is especially important to assess and attempt to understand longterm changes and decadal scale trends of the atmospheric water vapour regime (Jacob, 21). Because water molecules absorb microwaves and other radio wave frequencies, water in the atmosphere attenuates radar signals. In addition, atmospheric water reflects and refracts signals to an extent depending on whether it is vapour, liquid or solid. These properties of atmospheric water vapour are utilised by a number of systems to determine its spatio-temporal distribution, each device having its own characteristics with specific advantages and shortcomings. Amongst a variety of approaches to address this problem including the highresolution ground and space-based microwave radiometers, Raman Lidars, infrared sensors, microwave sensors and radiosonde observations, that of using phase delays of signals received at a precisely located /$ - see front matter r 25 Elsevier Ltd. All rights reserved. doi:1.116/j.jastp

2 624 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) site from global positioning system satellites (GPS), offers certain attractive advantages, notably of all weather coverage and cost effectiveness. A number of studies have shown that precipitable water vapour (PW) estimates from ground-based GPS observations and meteorological data give the same level of accuracy as radiosondes and microwave radiometers. GPS signals, like all electromagnetic waves are slowed down by the earth s atmosphere, resulting in a delay in the arrival time of the received signal. This delay is caused by induced dipole moment of the dry atmosphere (hydrostatic delay) and the permanent dipole moment of water vapour (wet delay). The hydrostatic delay contribution is about 9% of the total tropospheric zenith range delay, amounting to 2:3 m for an average ground atmospheric pressure of 113 mbar; and can be estimated with an accuracy of.2% from surface meteorological data. The wet component, on the other hand, is determined by the distribution of atmospheric parameters all along the signal path, not necessarily well correlated with surface conditions, particularly, the highly variable water vapour. The total tropospheric delay measured by a GPS receiver from all satellites in view, is eventually mapped to the vertical using a mapping function, to yield the combined zenith total delay (ZTD). High-precision GPS data processing software enables one to estimate the ZTD, after accounting for all the other contributions arising from ionospheric refraction, orbital accuracy, antenna phase center variations, signal multi-path and scattering by the neighbourhood environment of the receiver. The wet path delay is roughly proportional to the integrated water vapour (IWV) content along the signal path (Hogg et al., 1981; Askne and Nordius, 1987). Improved atmospheric models coupled to the analyses of GPS data should progressively yield more accurate estimates of PW for use in climatological studies (long term) and weather forecasting (short term) (Bock and Doerflinger, 2; Gradinarsky et al., 22). A brief account of water vapour estimation over the Indian subcontinent is given below. The global energy and water cycle experiment (GEWEX) program, launched by the World Climate Research Program, set out to acquire the vertical structure of atmospheric water vapour distribution using data from HIRS instrument on board the NOAA satellites. The water vapour fields thus obtained for the summer months of 1987 and 1988 were compared with precipitation figures over India and Southeast Asia. Moderate resolution imaging spectro-radiometer (MODIS) and airborne visible infrared imaging spectrometer (AVIRIS) were used to retrieve the column amount of atmospheric water vapour over parts of the Indian subcontinent, the Indian Ocean, Nepal and parts of the Tibetan Plateau. The images obtained show that column water vapour (CWV) values vary significantly with surface elevations. An analysis and comparison of satellite-observed cloud cover and water vapour at Hanle, India and Yanbajing, Tibet was carried out to select a site for installing the next-generation imaging atmospheric Cherenkov telescopes. Comparison of water vapour derived from ECMWF (European Center for Medium-Range Weather Forecasts) and NCEP (National Centers for Environmental Prediction) reanalyses and satellite observations from the NVAP (NASA Water Vapour Project) data set show that the main differences in variability were observed in the tropics. CWV was also deduced from IRS P4 OCM data over the land and ocean after the Gujarat earthquake to analyse the changes in the CWV that may have been associated with the earthquake. Dey et al. (23), used TRMM (tropical rainfall measuring mission) data to retrieve water vapour content over land regions and SSM/I (special sensor microwave imager) data over the sea in Gujarat to investigate anomalous changes if any, registered in the CWV, after the Gujarat earthquake. Global distribution of atmospheric water vapour (Mukai and Sano, 23) has been estimated from ADEOS (advanced earth observing satellite)/polder (polarization and directionality of earth reflectances) data obtained from a POLDER sensor, mounted on the Earth observation satellite ADEOS in The results thus obtained over eight months from November 96 to June 97, clearly bring out seasonal changes associated with the Indian monsoon. In this paper, an attempt has been made for the first time to estimate PW over four stations in the Indian subcontinent, using GPS data. 2. IWV from tropospheric delay GPS-derived total path delay which is due to the delay in signal arrival, can be expressed as (Bevis et al., 1992, 1994) DL ¼ DL z h M hðyþþdl z w M wðyþ, (1) where DL z h is Zenith hydrostatic delay (ZHD), DLz w is Zenith wet delay (ZWD), y is satellite elevation angle, M h ðyþ is hydrostatic mapping function and M w ðyþ is wet mapping function. Saastamoinen (1972) showed that the ZTD can be expressed as the sum of ZHD and ZWD, the former in millimetres being modelled as (Elegered et al., 1991) ZHD ¼ð2:2779 :24ÞP s =ð1 :266 cos 2l :28HÞ, ð2þ where P s is the total pressure (in millibars) at the earth s surface, l is the latitude and H is the height above the ellipsoid (in kilometres). The ZWD is modelled as Z Z ZWD ¼ 1 6 k 2 dz þ k 3 pv T p v T 2 dz, (3)

3 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) Table 1 Mean atmospheric temperature models Model Bevis et al. (1992, 1994) Eq. (7) Mendes et al. (2) Eq. (8) Solbrig (2) Eq. (9) Harmonic model Schueler et al. (21), Eq. (1) Linear surface temperature model Schueler et al. (21), Eq. (11) Mixed harmonic model Schueler et al. (21), Eq. (12) Mean temperature T M ¼ 7:2 ð KÞþ:72T ð KÞ T M ¼ 5:4 ð KÞþ:789T ð KÞ T M ¼ 54:7 ð KÞþ:77T ð KÞ T M ¼ T M þ ~T M cos 2p DoY DoY w 365:25ðdaysÞ T M ¼ 86:9 ð KÞþ:647T ð KÞ T M ¼ T M þ ~T M cos 2p DoY DoY w þ q 365:25ðdaysÞ T T where P v is the partial pressure of water vapour (in millibars), T is the atmospheric temperature (in degrees Kelvin), k 2 ¼ð17 1Þ K mbar 1 and k 3 ¼ð3:776 :4Þ1 5 K 2 mbar 1 ; the integral to be carried out along the Zenith path, and the delay given in units of z (Davis et al., 1985; Davis, 21). PW vapour (in millimetres) of the atmosphere is defined as the height of an equivalent column of liquid water. Numerically IWV in units of kg=m 2 is just the product of r and PW, where r is the density of water. Accordingly, PW and IWV are expressed as follows: PW ¼ K ZWD where K ¼½1 6 ðk 3 =T m þ k 2 ÞR vrš 1 (4) IWV ¼ r PW, (5) where R v is the gas constant for the water vapour, and T m is the weighted mean temperature of the atmosphere (Davis et al., 1985) given by R ðpv =TÞ dz T m ¼ R ðpv =T 2 Þ dz. (6) As a rough guide, the ratio PW/ZWD is :15 (Bevis et al., 1994), the actual value varying by as much as 2% i.e. the ratio of PW/ZWD may lie between.12 and.18 based on location, altitude, season and weather. The best possible accuracy in the estimation of PW and IWV from the observed ZWD can be achieved if the constant K in Eq. (4) is estimated using a value of T m that is tuned to the specific area and season. Mendes et al. (2) evaluated the accuracy of models (Bevis et al., 1992, 1994; Mendes et al., 2; Emardson and Derks, 2) for the determination of the weighted mean temperature of the atmosphere. It was concluded that regionally optimized models do not provide superior performance compared to global models. Schueler et al. (21) made a comparative study of several numerical models which give the mean atmospheric temperature for GPS water vapour estimation as a function of the dry surface temperature obtained from NCEP data. Based on a comparative study of the existing models (Bevis et al., 1992, 1994; Mendes et al., 2; Solbrig, 2; Emardson and Derks, 2) three new models were proposed: the Harmonic model, the linear surface temperature model and the mixed harmonic temperature model. Table 1 gives a brief outline of these models for abstracting the mean atmospheric temperature. In Table 1, T is the surface temperature in ð KÞ and T M is the mean temperature of the atmosphere in ð KÞ; T M is the average mean atmospheric temperature, ~T M is the amplitude of mean temperature, DoY is day of the year and DoY W day of the maximum winter, q T is the temperature amplifier. Site-specific coefficient q T for harmonic and mixed harmonic model for 335 GPS sites is given in In the theory outlined above, it is implicitly assumed that the wet delay is entirely due to water vapour and that liquid water and ice does not contribute significantly to the wet delay (Duan et al., 1996). This approximation works fine for almost all cases except when large quantities of wet snow are present in the air column over a GPS site, albeit this may happen only infrequently. PW vapour is finally derived from the ZTD obtained from the analysis of GPS data assuming an atmospheric model constrained by the surface pressure and temperature values at the ground surface. 3. IWV estimation at Indian GPS stations C-MMACS scientists, in collaboration with scientists of the host institutions have established 14 permanent GPS stations (Fig. 1) in different parts of the country:

4 626 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) Fig. 1. C-MMACS permanent GPS Network along with IGS stations. Bangalore, Kodaikanal, Bhopal, Almora (UP), Leh and Hanle in Ladakh, and eight others in northeastern India. These stations form part of the national network of GPS stations sponsored by the Department of Science and Technology for earthquake hazard assessment studies. All these stations are slated to be equipped with meteorological packages within the next one year so that water vapour estimations at these sites can be routinely made for research and development of potentially operational frameworks for real-time assimilation in meteorological data for numerical weather prediction. At present, contemporaneous meteorological data for such estimations are available only at four sites: Bangalore, Kodaikanal, Hanle and Shillong. Accordingly, available daily mean data at these sites for the years 21 23, have been used to study the variability of water vapour across a wide region from the temperate Bangalore to the high-altitude site at Hanle where the Indian Institute of Astrophysics recently established a high technology optical and infra-red telescope, and where, according to some hypotheses, the elevated heat source responsible for the intensification of the monsoon system lies. Details of the four GPS sites used for water vapour estimation are given in Table 2. Whilst the Bangalore, Kodiakanal and Hanle GPS sites were operational for all the 3 years, the Hanle site was operational for few days in all the months of 21 and the Shillong site was established in the 22. Observed surface pressure and temperature data were also available for the Bangalore and Hanle GPS sites for all the 3 years whereas for Kodaikanal, the meteorological data were available only for a few months in 22. The analysis presented here was therefore carried out for 3 years at Bangalore and Hanle, 2 years at Shillong and 5 months at Kodaikanal. Zenith total atmospheric delay at the above sites (Table 2) were obtained from the analysis of GPS data using GAMIT/GLOBK 1.5 data processing software along with the IGS (International GPS Service) sites Pol2, Kit3, Sele, Lhas (Fig. 1). After accounting for contributions to signal path delay from

5 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) Table 2 GPS sites used for PW estimation Site Code Latitude Longitude Height Data Remarks degrees degrees (m) used Bangalore IISC IGS site Hanle HNLE High altitude 22 site 23 Shillong CSOS Established in Kodaikanal KODI Meteorological data available only for five months ionospheric refraction, orbital accuracy, antenna phase centre modelling, signal multi-path and scattering by the receiver environment, the residual delay was modelled to yield the ZTD, assuming all of it to have been introduced by the neutral atmosphere. These have been compared with the ZTD at the IGS sites hosted on the SOPAC/CSRC archive ( Fig. 2a and b shows the plot of the ZTD values obtained by us against the IGS ZTD values for the IISC GPS site for the year 22. Chi-square values obtained by taking IGS ZTD values (Fig. 2a) as expected, and ZTD values from our analysis as observed, is.316 which is well below the critical value of chi-square of with 99.5% confidence and five degrees of freedom. It can also be seen from Fig. 2b that the difference between the expected and observed values fall within the band of :3 m: The ZTD values derived from our analysis are thus in good agreement with those obtained by IGS for all the IGS stations used in our analysis. The Zenith total atmospheric delay obtained from these analyses for the four GPS sites (Table 2) were used to derive the ZWD and PW in mm and IWV in kg=m 2 through Eqs. (2) (6),, using the surface temperature and pressure values to constrain the atmospheric model. The ratio of derived values of PW/ZWD is found to be.165 at Bangalore,.163 at Shillong,.14 at Hanle and.157 for Kodaikanal which compare well with the value of :15 2% given by Bevis et al. (1994). IWV estimated at Bangalore with observed temperature and pressure values has been compared (Fig. 3) with NCEP IWV values and IWV determined using NCEP meteorological data (ftp://ftp.cdc.noaa.gov/datasets/ ncep.reanalysis.dailyavgs/). Whilst the trends in the variation of IWV (Fig. 3a) over the year are similar, the actual IWV values (Fig. 3b) vary significantly which is expected as the NCEP meteorological data are not as accurate as the measured pressure and temperature values at the site. This indicates that better accuracies can be achieved using actual meteorological data. Comparison of IWV values estimated using different mean temperature models (Eqs. (6) (12)) is given in Figs. 4 and 5. It can be seen from Fig. 4 that the various mean temperature models, yield more or less the same values of IWV for all the sites. Also, the deviation from the mean (Fig. 5) for all the seven models falls in the band of 1kg=m 2 for IISC, :7kg=m 2 for Shillong and :5kg=m 2 for Hanle. So, all the seven models give the same IWV values for all the sites which are widely spread geographically over Indian subcontinent. The time series of the estimated IWV values using GPS and met data is given in Fig. 6. The annual variation of water vapour based on Indian seasons is clearly seen for the Bangalore site with a peak during the monsoon season. For the Hanle site, seasonal variation of monsoon is not that prominent as it is a high altitude site. Fig. 7 shows the monthly averages of IWV for all the four sites. As expected, the atmospheric water vapour over Bangalore is higher than at the other three sites at higher altitudes, and continues to be high even in October. Interannual variability between the 3 years (Fig. 8) shows that IWV in most of the months of the lean monsoon year 22 is consistently lower, although not low enough as one might expect from the drastic reduction of the monsoon rain fall that year. Fig. 9 shows the correspondence between ground humidity and the IWV estimated from GPS data, which is virtually inphase. The ground humidity value is of course twice as much as the estimated PW, which qualitatively appears to be about right. Results show that the variation of water vapour content from November to June is kg=m 2 at Bangalore, 5 12 kg=m 2 at Hanle and 1 35 kg=m 2 at Shillong which reflects the transition

6 628 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) ZTD(IGS) ZTD(Computed) 2.35 ZTD at IISC (kg/m 2 ) Day of the Year (22).1 ZTD(Computed) - ZTD(IGS).5 Difference in ZTD (m) Day of the Year (22, IISC) Fig. 2. Comparison of Zenith total delay (ZTD) from SOPAC/CSRC archive and the ZTD computed for IISC site for year 22 actual values and difference. from dry to wet season in India at those geographical locations. At Hanle, estimates of water vapour are a little higher than the expected value of 5 kg=m 2 ; during the winter months which may be due to the extreme cold weather. 4. Discussion and conclusion GPS-derived IWV estimation at four GPS sites geographically spread across the Indian subcontinent (Figs. 6 9) show the variability of water vapour across

7 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) IWV(using NCEP met. data) IWV(using observed met. data) IWV(modeled by NCEP) 4 IWV at IISC (kg/m 2 ) Day of the Year (22) 3 IWV(NCEP model) -IWV(Observed met. data) IWV(NCEP met data) - IWV(Observed met data) 2 Difference in IWV (kg/m 2 ) Day of the Year (22, IISC) Fig. 3. Comparison of IWV obtained using observed values of temperature and pressure with the NCEP values at Bangalore GPS site for year 22 actual values and difference. the sites with Bangalore having the highest value, Hanle the lowest, and Shillong and Kodaikanal having intermediate values, each corresponding well with its geographical location. Water vapour variations over the year for all the 3 years roughly correspond to the Indian monsoon period with December March being the dry season and June October the peak monsoon period, and the intervening months marking a transitional period. The interannual variability of IWV over the 3 years roughly corresponds to the Indian monsoon intensity

8 63 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) IWV at IISC (kg/m 2 ) Deviation in IWV (kg/m 2 ) 4 Dev. in IWV(Tm-Equation6) Dev. in IWV(Tm-Equation7) Dev. in IWV(Tm-Equation8) Dev. in IWV(Tm-Equation9) Dev. in IWV(Tm-Equation1) 2 Dev. in IWV(Tm-Equation11) Dev. in IWV(Tm-Equation12) Day of the Year (22) Day of the Year(22, IISC) IWV at HNLE (kg/m 2 ) Day of the Year (22) 5 Deviation in IWV (kg/m 2 ) 4 Dev. in IWV(Tm-Equation6) Dev. in IWV(Tm-Equation7) Dev. in IWV(Tm-Equation8) Dev. in IWV(Tm-Equation9) Dev. in IWV(Tm-Equation1) 2 Dev. in IWV(Tm-Equation11) Dev. in IWV(Tm-Equation12) Day of the Year (22, HNLE) IWV at CSOS (kg/m 2 ) (c) Day of the Year (23) Fig. 4. GPS derived IWV estimates using different mean temperature models for Bangalore (22), Hanle (22) and (c) Shillong (23). Deviation in IWV (kg/m 2 ) (c) 4 Dev. in IWV(Tm-Equation6) Dev. in IWV(Tm-Equation7) Dev. in IWV(Tm-Equation8) Dev. in IWV(Tm-Equation9) Dev. in IWV(Tm-Equation1) 2 Dev. in IWV(Tm-Equation11) Dev. in IWV(Tm-Equation12) Day of the Year (23, CSOS) Fig. 5. Deviation of estimated IWV values from the mean Bangalore (22), Hanle (22) and (c) Shillong (23). with 22 being the lean one. GPS derived IWV values presented here, are the first such determination over the Indian subcontinent. Whilst these results are still far from providing the vertical profile of water vapour in the atmosphere, that needs further developments and modelling to make

9 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) IWV at IISC (kg/m 2 ) Day of the Year(21-23) 2 IWV at HNLE (kg/m 2 ) Day of the Year(22-23) Fig. 6. Time series of estimated IWV using GPS data at Bangalore (21 23), and Hanle (22 23). water vapour tomography possible, currently available approaches as demonstrated above can yield accurate and reliable estimates of vertically integrated IWV above a GPS antenna site using, all weather, inexpensive GPS receivers capable of being deployed widely. Fourdimensional assimilation of IWV estimates when available from widely spread GPS stations, into a meso-scale model, have the potential of greatly offsetting the uncertainties of meteorological forecasts (Baker et al., 21), by creating continually updated initial state models. This, however, necessitates real time or near real-time availability of IWV estimates which, in turn,

10 632 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) IISC21 4 IWV (kg/m 2 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC MONTHS (21) IISC22 HNLE22 KODI22 CSOS22 IWV (kg/m 2 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC IISC23 HNLE23 CSOS23 MONTHS (22) IWV (kg/m 2 ) (c) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Day of the Year (23) Fig. 7. Monthly average of IWV values for the four GPS sites 21, 22 and (c) 23.

11 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) IISC21 IISC22 IISC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC MONTHS HNLE22 HNLE JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC MONTHS Fig. 8. Interannual variability of GPS derived IWV estimates for Bangalore (21 23), and Hanle (22 23). require installation of meteorological sensors at all the GPS sites to measure the surface pressure and temperature to desirable accuracy. With consistent data analysis in terms of methods and models, ground-based GPS will, as the length of the time series grows, become an independent data source in climate monitoring. Meanwhile, simulation experiments designed to assess the quality improvement of forecasts when GPS derived IWV data are incorporated, may generate insightful ideas as to how best one may fruitfully exploit the potential possibilities. Acknowledgements This work would not have been possible without the consistent support of Dr. Gangan Prathap, Head C- MMACS, and spontaneous help from Dr. S.K. Srivastav and Dr. S.K. Subramanian of IMD, Delhi and Dr. K.S. Hosalikar of IMD, Shillong. We have benefited a great deal from technical discussions with Dr. N. K. Indira of C-MMACS. We also acknowledge the technical help rendered by Mr. B.C. Bhatt and Dr. S.S. Gupta of Indian Institute of Astrophysics. We also

12 634 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) IWV at IISC (kg/m 2 ) IWV at HNLE (kg/m 2 ) IWV at CSOS (kg/m 2 ) (c) would like to thank the reviewers for their valuable comments which greatly helped us to improve the quality of this paper. References Relative Humidity (%) Day of the year(21-23) RelativeHumidity(%) Day of the Year (22-23) RelativeHumidity(%) Day of the Year (23) 1 Askne, J., Nordius, H., Estimation of tropospheric delay for microwaves from surface weather data. Radio Science 22, Fig. 9. Time series of GPS derived IWV estimates and ground humidity for Bangalore (21 23), Hanle (22 23) and (c) Shillong (23) Relative Humidity at IISC (%) Relative Humidity at HNLE (%) Relative Humidity at CSOS (%) Baker, H.C., Dodson, A.H., Penna, N.T., Higgins, M., Offiler, D., 21. Ground-based GPS water vapor estimation: potential for meteorological forecasting. Journal of Atmospheric and Solar-Terrestrial Physics 63, Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R.A., Ware, R.H., GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. Journal of Geophysical Research 97 (D14), Bevis, M., Businger, S., Chiswell, S., GPS meteorology: mapping zenith wet delays on to precipitable water. Journal of Applied Meteorology 33, Bock, O., Doerflinger, E, 2. Atmospheric processing methods for high accuracy positioning with GPS. Part A: Cost Action 716 workshop Towards operational GPS meteorology. book/bock_doerf.pdf Davis, J.L., 21. Atmospheric water vapor signals in GPS data: synergies, correlations, signals and errors. Physics and Chemistry of the Earth 26, Davis, J.L., Herring, T.A., Shapiro, I.I., Rogers, A.E., Elgered, G., Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Science 2, Dey, S., Sarkar, S., Singh, R.P., 23. Anomalous changes in column water vapor after Gujarat earthquake. Advances in Space Research 33, Duan, J., Bevis, M., Fang, P., Bock, Y., Chiswell, S., Businger, S., Rocken, C., Solheim, F., Van Hove, T., GPS meteorology: direct estimation of the absolute value of precipitable water. Journal of Applied Meteorology 35, Elegered, G., Davis, J.L., Herring, T.A., Shapiro, I.I., Geodesy by radio interferometry: water vapor radiometry for estimation of the wet delay. Journal of Geophysical Research 96, Emardson, T.R., Derks, H.J.P., 2. On the relation between the wet delay and the integrated precipitable water vapor in the European atmosphere. Meteorological Applications 7, Gradinarsky, L.P., Johansson, J.M., Bouma, H.R., Scherneck, H.G., Elgered, G., 22. Climate monitoring using GPS. Physics and Chemistry of the Earth 27, Hogg, D.C., Guiraud, F.O., Decker, N.T., Measurement of excess transmission length on earth space paths. Astronomy and Astrophysics 95, Jacob, D., 21. The role of water vapor in the atmosphere. A short overview from a climate modelers point of view. Physics and Chemistry of the Earth 26 (6 8), Mendes, V.B., Prates, G., Santoa, L., Langley, R.B., 2. An evaluation of the accuracy of models for the determination of the weighted mean temperature of the atmosphere. Proceedings of ION 2, National Technical Meeting, Anaheim, CA, USA, pp Mukai, S., Sano, I., 23. Global distribution of atmospheric water vapor. Science and Technology, Kinki University 15, Saastamoinen, J., Atmospheric correction for the troposphere and stratosphere in radio ranging of satellites. In: Henriksen, S. W., et al. (Ed.), Geophysical Monograph Series, vol. 15, American Geophysical Union, pp

13 S. Jade et al. / Journal of Atmospheric and Solar-Terrestrial Physics 67 (25) Schueler, T., Posfay, A., Hein, G.W., Biberger, R., 21. A global analysis of the mean atmospheric temperature for GPS water vapor estimation. C5: atmospheric effects, IONGPS21 14th International Technical Meeting of Satellite Division of the Institute of Navigation, Salt Lake City, Utah. Solbrig, P., 2. Untersuchungen uber die Nutzung numerischer Wettermodelle zur Wasserdampfbestimmung mit Hilfe des Global Positioning Systems. Diploma Thesis, Institute of Geodesy and Navigation, University FAF Munich, Germany.