- Email: [email protected]
Climate monitoring using GPS
Physics and Chemistry of the Earth 27 (2002) 335–340 www.elsevier.com/locate/pce
Climate monitoring using GPS L.P. Gradinarsky *, J.M. Johansson, H.R. Bouma, H.-G. Scherneck, G. Elgered Onsala Space Observatory, Chalmers University of Technology, SE-439 92 Onsala, Sweden Accepted 12 January 2002
Abstract We present results on long-term trends of integrated precipitable water vapor (IPWV) over the Scandinavian region based on data from the Swedish permanent Global Positioning System (GPS) network, obtained during the period August 1993 to the end of 2000. We assess the magnitude of the effects on the estimated IPWV caused by antenna radome changes by comparisons with other independent techniques, such as microwave radiometry and radiosondes. The agreement between the techniques is at 1 mm level for the IPWV content and at 0.1 mm/yr for the estimated linear trend. Using the IPWV differences between the techniques, we assess the effects of radome changes to be in the interval 0–1.8 mm depending on the type of radome used. The estimated trends of IPWV over Scandinavia show a general increase of 0.1–0.2 mm/yr, and are more pronounced in the south–west region. We also estimate trends based on summer and winter periods. We find them to be larger for the winter periods compared to the summer in the southern parts and the opposite in the northern regions of Scandinavia. Ó 2002 Elsevier Science Ltd. All rights reserved.
1. Introduction The Global Positioning System (GPS) became fully operational in 1994, when its satellite constellation was completed. Apart from its applications in navigation, GPS established itself as an excellent resource for high precision (mm-level) geodetic measurements. The atmospheric water vapor introduces additional delay to the primary GPS observable – the radio wave propagation time. Precise estimation of the excess propagation delay for increased geodetic precision initiated an additional application of GPS, the remote sensing of atmospheric water vapor (Bevis et al., 1992). The total atmospheric delay of the GPS signal can be divided into a hydrostatic (dry) term caused by the dry gases in the atmosphere and a wet term caused by the refractivity due to water vapor (Davis et al., 1985). Measurements from GPS provide estimates of the total delay comprising both terms. By using ground-based pressure measurements, an accurate estimate of the hydrostatic delay term can be obtained. Subtracting it from the GPS estimated total delay provides us with the wet delay, from which we are able to infer the integrated precipitable water vapor (IPWV) content in the Earth’s atmosphere (Emardson et al., 1998). * Corresponding author. Tel.: +46-031-772-5566; fax: +46-031-7725590. E-mail address: [email protected] (L.P. Gradinarsky).
In this paper, we assess the systematic behavior of IPWV using data from the Swedish permanent GPS network (SWEPOS) as well as from the Finnish permanent network FinRef, and covering the period from the establishment of SWEPOS in August 1993 until the end of 2000. The consistency of the trends of the IPWV is studied. For the Onsala site the results are compared to estimates derived from a water vapor radiometer (WVR) and radiosondes (RS), while for the Sundsvall € stersund sites to only RS data. The future and the O application of GPS for climate monitoring is highly dependent on good knowledge of the sensitivity of the technique to any site changes that occur during the observational period. The impact of one such effect (antenna radome changes) is studied. We also study the total IPWV trends over the Scandinavian region using different lengths of the time series and different seasons.
2. Instrumentation and data analyses Some of the objectives of SWEPOS are: differential GPS services for private users, geodesy and studies of crustal dynamics, and meteorological applications. Currently, it consists of 33 sites spread over Sweden with an average baseline length of 150–200 km. To obtain the GPS estimates of the IPWV, we applied the Precise Point Positioning (PPP) technique (Zumberge et al., 1997)
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available in the GIPSY software package (Webb and Zumberge, 1993). The PPP approach has the advantage of reducing the effects due to site specific changes inherent to the network solutions (Emardson et al., 2000). The elevation cutoff angle was set to 15°, the Niell mapping functions were used (Niell, 1996), no gradient estimation was applied, and the sampling interval was set to 5 min. Ocean loading modeling was also incorporated in the processing (Scherneck et al., 2000). After we inferred the total delay, we used pressure estimates derived from a model calculated at the Swedish Meteorological and Hydrological Institute (SMHI). The obtained estimates of the zenith wet delay were converted to IPWV using the formula presented by Emardson et al. (1998). The data were then decimated to 3 h sampling using a 1 h rectangular window averaging and synchronized with the RS launches at 00, 06, 12, and 18 UT. The water vapor radiometer (WVR), collocated with the GPS antenna at Onsala, measures the sky emission at two frequencies – 21.0 and 31.4 GHz. The sky emission at these frequencies is caused by the amounts of water vapor, liquid water, and oxygen in the atmosphere. The measured sky brightness temperatures are directly related to the wet path delay (Elgered, 1993). The WVR has operated in a continuous sky-scanning mode since 1993. The analysis of the WVR data is carried out using an automated editing procedure. Observations obtained within 15° from the sun are removed. Rain events are detected and the data are removed, since the wet delay algorithm breaks down when the drop sizes are not significantly smaller than the wavelength observed. The measured slant wet delay values are mapped to zenith using a simple cosecant mapping function, an approach appropriate for elevation angles above 20°, which is the minimum elevation angle used by the WVR. For this work, the WVR data are resampled and converted to IPWV as described in the section related to the GPS data. The long-term behavior and error sources of the Onsala WVR are presented in Elgered and Jarlemark (1998). The RS profiles of humidity, temperature, and pressure, were integrated according to Elgered and Jarle-
mark (1998). The RS type is Vaisala RS80 having humidity sensor repeatability of < 2%. The RS launches were two or four times per day for different time periods and RS locations. At Onsala, the RS are launched 37 km € stersund the away from the site while at Sundsvall and O site separation is around 35 km.
3. Results and discussion The amount of hardware changes at the SWEPOS sites is deliberately kept to minimum, to ensure a stable and consistent performance. Nevertheless some changes had to be carried out in an attempt to improve the site performance. One such event, capable of introducing systematic errors, was a change of the radome covering the GPS antenna at each site. Three types of radomes were used in SWEPOS during the studied period (see Table 1). Due to the different elevation dependent properties of the radomes, the estimated zenith delay and vertical coordinates are affected by these changes (Emardson et al., 2000; Johansson et al., 2002) 3.1. Radome biases In an attempt to evaluate the radome impact and assess the consistency of our data, we carried out a comparison between independent IPWV estimates derived from GPS, RS, and WVR. Table 1 summarizes the weighted difference of the estimates from sites with collocated techniques. The uncertainty in the table represents the formal errors which also reflects the number of available data. The agreement of the techniques is consistent with the comparison presented in Emardson et al. (2000), which was based on four years of data. Note that even the WVR and the RS data for Onsala are not entirely consistent in their agreement for the whole period studied. We can observe, for example, a bias of )0.4 mm when comparing periods before and after February 1999 (we used this date because of the radome change that occurred at the GPS site). This illustrates the difficulty to understand and assess biases and sys-
Table 1 Statistics of the weighted difference (bias) and its formal error of the estimated IPWV of coinciding observations derived from WVR, RS, and GPS Site: Difference (mm):
Onsala WVR ) RS
Sundsvall GPS ) RS
€ stersund O GPS ) RS
GPS ) WVR
GPS ) RS
Whole period
0:14 0:01
0:73 0:01
0:98 0:01
1:05 0:01
0:60 0:04
Type 1 (August 1993–June 1995)a Type 2 (June 1995–August 1996) No radome (August 1996–November 1996) Type 3 (November 1996–Today)b
0:22 0:01
0:50 0:01
0:86 0:01
0:20 0:01
1:23 0:01
1:26 0:02
0:81 0:05 1:27 0:02 0:05 0:05 1:05 0:01
No data 1:81 0:11 0:47 0:20 0:44 0:04
€ stersund and for periods with different types of antenna radomes are presented. Results for sites Onsala, Sundsvall, and O a Except for the Onsala site where the period is: August 1993–February 1999. b Except for the Onsala site where the period is: February 1999–Today.
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Table 2 Estimated jumps in the IPWV time series due to radome changes based on the difference RS ) GPS (PPP solution) Parameter jump
IPWV (mm)
Vertical (mm)
Site
Type 1 to Type 3 Type 1 to Type 2 Type 2 to No radome
þ0:40 0:02 þ0:46 0:05 1:22 0:05 1:34 0:23 þ1:00 0:04 0:03 0:21
)9 0.7 )11 0.9 0 0.9 +9 0.1 0 0.9 )2 0.1
Onsala Sundsvall Sundsvall € stersund O Sundsvall € stersund O
No radome to Type 3
We also display the estimates of jumps in the vertical coordinate associated to the same radome change and derived from a network solution.
tematic effects such as these caused by radome changes, if they are below a 1 mm level, due to the measurement uncertainty associated with each technique, which are typically at that level (Elgered and Jarlemark, 1998; Emardson et al., 2000). Table 2 presents an attempt to find correlation between jumps in the estimated vertical coordinates and IPWV derived from GPS with different types of radomes. We estimate IPWV jumps using the difference GPS – RS for each period with a certain radome type. The estimated vertical site coordinate jump associated to the same radome change is obtained from the analyses described by Johansson et al. (2002). There is only a weak relation between the vertical coordinate jumps and the IPWV jumps. However, since we estimated the vertical jumps from network solutions approach (Webb and Zumberge, 1993), the network balances the jumps. If one, for example, introduces changes in 11 sites in the north and leaves 10 sites in the south untouched, the vertical positions of the southern sites experience a side effect and in the northern subnet the vertical change is not the same everywhere. Future analyses will include correlations of the estimated IPWV jumps due to radome changes with estimates of the vertical coordinate based on the PPP solution (which were not available yet). Another independent explanation for the weak correlation might be that the different sites have a unique electromagnetic environment, which interacts differently with the various types of radomes (Jaldehag et al., 1996). This might cause different sensitivity to the radome changes at each location and thus making the assessment of an absolute value of the radome impact difficult.
1, and the results on the model fit and the detected trends are summarized in Table 3. The statistical results show a good agreement in the estimated trends, the difference being <0.05 and 0.14 mm/yr for Onsala and Sundsvall, respectively. We used a weighted leastsquares method for the model fit, where a first-order prediction error prefiltering with coefficients 1 and )0.8 was carried out for noise whitening of the time series (Claerbout, 1976). We also used v2 normalization to obtain the correct scaling of the formal errors associated with each technique. We estimated trends of the amount of IPWV over the Scandinavian region, using the four-parameter model described above. We experimented with including additional bias parameters in the model in order to take
3.2. Long-term trends We use IPWV trend estimates to evaluate the potential of GPS to sense long-term changes of IPWV. The trends shall be independent of absolute constant biases. In order to identify possible trends in IPWV content and remove effects of the annual term, we have fitted a fourparameter model, having an initial offset parameter, a trend, and an annual term described by a phase and an amplitude. The time series of the estimated IPWV based on GPS, RS, and WVR for Onsala are displayed in Fig.
Fig. 1. Time series of the estimated IPWV at Onsala derived from GPS (top), RS (middle), and WVR (bottom). In the top graph displayed also are model fits of the annual variation and the linear trend.
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Table 3 Statistics of the estimated IPWV trends from independent techniques for Onsala and Sundsvall Site Technique
Onsala
Sundsvall
WVR
RS
GPS
RS
GPS
Trend (mm/yr) Error (mm/yr) Model rms (mm)
0.27 0.02 5.45
0.22 0.04 5.08
0.22 0.07 5.20
0.27 0.04 4.94
0.13 0.08 4.86
into account changes of the signal path delay as a consequence of radome changes. Johansson et al. (2002) show that these effects cause jumps on the order of up to 10 mm in the estimated vertical position (see also Table 2). We found that the estimated IPWV trends are very sensitive to this bias parameters. Due to this sensitivity and the fact that we do not have any exact information on the radome change impact on the IPWV to verify the model estimated bias, we decided to use the model with no bias terms allowed in the time series. Fig. 2 is a
smoothed plot of the trends based on 17 GPS sites and using the time period from August 1993 until the end of 2001. Fig. 3, on the other hand, is based on 31 GPS sites being in continuous operation since December 1996. Since that time no changes in the radome types have occurred (except Onsala). The trend uncertainties based on the least squares error of the model fit are on the 0.08 mm/yr level for Fig. 2 and on the 0.16 mm/yr level for Fig. 3, due to the reduced number of data points in the second case. In the calculation of the least-squares error
Fig. 2. Map of the estimated IPWV trends (mm/yr) over the Scandinavian region, based on data from 17 GPS sites. The period used is August 1993–December 2000. The uncertainties of the estimated trends are 0.08 mm/yr.
Fig. 3. Map of the estimated IPWV trends (mm/yr) over the Scandinavian region, based on data from 31 GPS sites. The period used is December 1996–December 2000, when no radome changes occurred (except Onsala). The uncertainties are 0.16 mm/yr.
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of the model fit, we use the formal uncertainties associated with each technique and calculated as described above. Fading colors were used in the figures for areas not covered by sites in order to down weight the results for those areas in contrast to the enhanced colors in the vicinity of the sites. Comparing Figs. 2 and 3 one can observe the agreement in the trends, based on data with different time spans. This indicates that our approach not to estimate jumps at the time of radome changes produces consistent results. Note also the improved resolution in Fig. 3 due to the increase of the number of GPS sites. We also have some more confidence (even if not reflected in the formal uncertainties) in the results in Fig. 3 due to the fact that no radome replacements occurred in this period. The figures show positive trends in the IPWV for most of the regions. The average temperature for the same period over the whole of Sweden has also increased by 0.1–0.2 °C/yr. The values are based on the
reconstructed time series of the ground temperatures measured at nearby sites by SMHI. The IPWV is related to the evaporation and water vapor transport in and around the studied area. The most important driving force behind evaporation is the available energy, the available water for evaporation, together with the local turbulence (Ward and Robinson, 1990). An increase in the temperature means an increase in the available energy for evaporation. This will partly show as an increase in the IPWV content. Even though the amount of IPWV is dependent on more factors than the temperature, we observe a certain correlation with the temperature records. Finally, we present in Figs. 4 and 5 the results of the trends in the IPWV content for summer and winter periods. We used the data set containing data from the middle of 1995 to the end of 2000. In this period no data gaps are present due to missing precise orbit estimates, required for the PPP solutions. Such gaps do exist in the period 1993 to the beginning of 1995 (see Fig. 1, top).
Fig. 4. Map of the estimated IPWV summer trends (mm/yr) over the Scandinavian region, based on data from 17 GPS sites and using the period August 1995–Decemebr 2000. The uncertainties of the estimated trends are 0.10 mm/yr.
Fig. 5. Map of the estimated IPWV winter trends (mm/yr) over the Scandinavian region, based on data from 17 GPS sites and using the period August 1995–December 2000. The uncertainties of the estimated trends are 0.10 mm/yr.
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The total trends for this period look similar to the results in Figs. 2 and 3 and therefore are not presented. The summer and the winter time series were generated using the results of the model fit for the estimation of the total trends. We identified summers as periods covering the positive side of the sinusoid describing the annual term, and winters as those present in the negative part. We then fit our model again to the new time series. The six summer months show a positive trend in the northern parts of Scandinavia, which decreases southwards. It is slightly negative in some of the southern regions (see Fig. 4). During the six winter months, we see a clear positive trend in the southern regions with decreasing magnitude in northern direction (see Fig. 5). The formal errors of the results presented in this figure are on the 0.1 mm/yr level. As a whole, the positive winter trend dominates in the south, while the positive summer trend is prevailing in the north. We have also compared Figs. 4 and 5 with the time series of ground temperatures measured at nearby sites by SMHI. We find that summer trends are of the order of 0.0–0.3 °C/yr with the larger values occurring in the north which is consistent with Fig. 4 if we assume that warmer air will hold more water vapor. For the winter trends in ground temperature we find a maximum of 0.3 °C/yr in the middle of network, decreasing to about 0.0 °C/yr in the northern and southern parts, which is a different pattern compared to the IPWV results in Fig. 5.
4. Conclusions We assessed the GPS capability to provide estimates of IPWV for future applications of monitoring climate variability. For climate applications, time series of IPWV longer then a few decades are required. Such are not yet available from GPS measurements. However, the continuous operation of an ever increasing number of globally distributed GPS sites creates a very good precondition for the application of IPWV monitoring, covering both short and long temporal and spatial scales. Care should be taken to understand site-specific effects on the GPS estimate. From our data set, given its limitations in temporal and spatial coverage, we conclude that only in some cases IPWV trends can be used as an indicator of temperature changes. With a 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.
References Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R.A., Ware, R.H., 1992. GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J. Geophys. Res. 97, 15,787–15,801. Claerbout, J.F., 1976. Fundamentals of Geophysical Data Processing. McGraw-Hill Inc., New York, 274 p. Davis, J.L., Herring, T.A., Shapiro, I.I., Rogers, A.E.E., Elgered, G., 1985. Geodesy by radio interferometry: effects of atmospheric modeling errors on the estimates of the baseline length. Radio Sci. 20 (6), 1593–1609. Elgered, G., 1993. Tropospheric radio-path delay from ground-based microwave radiometry. In: Janssen, M.A. (Ed.), Atmospheric Remote Sensing by Microwave Radiometry. Wiley and Sons Inc., New York, pp. 218–258. Elgered, G., Jarlemark, P.O.J., 1998. Ground-based microwave radiometry and long-term observations of atmospheric water vapor. Radio Sci. 33, 707–717. Emardson, T.R., Elgered, G., Johansson, J.M., 1998. Three months continuous monitoring of the atmospheric water vapor with a network of global positioning system receivers. J. Geophys. Res. 103 (D2), 1807–1820. Emardson, T.R., Johansson, J.M., Elgered, G., 2000. The systematic behavior of water vapor estimates using four years of GPS observations. Trans. IEEE Geosci. Remote Sens. GE-3, 324–329. Jaldehag, R.T.K., Johansson, J.M., El osegui, P., Davis, J.L., Niell, A.E., R€ onn€ang, B.O., Shapiro, I.I., 1996. Geodesy using the swedish permanent GPS network: effects of signal scattering on estimates of relative site positions. J. Geophys. Res. 101 (B8), 17,841–17,860. Johansson, J.M., Davis, J.L., Scherneck, H.-G., Milne, G.A., Vermeer, M., Mitrovica, J.X., Bennett, R.A., Elgered, G., El osegui, P., Koivula, H., Poutanen, M., R€ onn€ang, B.O., Shapiro, I.I., 2002. Continuous GPS measurements of postglacial adjustment in Fennoscandia, 1. Geodetic results. J. Geophys. Res. (accepted). Niell, A., 1996. Global mapping functions for the atmosphere delay at radio wavelength. J. Geophys. Res. 101 (B2), 3227–3246. Scherneck, H.-G., Johansson, J.M., Webb, F.H., 2000. Ocean loading tides in GPS and rapid variations of the frame origin. In: Schwarz, K.-P. (Ed.), Geodesy Beyond 2000. The Challenges of the First Decade. IAG General Assembly Birmingham, July 19–30 1999, pp. 32–40. Ward, R.C., Robinson, M., 1990. Principles of Hydrology, third ed. McGraw-Hill International (UK) Limited, London. Webb, F.H., Zumberge, J.F., 1993. An Introduction to GIPSY/ OASIS-II. JPL Publication D-11088, Jet Propulsion Laboratory, Pasadena, CA, 1993. Zumberge, J.F., Heflin, M.B., Jefferson, D.C., Watkins, M.M., Webb, F.H., 1997. Precise point positioning for the efficient and robust analysis of GPS data from large networks. J. Geophys. Res. 102 (B3), 5005–5017.
Climate monitoring using GPS L.P. Gradinarsky *, J.M. Johansson, H.R. Bouma, H.-G. Scherneck, G. Elgered Onsala Space Observatory, Chalmers University of Technology, SE-439 92 Onsala, Sweden Accepted 12 January 2002
Abstract We present results on long-term trends of integrated precipitable water vapor (IPWV) over the Scandinavian region based on data from the Swedish permanent Global Positioning System (GPS) network, obtained during the period August 1993 to the end of 2000. We assess the magnitude of the effects on the estimated IPWV caused by antenna radome changes by comparisons with other independent techniques, such as microwave radiometry and radiosondes. The agreement between the techniques is at 1 mm level for the IPWV content and at 0.1 mm/yr for the estimated linear trend. Using the IPWV differences between the techniques, we assess the effects of radome changes to be in the interval 0–1.8 mm depending on the type of radome used. The estimated trends of IPWV over Scandinavia show a general increase of 0.1–0.2 mm/yr, and are more pronounced in the south–west region. We also estimate trends based on summer and winter periods. We find them to be larger for the winter periods compared to the summer in the southern parts and the opposite in the northern regions of Scandinavia. Ó 2002 Elsevier Science Ltd. All rights reserved.
1. Introduction The Global Positioning System (GPS) became fully operational in 1994, when its satellite constellation was completed. Apart from its applications in navigation, GPS established itself as an excellent resource for high precision (mm-level) geodetic measurements. The atmospheric water vapor introduces additional delay to the primary GPS observable – the radio wave propagation time. Precise estimation of the excess propagation delay for increased geodetic precision initiated an additional application of GPS, the remote sensing of atmospheric water vapor (Bevis et al., 1992). The total atmospheric delay of the GPS signal can be divided into a hydrostatic (dry) term caused by the dry gases in the atmosphere and a wet term caused by the refractivity due to water vapor (Davis et al., 1985). Measurements from GPS provide estimates of the total delay comprising both terms. By using ground-based pressure measurements, an accurate estimate of the hydrostatic delay term can be obtained. Subtracting it from the GPS estimated total delay provides us with the wet delay, from which we are able to infer the integrated precipitable water vapor (IPWV) content in the Earth’s atmosphere (Emardson et al., 1998). * Corresponding author. Tel.: +46-031-772-5566; fax: +46-031-7725590. E-mail address: [email protected] (L.P. Gradinarsky).
In this paper, we assess the systematic behavior of IPWV using data from the Swedish permanent GPS network (SWEPOS) as well as from the Finnish permanent network FinRef, and covering the period from the establishment of SWEPOS in August 1993 until the end of 2000. The consistency of the trends of the IPWV is studied. For the Onsala site the results are compared to estimates derived from a water vapor radiometer (WVR) and radiosondes (RS), while for the Sundsvall € stersund sites to only RS data. The future and the O application of GPS for climate monitoring is highly dependent on good knowledge of the sensitivity of the technique to any site changes that occur during the observational period. The impact of one such effect (antenna radome changes) is studied. We also study the total IPWV trends over the Scandinavian region using different lengths of the time series and different seasons.
2. Instrumentation and data analyses Some of the objectives of SWEPOS are: differential GPS services for private users, geodesy and studies of crustal dynamics, and meteorological applications. Currently, it consists of 33 sites spread over Sweden with an average baseline length of 150–200 km. To obtain the GPS estimates of the IPWV, we applied the Precise Point Positioning (PPP) technique (Zumberge et al., 1997)
1474-7065/02/$ - see front matter Ó 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 4 7 4 - 7 0 6 5 ( 0 2 ) 0 0 0 0 9 - 8
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available in the GIPSY software package (Webb and Zumberge, 1993). The PPP approach has the advantage of reducing the effects due to site specific changes inherent to the network solutions (Emardson et al., 2000). The elevation cutoff angle was set to 15°, the Niell mapping functions were used (Niell, 1996), no gradient estimation was applied, and the sampling interval was set to 5 min. Ocean loading modeling was also incorporated in the processing (Scherneck et al., 2000). After we inferred the total delay, we used pressure estimates derived from a model calculated at the Swedish Meteorological and Hydrological Institute (SMHI). The obtained estimates of the zenith wet delay were converted to IPWV using the formula presented by Emardson et al. (1998). The data were then decimated to 3 h sampling using a 1 h rectangular window averaging and synchronized with the RS launches at 00, 06, 12, and 18 UT. The water vapor radiometer (WVR), collocated with the GPS antenna at Onsala, measures the sky emission at two frequencies – 21.0 and 31.4 GHz. The sky emission at these frequencies is caused by the amounts of water vapor, liquid water, and oxygen in the atmosphere. The measured sky brightness temperatures are directly related to the wet path delay (Elgered, 1993). The WVR has operated in a continuous sky-scanning mode since 1993. The analysis of the WVR data is carried out using an automated editing procedure. Observations obtained within 15° from the sun are removed. Rain events are detected and the data are removed, since the wet delay algorithm breaks down when the drop sizes are not significantly smaller than the wavelength observed. The measured slant wet delay values are mapped to zenith using a simple cosecant mapping function, an approach appropriate for elevation angles above 20°, which is the minimum elevation angle used by the WVR. For this work, the WVR data are resampled and converted to IPWV as described in the section related to the GPS data. The long-term behavior and error sources of the Onsala WVR are presented in Elgered and Jarlemark (1998). The RS profiles of humidity, temperature, and pressure, were integrated according to Elgered and Jarle-
mark (1998). The RS type is Vaisala RS80 having humidity sensor repeatability of < 2%. The RS launches were two or four times per day for different time periods and RS locations. At Onsala, the RS are launched 37 km € stersund the away from the site while at Sundsvall and O site separation is around 35 km.
3. Results and discussion The amount of hardware changes at the SWEPOS sites is deliberately kept to minimum, to ensure a stable and consistent performance. Nevertheless some changes had to be carried out in an attempt to improve the site performance. One such event, capable of introducing systematic errors, was a change of the radome covering the GPS antenna at each site. Three types of radomes were used in SWEPOS during the studied period (see Table 1). Due to the different elevation dependent properties of the radomes, the estimated zenith delay and vertical coordinates are affected by these changes (Emardson et al., 2000; Johansson et al., 2002) 3.1. Radome biases In an attempt to evaluate the radome impact and assess the consistency of our data, we carried out a comparison between independent IPWV estimates derived from GPS, RS, and WVR. Table 1 summarizes the weighted difference of the estimates from sites with collocated techniques. The uncertainty in the table represents the formal errors which also reflects the number of available data. The agreement of the techniques is consistent with the comparison presented in Emardson et al. (2000), which was based on four years of data. Note that even the WVR and the RS data for Onsala are not entirely consistent in their agreement for the whole period studied. We can observe, for example, a bias of )0.4 mm when comparing periods before and after February 1999 (we used this date because of the radome change that occurred at the GPS site). This illustrates the difficulty to understand and assess biases and sys-
Table 1 Statistics of the weighted difference (bias) and its formal error of the estimated IPWV of coinciding observations derived from WVR, RS, and GPS Site: Difference (mm):
Onsala WVR ) RS
Sundsvall GPS ) RS
€ stersund O GPS ) RS
GPS ) WVR
GPS ) RS
Whole period
0:14 0:01
0:73 0:01
0:98 0:01
1:05 0:01
0:60 0:04
Type 1 (August 1993–June 1995)a Type 2 (June 1995–August 1996) No radome (August 1996–November 1996) Type 3 (November 1996–Today)b
0:22 0:01
0:50 0:01
0:86 0:01
0:20 0:01
1:23 0:01
1:26 0:02
0:81 0:05 1:27 0:02 0:05 0:05 1:05 0:01
No data 1:81 0:11 0:47 0:20 0:44 0:04
€ stersund and for periods with different types of antenna radomes are presented. Results for sites Onsala, Sundsvall, and O a Except for the Onsala site where the period is: August 1993–February 1999. b Except for the Onsala site where the period is: February 1999–Today.
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Table 2 Estimated jumps in the IPWV time series due to radome changes based on the difference RS ) GPS (PPP solution) Parameter jump
IPWV (mm)
Vertical (mm)
Site
Type 1 to Type 3 Type 1 to Type 2 Type 2 to No radome
þ0:40 0:02 þ0:46 0:05 1:22 0:05 1:34 0:23 þ1:00 0:04 0:03 0:21
)9 0.7 )11 0.9 0 0.9 +9 0.1 0 0.9 )2 0.1
Onsala Sundsvall Sundsvall € stersund O Sundsvall € stersund O
No radome to Type 3
We also display the estimates of jumps in the vertical coordinate associated to the same radome change and derived from a network solution.
tematic effects such as these caused by radome changes, if they are below a 1 mm level, due to the measurement uncertainty associated with each technique, which are typically at that level (Elgered and Jarlemark, 1998; Emardson et al., 2000). Table 2 presents an attempt to find correlation between jumps in the estimated vertical coordinates and IPWV derived from GPS with different types of radomes. We estimate IPWV jumps using the difference GPS – RS for each period with a certain radome type. The estimated vertical site coordinate jump associated to the same radome change is obtained from the analyses described by Johansson et al. (2002). There is only a weak relation between the vertical coordinate jumps and the IPWV jumps. However, since we estimated the vertical jumps from network solutions approach (Webb and Zumberge, 1993), the network balances the jumps. If one, for example, introduces changes in 11 sites in the north and leaves 10 sites in the south untouched, the vertical positions of the southern sites experience a side effect and in the northern subnet the vertical change is not the same everywhere. Future analyses will include correlations of the estimated IPWV jumps due to radome changes with estimates of the vertical coordinate based on the PPP solution (which were not available yet). Another independent explanation for the weak correlation might be that the different sites have a unique electromagnetic environment, which interacts differently with the various types of radomes (Jaldehag et al., 1996). This might cause different sensitivity to the radome changes at each location and thus making the assessment of an absolute value of the radome impact difficult.
1, and the results on the model fit and the detected trends are summarized in Table 3. The statistical results show a good agreement in the estimated trends, the difference being <0.05 and 0.14 mm/yr for Onsala and Sundsvall, respectively. We used a weighted leastsquares method for the model fit, where a first-order prediction error prefiltering with coefficients 1 and )0.8 was carried out for noise whitening of the time series (Claerbout, 1976). We also used v2 normalization to obtain the correct scaling of the formal errors associated with each technique. We estimated trends of the amount of IPWV over the Scandinavian region, using the four-parameter model described above. We experimented with including additional bias parameters in the model in order to take
3.2. Long-term trends We use IPWV trend estimates to evaluate the potential of GPS to sense long-term changes of IPWV. The trends shall be independent of absolute constant biases. In order to identify possible trends in IPWV content and remove effects of the annual term, we have fitted a fourparameter model, having an initial offset parameter, a trend, and an annual term described by a phase and an amplitude. The time series of the estimated IPWV based on GPS, RS, and WVR for Onsala are displayed in Fig.
Fig. 1. Time series of the estimated IPWV at Onsala derived from GPS (top), RS (middle), and WVR (bottom). In the top graph displayed also are model fits of the annual variation and the linear trend.
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Table 3 Statistics of the estimated IPWV trends from independent techniques for Onsala and Sundsvall Site Technique
Onsala
Sundsvall
WVR
RS
GPS
RS
GPS
Trend (mm/yr) Error (mm/yr) Model rms (mm)
0.27 0.02 5.45
0.22 0.04 5.08
0.22 0.07 5.20
0.27 0.04 4.94
0.13 0.08 4.86
into account changes of the signal path delay as a consequence of radome changes. Johansson et al. (2002) show that these effects cause jumps on the order of up to 10 mm in the estimated vertical position (see also Table 2). We found that the estimated IPWV trends are very sensitive to this bias parameters. Due to this sensitivity and the fact that we do not have any exact information on the radome change impact on the IPWV to verify the model estimated bias, we decided to use the model with no bias terms allowed in the time series. Fig. 2 is a
smoothed plot of the trends based on 17 GPS sites and using the time period from August 1993 until the end of 2001. Fig. 3, on the other hand, is based on 31 GPS sites being in continuous operation since December 1996. Since that time no changes in the radome types have occurred (except Onsala). The trend uncertainties based on the least squares error of the model fit are on the 0.08 mm/yr level for Fig. 2 and on the 0.16 mm/yr level for Fig. 3, due to the reduced number of data points in the second case. In the calculation of the least-squares error
Fig. 2. Map of the estimated IPWV trends (mm/yr) over the Scandinavian region, based on data from 17 GPS sites. The period used is August 1993–December 2000. The uncertainties of the estimated trends are 0.08 mm/yr.
Fig. 3. Map of the estimated IPWV trends (mm/yr) over the Scandinavian region, based on data from 31 GPS sites. The period used is December 1996–December 2000, when no radome changes occurred (except Onsala). The uncertainties are 0.16 mm/yr.
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of the model fit, we use the formal uncertainties associated with each technique and calculated as described above. Fading colors were used in the figures for areas not covered by sites in order to down weight the results for those areas in contrast to the enhanced colors in the vicinity of the sites. Comparing Figs. 2 and 3 one can observe the agreement in the trends, based on data with different time spans. This indicates that our approach not to estimate jumps at the time of radome changes produces consistent results. Note also the improved resolution in Fig. 3 due to the increase of the number of GPS sites. We also have some more confidence (even if not reflected in the formal uncertainties) in the results in Fig. 3 due to the fact that no radome replacements occurred in this period. The figures show positive trends in the IPWV for most of the regions. The average temperature for the same period over the whole of Sweden has also increased by 0.1–0.2 °C/yr. The values are based on the
reconstructed time series of the ground temperatures measured at nearby sites by SMHI. The IPWV is related to the evaporation and water vapor transport in and around the studied area. The most important driving force behind evaporation is the available energy, the available water for evaporation, together with the local turbulence (Ward and Robinson, 1990). An increase in the temperature means an increase in the available energy for evaporation. This will partly show as an increase in the IPWV content. Even though the amount of IPWV is dependent on more factors than the temperature, we observe a certain correlation with the temperature records. Finally, we present in Figs. 4 and 5 the results of the trends in the IPWV content for summer and winter periods. We used the data set containing data from the middle of 1995 to the end of 2000. In this period no data gaps are present due to missing precise orbit estimates, required for the PPP solutions. Such gaps do exist in the period 1993 to the beginning of 1995 (see Fig. 1, top).
Fig. 4. Map of the estimated IPWV summer trends (mm/yr) over the Scandinavian region, based on data from 17 GPS sites and using the period August 1995–Decemebr 2000. The uncertainties of the estimated trends are 0.10 mm/yr.
Fig. 5. Map of the estimated IPWV winter trends (mm/yr) over the Scandinavian region, based on data from 17 GPS sites and using the period August 1995–December 2000. The uncertainties of the estimated trends are 0.10 mm/yr.
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The total trends for this period look similar to the results in Figs. 2 and 3 and therefore are not presented. The summer and the winter time series were generated using the results of the model fit for the estimation of the total trends. We identified summers as periods covering the positive side of the sinusoid describing the annual term, and winters as those present in the negative part. We then fit our model again to the new time series. The six summer months show a positive trend in the northern parts of Scandinavia, which decreases southwards. It is slightly negative in some of the southern regions (see Fig. 4). During the six winter months, we see a clear positive trend in the southern regions with decreasing magnitude in northern direction (see Fig. 5). The formal errors of the results presented in this figure are on the 0.1 mm/yr level. As a whole, the positive winter trend dominates in the south, while the positive summer trend is prevailing in the north. We have also compared Figs. 4 and 5 with the time series of ground temperatures measured at nearby sites by SMHI. We find that summer trends are of the order of 0.0–0.3 °C/yr with the larger values occurring in the north which is consistent with Fig. 4 if we assume that warmer air will hold more water vapor. For the winter trends in ground temperature we find a maximum of 0.3 °C/yr in the middle of network, decreasing to about 0.0 °C/yr in the northern and southern parts, which is a different pattern compared to the IPWV results in Fig. 5.
4. Conclusions We assessed the GPS capability to provide estimates of IPWV for future applications of monitoring climate variability. For climate applications, time series of IPWV longer then a few decades are required. Such are not yet available from GPS measurements. However, the continuous operation of an ever increasing number of globally distributed GPS sites creates a very good precondition for the application of IPWV monitoring, covering both short and long temporal and spatial scales. Care should be taken to understand site-specific effects on the GPS estimate. From our data set, given its limitations in temporal and spatial coverage, we conclude that only in some cases IPWV trends can be used as an indicator of temperature changes. With a 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.
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