Precipitable water vapour estimation using the permanent single GPS station in Zanjan, Iran

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1 METEOROLOGICAL APPLICATIONS Meteorol. Appl. 24: (2017) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: /met.1639 Precipitable water vapour estimation using the permanent single GPS station in Zanjan, Iran Saeed Abbasy, a * Madjid Abbasi, a Jamal Asgari b and Abdolreza Ghods c a Surveying Engineering Department, University of Zanjan, Iran b Surveying Engineering Department, University of Esfahan, Iran c Department of Earth Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran ABSTRACT: Meteorological investigations using the global positioning system (GPS) are based on expensive permanent networks and they are not developed globally on the Earth. In this study it is confirmed that single station GPS meteorology is feasible where there is no possibility for development of a sophisticated dense GPS network. Since 1 January 2011 a GPS station has been installed in the Institute for Advanced Studies in Basic Sciences in the province of Zanjan, Iran, where upper air meteorological data are not available. The GPS data were processed in order to estimate the zenith total delay (ZTD) of GPS signals due to the troposphere. The estimated ZTD was then transformed to precipitable water vapour (PWV) using the ERA-Interim globally available humidity and temperature vertical profiles. Three kinds of validation were applied to the estimated PWV and all of them reasonably proved the validity of the GPS results: (1) the measured surface water vapour pressure and dew point temperature show 87.8 and 86.6% correlation respectively with the estimated PWV; (2) the PWV measured using radiosondes in three neighbouring cities of Zanjan (Tabriz, Tehran and Kermanshah) with nearly the same climatic regime show 81.1, 71.7 and 66.4% correlation respectively with the GPS PWV time series, and (3) the global reanalysis datasets for Zanjan show 89.2% correlation with the GPS results. These validations indicate that, in the absence of permanent GPS networks, if proper data processing strategies are adopted the low cost single station GPS meteorology can be considered as a possibility for meteorological monitoring. KEY WORDS single station GPS meteorology; precipitable water vapour; radiosonde measurements; water-vapour-weighted mean temperature Received 3 March 2016; Revised 30 September 2016; Accepted 8 November Introduction Water vapour is an efficient greenhouse gas and is an indicator of global warming because warmer air will hold more water vapour (Elgered et al., 1997). Precipitable water vapour (PWV) is an indicator of water vapour quantity in the atmosphere. Therefore, having a precise measure of PWV can improve the monitoring/evaluation of climate and agricultural meteorology (Revuelta et al., 1985). Quantitative precipitation forecasting, determination of horizontal moisture flux, radiation budget studies and probable minimum temperature are some of the areas in which a knowledge of the PWV is useful (Tuller, 1977). Also, having a measure of this parameter is needed in hydrometeorological and electromagnetic wave propagation studies (Reber and Swope, 1972) and is important in the objective prediction of clouds (Viswanadham, 1981). Information on PWV can be used as a constraint in numerical weather prediction (NWP) data assimilation or serves as an independent data source to validate NWP models and is a useful tool when searching for trends in climate change (Emardson and Derks, 2000). Some applications of PWV are also explained by Hong et al. (2015). There are several methods for the determination of PWV. They can be divided into satellite based (space to Earth) and ground based (Earth to space). The next paragraph introduces some ground based methods. * Correspondence to: S. Abbasy, Surveying Engineering Department, Faculty of Engineering, University Blvd., , University of Zanjan, Zanjan, Iran. saeed_abbasy@znu.ac.ir The most widely used instruments are radiosondes. These systems measure several meteorological parameters such as temperature, relative humidity, pressure, dew point temperature, mixing ratio and wind direction at various heights, which result in vertical profiles and lead to considerable measurement accuracy. However, the distribution of radiosondes on the Earth is relatively sparse which results in low spatial resolution. Furthermore, because of high measuring costs, radiosondes are mostly used twice daily which results in low temporal resolution. Due to these limitations, some effort has been made to estimate PWV by other methods, e.g. Sun photometers, infrared radiometers (Clay et al., 1998; Brooks et al., 2007; Maghrabi et al., 2009; Maghrabi and Clay, 2010), ground-based microwave radiometers and light detection and ranging (LIDAR) systems (Gerding et al., 2004; Kuwahara et al., 2008). Using satellites in meteorology dates back to the early 1960s with the launch of the TIROS-1 satellite. Since then, numerous satellites carrying meteorological sensors have been launched. PWV obtained by the space to Earth method has the great advantage of global coverage. However, depending on the orbital height of the satellites, the spatial and temporal resolutions of the data are not sufficiently high (Seeber, 2003). Limitations in the accuracy of humidity observations, as well as in temporal and spatial coverage, often cause failures in short-term forecasts, in particular concerning clouds and precipitation (Yang et al., 1999). PWV can be evaluated by using a global navigation satellite system (GNSS) of which there are three main systems, the global positioning system (GPS), GLONASS and Galileo. This system 2017 Royal Meteorological Society

2 416 S. Abbasy et al. works for almost any weather and any time. The troposphere induces a delay on the transmitted signal from satellite to ground receiver. This delay is an important error source for positioning, while for meteorology it contains information from the water vapour content of the signal trajectory. The GPS network (if available) can provide much higher spatial resolution than other meteorological networks (Means, 2013). A great advantage of GNSS meteorology is the possibility of a high measurement sampling rate. A permanent GNSS network can provide near continuous, accurate, all-weather, real-time moisture data that can help research in mesoscale modelling and data assimilation, severe weather, precipitation, cloud dynamics, regional climate and hydrology (Ware et al., 2000). Therefore, GPS-sensed PWV data can be used to improve storm system analysis (Rocken et al., 1995; Businger et al., 1996). In the following, a brief historical overview of GPS meteorology is presented. Bevis et al. (1992) presented a new theoretical approach in remote sensing of the troposphere with the GPS. Then, in a practical experience, Rocken et al. (1993) in Colorado installed at the same time two GPS receivers and two water vapour radiometers (WVRs) at the ends of a 50 km baseline and observed just about 1 mm difference in relative PWV evaluated from the WVR and the GPS. They also carried out the GPS/STORM experiment in Duan et al. (1996) estimated the absolute PWV without using a WVR with a mm root mean square error. Elgered et al. (1997), using 4 days of data of a GPS network in Sweden, studied the detailed motion of the air mass system. They showed that the estimated water vapour from the GPS agrees with the data of the radiosonde and WVR to within 1 mm root mean square. Rocken et al. (1997), Emardson et al. (1998), Fang et al. (1998), Peter et al. (1999) and Niell et al. (2001) carried out similar studies and obtained satisfactory results about the applicability of the GPS in PWV estimation. Increasing the amount of estimated PWV by the GNSS method led to its inclusion in weather forecasting models. Bennitt and Jupp (2012) have operationally assimilated GPS zenith total delay (ZTD) observations in the Met Office s North Atlantic and European NWP model since Means (2013) used GPS estimated PWV for the period for over 500 sites as a diagnostic of the North American monsoon in California and Nevada and showed that the southern California deserts experience a large increase in atmospheric water vapour associated with the North American monsoon. These kinds of studies generally need a permanent GPS network which is usually installed for geodynamics purposes and the data are processed by methods known as differential methods. The cost of installation, maintenance and computations of such networks is very high. Therefore, such networks are not available everywhere on the Earth. The aim of this study was to introduce a single station strategy in GPS meteorology in the regions where GPS networks are not installed. In January 2011, a single permanent GPS station was installed in the Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran. Zanjan is situated on a plateau in the northwest of Iran at N, E geographical co-ordinates with a mean altitude of about 1700 m in the southwest of the Caspian Sea (Figure 1). In this region of Iran the climate variability is very high: the winters are cold with generally heavy snowfall and subfreezing air temperatures during December, January and February (recorded absolute minimum is 30 C). Spring and autumn are relatively mild, while summers are dry and hot (recorded absolute maximum is +40 C). The mean annual temperature is 11 C. The climate in the northwest of Iran can be classified as Csa Mediterranean climate, in the Köppen Geiger climate 2017 Royal Meteorological Society Figure 1. Geographical positions of radiosonde stations in Tabriz, Kermanshah and Tehran. A permanent GPS station is situated at about 1800 m altitude in Zanjan which is considerably influenced by humid air masses coming from the Caspian Sea. classification (Farajzadeh and Matzarakis, 2009). Rain occurs with a mean annual rainfall of about 300 mm. The humidity of the Zanjan region is influenced by the south Caspian Sea humid climate, by dry air masses originating from southern Arabian regions and by humid air masses of Atlantic and Mediterranean origin. The Islamic Republic of Iran Meteorological Organization (IRIMO) is responsible for meteorological data measurement and management in Iran. Their surface meteorological records are relatively dense while upper air measurements like radiosondes and radiometers are very sparse in the region (Figure 1). The advantage of the newly established IASBS GPS station is that it gives valuable observations of the troposphere and ionosphere in Zanjan which can improve our knowledge about the dynamics of the atmosphere in this region considerably. In this study the process of extraction of PWV from a single GPS station was investigated and then the results were validated with PWV estimations from other methods. In Section 2, the datasets used in this research are introduced. Section 3 gives an overview of the precise point positioning (PPP) method used for GPS data processing and presents the computation procedure for PWV. In Section 4 the results of numerical investigations and their related validations are presented. The last section is devoted to the conclusion. 2. Data Six kinds of data were used in the present study, as outlined below. 1. The data from the GPS permanent station of IASBS. This station uses a dual frequency Trimble 5700 II receiver with Zephyr geodetic antenna. The receiver can observe code observables C1 and P2 and phase observables on L1 and Meteorol. Appl. 24: (2017)

3 Precipitable water vapour estimation using single GPS station 417 L2 carriers. The observation rate is set to 10 s. In this study observations from 1 January 2011 to 20 July 2014 were used. 2. Pressure data collected in the IASBS meteorology station at 2 min intervals. 3. ERA-Interim data (Dee et al., 2011) which is a global atmospheric reanalysis from 1979, continuously updated in real time, furnished by the European Centre for Medium-Range Weather Forecasts (ECMWF). The data are available from levtype=pl/. The data used from ERA-Interim in this research were relative humidity, temperature and geopotential at 37 pressure levels. 4. Pressure, air temperature, dew point temperature and water vapour pressure data from Zanjan synoptic station managed by IRIMO. 5. PWV time series extracted from observations made in Tabriz, Tehran and Kermanshah radiosonde stations. The data are available from the website of the Department of Atmospheric Science of the University of Wyoming ( 6. PWV time series from global reanalysis datasets available from reanalysis/. This website corresponds to the Physical Sciences Division of the Earth System Research Laboratory of the National Oceanic and Atmospheric Administration. 3. Methodology 3.1. GPS data processing The difference in observed distance and geometric distance is mainly due to the tropospheric delay of the GPS signals. This is the concept of GPS meteorology. The main step in single station GPS meteorology is therefore to compute the antenna position with high accuracy. Single station positioning with fixed precise orbit solutions and Doppler satellite observations was first introduced by Anderle (1976), who named the method PPP (Kouba and Héroux, 2001). In this method, high co-ordinate precisions of about 1 cm are achievable with dual frequency receivers with suitable antennas and a sufficiently high sampling rate with less than 24 h of continuous observations (Zumberge et al., 1997; Gao, 2006). Precise satellite orbits and clocks for point positioning are available from the International GNSS Service and related websites. The procedure of computation of point co-ordinates in the static PPP method is briefly explained in the next steps. 1. Code observations are smoothed using phase measurements. 2. Ionospheric free linear combinations of code and phase observations are computed. 3. Precise satellite orbit and clock data are introduced as known parameters into observation equations. The remaining unknowns are now three point co-ordinates in the Earth centred Earth fixed (ECEF) reference frame as well as one receiver clock bias, one phase ambiguity and slant tropospheric delays (STDs) for each satellite in every epoch. In order to overcome the singularity problem due to multiple STDs, a mapping function is needed to convert STDs to a single ZTD for each epoch. 4. The six unknowns of the previous step are estimated by the least squares method for each observation epoch. 5. Estimated unknowns are used as initial values for the next step estimation procedure. GPS data were processed by GPS Software (GPSS). This software was developed by Asgari (2005) for PPP GPS data processing. It uses raw data in Receiver INdependent EXchange (RINEX) format and precise ephemeris. It includes additional visualizations and data quality control tools. The ZTD can be estimated by the PPP method together with absolute positioning Transferring ZTD to PWV The ZTD estimated from the GPSS in the previous section was transformed to PWV in two steps. The zenith hydrostatic delay (ZHD) is subtracted from ZTD to obtain ZWD. ZHD is calculated using the hydrostatic component of the Saastamoinen tropospheric delay model (Andrei and Chen, 2009): ZTD = ZHD + ZWD (1) and ZHD = P g 0 (2) g = cos 2φ h 0 (3) In these equations, g (m s 2 ) is standard gravity, φ is geodetic latitude, and h 0 and P 0 are point height (km) and surface pressure (hpa) respectively. According to Andrei and Chen (2009), this model gives the highest accuracy in hydrostatic tropospheric delay. Now ZWD is transformed to PWV through a conversion factor Q given by (Bevis et al., 1994): Q = ZWD ( PWV = ρ R w k M 2 + k ) (4) wv T m In this equation, k 2 = 22.1 K hpa 1 and k 3 = K 2 hpa 1 are known constants. ρ w, M wv and R are water density, water vapour molar mass and gas global constant, respectively. T m is the water-vapour-weighted mean temperature of the atmosphere (represented by N levels). It is defined and approximated as in Davis et al. (1985): T m Pv T dz P v dz T 2 N i=1 N T 2 i i=1 P vi T i Δz i P vi Δz i (5) where T is the atmospheric temperature in Kelvin and z represents distance in the vertical direction. P v is the partial pressure of water vapour in hectopascals and is given by Wang et al. (2005) as: P v (z) = RH (z) e s {T (z)} (6) where RH is relative humidity and e s is the saturation vapour pressure given by Tetens (1930): ( ) 17.27T e s (T) = exp (7) T The evaluation of Equations (5), (6) and (7) needs the numerical values of relative humidity and temperature to be known at different height levels.

4 418 S. Abbasy et al. Figure 2. (a) Surface air pressure measured at the Institute for Advanced Studies in Basic Sciences (IASBS) meteorological station and at Zanjan synoptic station, and (b) their difference. Figure 3. (a) Zenith total delay (ZTD estimated from GPS data, (b) zenith hydrostatic delay (ZHD) computed by calibrated surface air pressure data using the Saastamoinen model, and (c) the resulting zenith wet delay (ZWD) time series at the Institute for Advanced Studies in Basic Sciences. 4. Numerical investigation 4.1. Evaluation of PWV In the absence of detailed atmospheric data along the vertical profiles measured by radiosondes, the approximate widely used empirical relation for computation of T m is given by Bevis et al. (1992) as: T m = T 0 (8) where T 0 is the surface temperature in Kelvin. This equation is derived from empirical relations between radio soundings and ground measurements over the United States. The validity of the relation is examined by Wang et al. (2005). They conclude that it is probably not valid over regions beyond the contiguous United States and, in the absence of local profile data of atmospheric temperature and humidity, they recommend using globally available profile data (such as ERA-40). There are no in situ radiosonde profile observations at the IASBS GPS station. Therefore, the ERA-Interim data, introduced in Section 2, were used to evaluate T m and then PWV through Equation (4) in which ZWD is derived from Equations (1) and (2). The surface pressure P 0 at the IASBS GPS station is therefore needed. Pressure records of the IASBS meteorological station were measured between March 2011 and December This dataset suffers from large gaps. It was therefore used to calibrate the Zanjan synoptic station data, introduced in Section 2, in order to bring them to the IASBS station level. The Zanjan synoptic station is about 5 km from the IASBS, and its height is about 100 m lower. The mean value of the difference in pressures measured at the two stations ( ± 0.35 hpa) was added to the Zanjan station pressure data (Figure 2). The estimated ZTD from GPS measurements in the IASBS station is shown in Figure 3(a). ZTD varies between 1.85 and 2.05 m with an obvious periodic oscillation. Figure 3(b) shows ZHD computed by a calibrated pressure time series of the Zanjan synoptic station. ZWD computed by Equation (1) is presented in Figure 3(c). The final step in our computations was to evaluate the PWV using Equation (4). The result is shown in Figure 4(a) Empirical relation for T m in Zanjan In order to verify the validity of Equation (8) given by Bevis et al. (1992) in the Zanjan synoptic station, the T m time series based on the ERA-Interim archive was calculated according to Equation (5). A straight line was then fitted to the point cloud plot

5 Precipitable water vapour estimation using single GPS station 419 Figure 4. (a) Precipitable water vapour (PWV) estimated using GPS and the ERA-Interim data set, (b) water vapour pressure (WVP) and (c) dew point temperature measured at the Zanjan synoptic station. of T m with respect to surface temperature T s (Figure 5). The fitted line is T m = α + βt s where α = 80.1 and β = Compared with Equation (8) it is clearly seen that this line is not consistent with that given by Bevis et al. (1992). Considering probable temperature variations in Zanjan synoptic station between 30 and +40 C, differences between 0.91 and 1.68 C are probable in the estimated T m. According to Equation (4), PWV is ZWD divided by the conversion factor Q. Uncertainty in T m propagates to Q and then to PWV. Application of the error propagation to Equation (4) results in errors with amplitudes between 0 and 0.2 mm in estimated PWV Validation of GPS derived PWV Three kinds of validations were applied: (1) empirical relations between meteorological parameters (measured on the surface) and PWV; (2) correlation of PWV with radiosonde data; and (3) correlation of PWV with global reanalysis datasets. Comparisons were made by matching datasets in time through spline interpolation. Empirical relations have been given since the early 20 th Century in order to estimate PWV from surface meteorological observations. The first relation was given by Hann (1906) and relates water vapour pressure (WVP) measured in mm Hg on the Earth surface and PWV in mm: PWV = 2.3WVP (9) The estimated PWV in the IASBS GPS station and the WVP measured in the Zanjan synoptic station are presented in Figures 4(a) and (b). The mean value of their ratio (PWV to WVP) is found to be equal to 2.0. Humphreys (1911) used Equation (9) and obtained a PWV to WVP ratio for Europe equal to 2.0. Fowle (1914) computed the same co-efficient to be equal to 1.9 for California. The correlation co-efficient of estimated PWV in the IASBS GPS station and WVP is equal to 87.8%. In a study by Tuller (1977) on the relationship between PWV and surface humidity in New Zealand, annual correlation co-efficients of 78 86% have been reported between WVP and PWV for different sites. Another similar relation is given by Fowle (1914) who modified and developed a new version of the equation derived by Hann (1906): PWV = 2.3 WVP 10 z (10) Here z is the station height in metres. The IASBS station height is 1792 m; therefore the co-efficient in the above relation is estimated to be equal to 2.4. The third relation used for validating the results by empirical relations is given by Reitan (1963) which takes into account the dew point temperature in the estimation of PWV: ln PWV = bt d + a (11) Here T d is the dew point temperature in F. Considering this linear relation, the following values were estimated: ln PWV = 0.04T d 1.45 (12) Here, the estimated co-efficients are in the range of values reported by Reitan (1963) which is a validation of the PWV values estimated from GPS measurements. The correlation co-efficient between the estimated PWV from the GPS and the dew point temperature measurements of the Zanjan station (Figure 4(c)) is 86.7%. This correlation co-efficient has been estimated in previous studies in different parts of the world. For example, Reitan (1963) reports correlation co-efficients between 0.96 and 0.99 by using mean monthly data for the United States and Tuller (1977) obtained values between 0.81 and 0.88 at six New Zealand stations. Radiosondes give the most accurate meteorological data along the upward profile of a station. The data of three radiosonde stations were used for validation of the estimated PWV time series. There is no radiosonde station in Zanjan. Therefore the data of three neighbour stations situated on a triangle around Zanjan were used (Figure 1). The radiosonde data are available from The sampling interval of Tehran and Kermanshah is 12 h, while that of Tabriz is 24 h. The PWV data series of these stations and the data estimated from GPS observations are

6 420 S. Abbasy et al. Figure 5. Point cloud showing the T m values derived from the ERA-Interim data set for Zanjan synoptic station with respect to surface temperature values. The straight continuous line shows the linear relationship given by Bevis et al. (1992) and the dashed line shows the fitted line to point cloud. Figure 6. Precipitable water vapour (PWV) time series at (a) Tehran, (b) Kermanshah and (c) Tabriz stations from radiosonde measurements and (d) at the Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan station from GPS measurements. shown in Figure 6. The correlation co-efficients of the PWV time series of IASBS with those of Tabriz, Tehran and Kermanshah are 81.1, 71.7 and 66.4%, respectively. Considering that all these four cities have nearly the same climatic regime, it is expected that their PWV time series behave in the same manner. This is clearly seen from our correlation co-efficients and from the plots presented in Figure 6. The PWV can also be obtained from the reanalysis dataset (Section 2). This dataset is given at 2.5 by 2.5 every 6 h from 1948 until now. The corresponding time series from 1 January 2011 to 20 July 2014 is shown in Figure 7(b). The correlation co-efficient between this time series and the PWV from the IASBS GPS station (Figure 7(a)) is 89.2% and the mean value and standard deviation of their difference (Figure 7(c)) are only 0.39 and 2.45 mm respectively. This high correlation shows that the GPS estimated results are reliable enough to be included in the reanalysis process or any other analysis based on PWV data. 5. Conclusion Based on the data measured by a dual frequency Trimble 5700 GPS receiver installed on a permanent station in the Institute for Advanced Studies in Basic Sciences in Zanjan, Iran, the precipitable water vapour (PWV) time series from 1 January 2011 to 20 July 2014 with 1 min time spacing was estimated. Despite the generally used surface air temperature data in PWV estimation from the zenith tropospheric delay (ZTD), the globally available ERA-Interim dataset was used in this research in the estimation process. It should be emphasized that contrary to common studies performed on the basis of GPS/GNSS networks, this study was performed on a single GPS station.

7 Precipitable water vapour estimation using single GPS station 421 Figure 7. Precipitable water vapour (PWV) time series (a) at the Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan station from GPS measurements, (b) from the Physical Sciences Division of the Earth System Research Laboratory of the National Oceanic and Atmospheric Administration s reanalysis dataset and (c) the difference of these two time series. In order to validate the results, three strategies were adopted: (1) comparison of GPS derived PWV with that computed from meteorological parameters measured on the surface, through empirical relations; (2) correlation of PWV with measurements of radiosondes available in three surrounding cities of Zanjan; and (3) correlation of PWV with global reanalysis datasets. All these strategies testify to the correctness of the estimated PWV with single station GPS data. The well documented advantages of meteorology with GPS are the high sampling rate possibility compared with radiosondes, for example, as well as the low installation, maintenance and data processing costs for PWV estimation. However, for regions that are not covered with GPS networks, like Zanjan, single station GPS meteorology gives the possibility of, first, measuring the precise station position (with geodynamics applications) and then of estimating the PWV which is one of the key parameters in studies of the dynamics of the atmosphere. For these reasons, employing GPS single station receivers as high performance meteorological instruments is suggested in regions of poor data coverage. Acknowledgements The authors would like acknowledge the Department of Earth Sciences of the Institute for Advanced Studies in Basic Sciences, Zanjan, Iran, for providing the GPS instruments as well as the corresponding data for this research. The Iranian Meteorological Organization (Zanjan office) is appreciated for their support for surface meteorological data. Two anonymous reviewers are acknowledged for their valuable comments. References Anderle RJ Point positioning concept using precise ephemeris. Satellite Doppler positioning, Proceedings of the International Geodetic Symposium, October 1976, New Mexico State University, Las Cruces, New Mexico, Vol. 1; Andrei CO, Chen R Assessment of time-series of troposphere zenith delays derived from the global data assimilation system numerical weather model. GPS Solutions 13(2): Asgari J Etude de modèles prédictifs dans un réseau de stations GPS permanents, PhD thesis, Ecole Doctorale Astronomie and Astrophysique d Ile de France, Paris Observatory, France. Bennitt GV, Jupp A Operational assimilation of GPS zenith total delay observations into the Met Office numerical weather prediction models. Mon. Weather Rev. 140(8): Bevis M, Businger S, Chiswell S, Herring TA, Anthes RA, Rocken C, et al GPS meteorology: mapping zenith wet delays onto precipitable water. J. Appl. Meteorol. 33(3): Bevis M, Businger S, Herring T, Rocken C, Anthes R, Ware R GPS meteorology remote sensing of atmospheric water vapor using the Global Positioning System. J. Geophys. Res. 97(D14): Brooks DR, Mims FM III, Roettger R Inexpensive near-ir sun photometer for measuring total column water vapor. J. Atmos. Oceanic Technol. 24(7): Businger S, Chiswell SR, Bevis M, Duan J, Anthes RA, Rocken C, et al The promise of GPS in atmospheric monitoring. Bull. Am. Meteorol. Soc. 77(1): Clay RW, Wild NR, Bird DJ, Dawson BR, Johnston M, Patrick R, et al A cloud monitoring system for remote sites. Publ. Astron. Soc. Aust. 15(03): Davis J, Herring T, Shapiro I, Rogers A, Elgered G Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci. 20(6): Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, et al The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137: Duan J, Bevis M, Fang P, Bock Y, Chiswell S, Businger S, et al GPS meteorology: direct estimation of the absolute value of precipitable water. J. Appl. Meteorol. 35(6): Elgered G, Johansson JM, Rönnäng BO, Davis JL Measuring regional atmospheric water vapor using the Swedish permanent GPS network. Geophys. Res. Lett. 24(21): Emardson TR, Derks HJP On the relation between the wet delay and the integrated precipitable water vapour in the European atmosphere. Meteorol. Appl. 7(01):

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