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Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3)

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Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate

Abstract

There has been growing interest in recent years in the use of homogeneously reprocessed ground-based GNSS, VLBI, and DORIS measurements for climate applications. Existing datasets are reviewed and the sensitivity of tropospheric estimates to the processing details is discussed. The uncertainty in the derived IWV estimates and linear trends is around 1 kg m−2 RMS and ± 0.3 kg m−2 per decade, respectively. Standardized methods for ZTD outlier detection and IWV conversion are proposed. The homogeneity of final time series is limited however by changes in the stations equipment and environment. Various homogenization algorithms have been evaluated based on a synthetic benchmark dataset. The uncertainty of trends estimated from the homogenized times series is estimated to ±0.5 kg m−2 per decade. Reprocessed GNSS IWV data are analysed along with satellites data, reanalyses and global and regional climate model simulations. A selection of global and regional reprocessed GNSS datasets and ERA-interim reanalysis are made available through the GOP-TropDB tropospheric database and online service. A new tropo SINEX format, providing new features and simplifications, was developed and it is going to be adopted by all the IAG services.

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Notes

  1. 1.

    Parts from this section were previously published in Pacione et al. (2017).

  2. 2.

    Parts from this section were previously published in Ahmed et al. (2014).

  3. 3.

    Parts from this section were previously published in Stepnaik et al. (2017).

  4. 4.

    Parts from this section were previously published in Bałdysz et al. (2016).

  5. 5.

    Parts from this section were previously published in Douša et al. (2017a, b), Balidakis et al. (2018) and Ning et al. (2017).

  6. 6.

    Parts from this section were previously published in Araszkiewicz and Voelksen (2017) and Pacione et al. 2017).

  7. 7.

    Parts from this section were previously published in Pacione et al. (2017).

  8. 8.

    Parts from this section were previously published in Forkman et al. (2017).

  9. 9.

    http://www.businessdictionary.com/definition/data-screening.html

  10. 10.

    Parts from this section were previously published in Ning et al. 2016a, b).

  11. 11.

    Parts from this section were previously published in Ning et al. (2013).

  12. 12.

    Parts from this section were previously published in Parracho (2017).

  13. 13.

    Parts from this section were previously published in Mircheva et al. (2017).

References

  • Adams, D. K., Fernandes, R. M. S., & Maia, J. M. F. (2011). GNSS precipitable water vapour from an Amazonian rain forest flux tower. Journal of Atmospheric and Oceanic Technology, 28(10), 1192–1198. https://doi.org/10.1175/JTECH-D-11-00082.1.

    Article  Google Scholar 

  • Ahmed F., Teferle N., Bingley R., & Hunegnaw A. (2014). A comparative analysis of tropospheric delay estimates from network and precise point positioning processing strategies. Presented at IGS Workshop 2014, 23–27.06.2014, Pasadena, California, USA.

    Google Scholar 

  • Alexandersson, H. (1986). A homogeneity test applied to precipitation data. Journal of Climatology, 6, 661–675.

    Article  Google Scholar 

  • Altamimi, Z., Rebisching, P., Metivier, L., & Collilieux, X. (2016). ITRF2014: A new release of the International Terrestrial Reference Frame modelling nonlinear station motions. Journal of Geophysical Research – Solid Earth, 121(8), 6109–6131. https://doi.org/10.1002/2016JB013098.

    Article  Google Scholar 

  • Antón, M., Loyola, D., Román, R., & Vömel, H. (2015). Validation of GOME-2/MetOp-A total water vapour column using reference radiosonde data from the GRUAN network. Atmospheric Measurement Techniques, 8, 1135–1145. https://doi.org/10.5194/amt-8-1135-2015

    Article  Google Scholar 

  • Araszkiewicz, A., & Voelksen, C. (2017). The impact of the antenna phase centre models on the coordinates in the EUREF Permanent Network. GPS Solutions, 21, 747–757. https://doi.org/10.1007/s10291-016-0564-7.

    Article  Google Scholar 

  • Askne, J., & Nordius, H. (1987). Estimation of tropospheric delay for microwaves from surface weather data. Radio Science, 22(3), 379–386. https://doi.org/10.1029/RS022i003p00379.

    Article  Google Scholar 

  • Bałdysz Z, Nykiel G, Araszkiewicz A, Figurski M, Szafranek K (2016) Comparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoring. Atmospheric Measurement Techniques, 9(9), 4861–4877. https://doi.org/https://doi.org/10.5194/amt-9-4861-2016 (licensed under CC BY 3.0, https://creativecommons.org/licenses/by/3.0/).

    Article  Google Scholar 

  • Balidakis, K., Nilsson, T., Zus, F., Glaser, S., Heinkelmann, R., Deng, Z., & Schuh, H. (2018). Estimating integrated water vapour trends from VLBI, GPS and numerical weather models: sensitivity to tropospheric parameterization. Journal of Geophysical Research: Atmospheres, 123, 6356–6372. https://doi.org/10.1029/2017JD028049.

    Article  Google Scholar 

  • Barnston, A. G., & Livezey, R. E. (1987). Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Monthly Weather Review, 115(6), 1083–1126. https://doi.org/https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2

  • Bar-Sever, Y. E., Kroger, P. M., & Borjesson, J.A. (1998). Estimating Horizontal Gradients of Tropospheric Path Delay with a Single GPS Receiver. Journal of Geophysical Research, 103, 5019–5035.

    Google Scholar 

  • Basili, P., Bonafoni, S., Ferrara, R., Ciotti, P., Fionda, E., & Arnbrosini, R. (2001). Atmospheric water vapour retrieval by means of both a GPS network and a microwave radiometer during an experimental campaign in Cagliari, Italy. In 1999, Geoscience and Remote Sensing, IEEE Transactions on, Vol. 39, No.11, pp. 2436–2443. https://doi.org/10.1109/36.964980

    Article  Google Scholar 

  • Beirle, S., Lampel, J., Wang, Y., Mies, K., Dörner, S., Grossi, M., Loyola, D., Dehn, A., Danielczok, A., Schröder, M., & Wagner, T. (2018). The ESA GOME-Evolution “Climate” water vapor product: a homogenized time series of H2O columns from GOME, SCIAMACHY, and GOME-2. Earth System Science Data, 10, 449–468. https://doi.org/10.5194/essd-10-449-2018.

    Article  Google Scholar 

  • Bennouna, Y. S., Torres, B., Cachorro, V. E., Ortiz de Galisteo, J. P., & Toledano, C. (2013). The evaluation of the integrated water vapour annual cycle over the Iberian Peninsula from EOS-MODIS against different ground-based techniques. Quarterly Journal of the Royal Meteorological Society, 139, 1935–1956. https://doi.org/10.1002/qj.2080.

    Article  Google Scholar 

  • Berckmans, J., Giot, O., De Troch, R., Hamdi, R., Ceulemans, R., & Termonia, P. (2017). Reinitialised versus continuous regional climate simulations using ALARO-0 coupled to the land surface model SURFEXv5. Geoscientific Model Development, 10, 223–238.

    Article  Google Scholar 

  • Berg, H. (1948). Allgemeine meteorologie. Bonn: Dümmler’s Verlag.

    Google Scholar 

  • Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., & Ware, R. H. (1992). GPS meteorology: remote sensing of atmospheric water vapour using the global positioning system. Journal of Geophysical Research, 97(D14), 15787–15801.

    Article  Google Scholar 

  • Bevis, M., Businger, S., Chiswell, S., Herring, T. A., Anthes, R. A., Rocken, C., & Ware, R. H.(1994). GPS meteorology: Mapping Zenith wet delays onto precipitable water. Journal of Applied Meterology, 33(3), 379–386. https://doi.org/https://doi.org/10.1175/1520−0450(1994)033<0379:GMMZWD>2.0.CO;2

  • Bock, O. (2015, May) ZTD assessment and screening. COST ES1206 GNSS4SWEC Workshop, Thessaloniki, Greece, 11–13 May 2015.

    Google Scholar 

  • Bock, O. (2016a). A reference IWV dataset combining IGS repro1 and ERA-Interim reanalysis for the assessment of homogenization algorithms. 3rd COST ES1206 Workshop, 8–11 March 2016, Reykjavik, Island.

    Google Scholar 

  • Bock, O. (2016b). Post-processing of GNSS ZTD, COST ES1206 GNSS4SWEC Summer School, 29–31 August 2016, GFZ, Potsdam, Germany. Available at: ftp://ftp.gfz-potsdam.de/pub/GNSS/workshops/gnss4swec/Summer_School/D2/3_Bock_PostProc.pdf

  • Bock, O. (2016c). Conversion of GNSS ZTD to IWV, COST ES1206 GNSS4SWEC Summer School, 29–31 August 2016, GFZ, Potsdam, Germany. Available at: ftp.gfz-potsdam.de/pub/GNSS/workshops/gnss4swec/Summer_School/D3/2_Bock_Homogenization.pdf

  • Bock, O. (2016d). Screening and validation of new reprocessed GNSS IWV data in Arctic region, COST ES1206 GNSS4SWEC Workshop, Potsdam, Germany, 31 August–2 September 2016.

    Google Scholar 

  • Bock O., & Pacione, R. (2014). ZTD to IWV conversion, COST action ES1206 – GNSS4SWEC. 2nd WG meeting, Varna, Bulgaria, 11–12 September, 2014.

    Google Scholar 

  • Bock, O., Bouin, M.-N., Walpersdorf, A., Lafore, J. P., Janicot, S., Guichard, F., & Agusti-Panareda, A. (2007). Comparison of ground-based GPS precipitable water vapour to independent observations and NWP model reanalyses over Africa. Quarterly Journal of the Royal Meteorological Society, 133, 2011–2027. https://doi.org/10.1002/qj.185.

    Article  Google Scholar 

  • Bock, O., P. Willis, M. Lacarra, & P. Bosser (2010) An inter-comparison of zenith tropospheric delays derived from DORIS and GPS data, Advances in Space Research, 46(12), 1648–1660. https://doi.org/10.1016/j.asr.2010.05.018

    Article  Google Scholar 

  • Bock, O., Willis, P., Wang, J., & Mears, C. (2014). A high-quality, homogenized, global, long-term (1993-2008) DORIS precipitable water dataset for climate monitoring and model verification. Journal of Gephysics Research Atmosphere, 119(12), 7209–7230. https://doi.org/10.1002/2013JD021124.

    Article  Google Scholar 

  • Bock O., & Parracho, A. C. (2019) Consistency and representativeness of integrated water vapour from ground-based GPS observations and ERA-Interim reanalysis, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-28

    Google Scholar 

  • Bodeker, G. E., Bojinski, S., Cimini, D., Dirksen, R. J., Haeffelin, M., Hannigan, J. W., Hurst, D. F., Leblanc, T., Madonna, F., Maturilli, M., Mikalsen, A. C., Philipona, R., Reale, T., Seidel, D. J., Tan, D. G. H., Thorne, P. W., Vömel, H., and Wang, J. (2016) Reference upper-air observations for climate: From concept to reality, Amer Meteorological Society, 123–135. https://doi.org/10.1175/BAMSD-14-00072.1

  • Boehm, J., & Schuh, H. (2007). Troposphere gradients from the ECMWF in VLBI analysis. Journal of Geodesy, 81, 403–408. https://doi.org/10.1007/s00190-0144-2.

    Article  Google Scholar 

  • Boehm, J., Niell, A., Tregoning, P., & Schuh, H. (2006a). Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data. Geophysical Research Letters, 33, L07304. https://doi.org/10.1029/2005GL025546.

    Article  Google Scholar 

  • Boehm, J., Werl, B., & Schuh, H. (2006b). Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data. Journal of Geophysical Research, 111, B02406. https://doi.org/10.1029/2005JB003629.

    Article  Google Scholar 

  • Boehm, J., Heinkelmann, R., & Schuh, H. (2007). Short Note: A global model of pressure and temperature for geodetic applications. Journal of Geodesy, 81(10), 679–683. https://doi.org/10.1007/s00190-007-0135-3.

    Article  Google Scholar 

  • Böhm, J., Möller, G., Schindelegger, M., Pain, G., & Weber, R. (2015). Development of an improved empirical model for slant delays in the troposphere (GPT2w). GPS Solutions, 3, 433–441.

    Article  Google Scholar 

  • Bos, M. S., Fernandes, R. M. S., Williams, S. D. P., & Bastos, L. (2013). Fast Error Analysis of Continuous GNSS Observations with Missing Data. Journal of Geodesy, 87(4), 351–360. https://doi.org/10.1007/s00190-012-0605-0.

    Article  Google Scholar 

  • Bosser, P., & Bock, O. (2016). Screening of GPS ZTD estimates. 3rd COST ES1206 Workshop, 8–11.03.2016, Reykjavik, Island.

    Google Scholar 

  • Brown, R. A. (1970). A secondary flow model for the planetary boundary layer. Journal of the Atmospheric Sciences, 27, 742–757.

    Article  Google Scholar 

  • Bruyninx, C. H., Habrich, W., Söhne, A., Kenyeres, G., & Stangl, V. C. (2012). Enhancement of the EUREF Permanent Network Services and Products. Geodesy for Planet Earth, IAG Symposia Series, Vol. 136, pp. 27–35. https://doi.org/10.1007/978-3-642-20338-1_4.

    Google Scholar 

  • Buehler, S. A., Östman, S., Melsheimer, C., Holl, G., Eliasson, S., John, V. O., Blumenstock, T., Hase, F., Elgered, G., Raffalski, U., Nasuno, T., Satoh, M., Milz, M., & Mendrok, J. (2012). A multi-instrument comparison of integrated water vapour measurements at a high latitude site. Atmospheric Chemistry and Physics, 12, 10925–10943. https://doi.org/10.5194/acp-12-10925-2012.

    Article  Google Scholar 

  • Byram S., Hackman C., & Tracey J. (2011). Computation of a high-precision GPS-based troposphere product by the USNO. In Proceedings of ION GNSS.

    Google Scholar 

  • Byun, S. H., & Bar-Sever, Y. E. (2009). A new type of troposphere zenith path delay product of the International GNSS Service. Journal of Geodesy, 83(3–4), 1–7.

    Article  Google Scholar 

  • Calori, A., Santos, J. R., Blanco, M., Pessano, H., Llamedo, P., Alexander, P., & de la Torre, A. (2016). Ground-based GNSS network and integrated water vapour mapping during the development of severe storms at the Cuyo region (Argentina). Atmospheric Research, 176–177, 267–275. https://doi.org/10.1016/j.atmosres.2016.03.002.

    Article  Google Scholar 

  • Campanelli, M., Mascitelli, A., Sanò, P., Diémoz, H., Estellés, V., Federico, S., Iannarelli, A. M., Fratarcangeli, F., Mazzoni, A., Realini, E., Crespi, M., Bock, O., Martìnez-Lozano, J. A., & Dietrich, S. (2017). Precipitable water vapour content from ESR/SKYNET Sun-sky radiometers: Validation against GNSS/GPS and AERONET over three different sites in Europe. Atmospheric Measurement Techniques Discussions, 1–27. https://doi.org/10.5194/amt-2017-221,. in review.

  • Caussinus, H., & Mestre, O. (2004). Detection and correction of artificial shifts in climate series. Journal of the Royal Statistical Society, Series C, 53, 405–425.

    Article  Google Scholar 

  • Chen, G., & Herring, T. A. (1997). Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data. Journal of Geophysical Research, 102, 20489–20502. https://doi.org/10.1029/97JB01739.

    Article  Google Scholar 

  • Dach, R., Böhm, J., Lutz, S., Steigenberger, P., & Beutler, G. (2010). Evaluation of the impact of atmospheric pressure loading modelling on GNSS data analysis. Journal of Geodesy, 85, 75–91. https://doi.org/10.1007/s00190-010-0417-z.

    Article  Google Scholar 

  • Dach, R., Lutz, S., Walser, P., & Fridez, P. (2015). User manual of the Bernese GNSS Software, Version 5.2. University of Bern, Bern Open Publishing. https://doi.org/10.7892/boris.72297.

  • Davis, J. L., Herring, T. A., Shapiro, I. I., Rogers, A. E., & Elgered, G. (1985). Geodesy by radio interferometry: Effects of atmospheric modelling errors on estimates of baseline length. Radio Science, 20, 1593–1607.

    Article  Google Scholar 

  • Deblonde, G., Macpherson, S., Mireault, Y., & Heroux, P. (2005). Evaluation of GPS precipitable water over Canada and the IGS network. Journal of Applied Meteorology, 44, 153–166. https://doi.org/10.1175/JAM-2201.1

    Article  Google Scholar 

  • Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., & Bechtold, P. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/10.1002/qj.828.

    Article  Google Scholar 

  • Douša, J., & Győri, G. (2013). Database for tropospheric product evaluations – implementation aspects. Geoinformatics, 10, 39–52.

    Google Scholar 

  • Douša, J., Byram, S., Győri, G., Böhm, O., Hackman, C., & Zus, F.. (2014). Development towards inter-technique troposphere parameter comparisons and their exploitation. 2014 IGS Workshop, Pasadena, CA.

    Google Scholar 

  • Douša, J., Böhm, O., Byram, S., Hackman, C., Deng, Z., Zus, F., Dach, R., & Steigenberger, P. (2016). Evaluation of GNSS reprocessing tropospheric products using GOP-TropDB. Presentation at IGS Workshop 2016, Sydney, March 8–10.

    Google Scholar 

  • Douša, J., Heikelmann, R., & Balidakis, K. (2017a). Tropospheric ties for inter-technique comparisons and combinations, Presentation at IAG – IASPE, July 30 – August 4, 2017. Japan: Kobe.

    Google Scholar 

  • Douša, J., Václavovic, P., & Elias, M. (2017b). Tropospheric products of the second GOP European GNSS reprocessing (1996–2014). Atmospheric Measurement Techniques, 10, 3589–3607. https://doi.org/10.5194/amt-10-3589-2017 (licensed under CC BY 3.0, https://creativecommons.org/licenses/by/3.0/). 

    Article  Google Scholar 

  • Duan, J., Bevis, M., Fang, P., Bock, Y., Chiswell, S., Businger, S., Rocken, C., Solheim, F., van Hove, T., Ware, R., McClusky, S., Herring, T. A., & King, R. W. (1996). GPS meteorology: Direct estimation of the absolute value of precipitable water. Journal of Applied Meteorology, 35(6), 830–838. https://doi.org/https://doi.org/10.1175/1520-0450(1996)035<0830:GMDEOT>2.0.CO;2.

  • Eliaš, M., Douša, J., & Jarušková, D. (2019). An assessment of method for change-point detection applied in tropospheric parameter time series given from numerical weather model (submitted to Acta Geodyn Geomater).

    Google Scholar 

  • Forkman, P., Elgered, G., & Ning, T. (2017). Accuracy assessment of the two WVRs, Astrid and Konrad, at the Onsala Space Observatory. In Proceedings of the 23rd European VLBI Group for Geodesy and Astrometry Working Meeting, Chalmers University of Technology, Gothenburg, Sweden (pp. 65–69).

    Google Scholar 

  • Foster, J., Bevis, M., & Raymond, W. (2006). Precipitable water and the lognormal distribution. Journal of Geophysical Research, 111, D15102. https://doi.org/10.1029/2005JD006731.

    Article  Google Scholar 

  • Gazeaux, J., et al. (2013). Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment. Journal of Geophysical Research – Solid Earth, 118, 2397–2407. https://doi.org/10.1002/jgrb.50152.

    Google Scholar 

  • GCOS-112. (2007, April). GCOS Reference Upper-Air Network (GRUAN): Justification, requirements, siting and instrumentation options. April 2007. WMO Tech. Doc. No. 1379, WMO.

    Google Scholar 

  • GCOS-171. (2013, March). The GCOS Reference Upper-Air Network (GRUAN) GUIDE. GCOS-171, Version 1.1.0.3, March 2013, WIGOS Technical Report No. 2013–03.

    Google Scholar 

  • Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., & Wargan, K. (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1.

    Article  Google Scholar 

  • Gendt, G. (1997). SINEX TRO – solution (Software/technique) independent exchange format for combination of TROpospheric estimates Version 0.01, 1 March 1997. Available at: https://igscb.jpl.nasa.gov/igscb/data/format/sinex_tropo.txt. (Last access 29 April 2017).

  • Gobinddass, M. L., Willis, P., Sibthorpe, A., Zelensky, N. P., Lemoine, F. G., Ries, J. C., Ferland, R., Bar-Sever, Y. E., de Viron, O., & Diament, M. (2009). Improving DORIS geocenter time series using an empirical rescaling of solar radiation pressure models. Advances in Space Research, 44(11), 1279–1287. https://doi.org/10.1016/j.asr.2009.08.004.

    Article  Google Scholar 

  • Gobinddass, M. L., Willis, P., Menvielle, M., & Diament, M. (2010). Refining DORIS atmospheric drag estimation in preparation of ITRF2008. Advances in Space Research, 46(12), 1566–1577. https://doi.org/10.1016/j.asr.2010.04.004.

    Article  Google Scholar 

  • Gruber C., Auer I., & Böhm R. (2009). Endberichte HOM-OP Austria Aufbau und Installierung eines Tools zur operationellen Homogenisierung von Klimadaten mit 3 Annexen.

    Google Scholar 

  • Guerova, G., Jones, J., Douša, J., Dick, G., de Haan, S., Pottiaux, E., Bock, O., Pacione, R., Elgered, G., Vedel, H., & Bender, M. (2016). Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe. Atmospheric Measurement Techniques, 9, 5385–5406. https://doi.org/10.5194/amt-9-5385-2016.

    Article  Google Scholar 

  • Győri, G., & Douša, J. (2016). GOP-TropDB developments for tropospheric product evaluation and monitoring – Design, functionality and initial results. IAG Symposia Series, Springer, 143, 595–602.

    Google Scholar 

  • Haase, J., Ge, M., Vedel, H., & Calais, E. (2003). Accuracy and variability of GPS tropospheric delay measurements of water vapour in the Western Mediterranean. Journal of Applied Meteorology, 42(11), 1547–1568.

    Article  Google Scholar 

  • Hagemann, S., Bengtsson, L., & Gendt, G. (2003). On the determination of atmospheric water vapour from GPS measurements. Journal of Geophysical Research, 108(D21), 4678. https://doi.org/10.1029/2002JD003235.

    Article  Google Scholar 

  • Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1–4), 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X.

    Article  Google Scholar 

  • Hasegawa, S., & Stokesberry, D. (1975). Automatic digital microwave hygrometer. The Review of Scientific Instruments, 46, 867–873. https://doi.org/10.1063/1.1134331.

    Article  Google Scholar 

  • Healy, S. B. (2011). Refractivity coefficients used in the assimilation of GPS radio occultation measurements. Journal of Geophysical Research, 116, D01106. https://doi.org/10.1029/2010JD014013.

    Article  Google Scholar 

  • Heinkelmann, R., Willis, P., Deng, Z., Dick, G., Nilsson, T., Soja, B., Zus, F., Wickert, J., & Schuh, H. (2016). The effect of the temporal resolution of atmospheric gradients on atmospheric parameters. Advances in Space Research, 58(12), 2758–2773. https://doi.org/10.1016/j.asr.2016.09.023.

    Article  Google Scholar 

  • Heise, S., Bender, M., Beyerle, G., Dick, G., Gendt, G., Schmidt, T., & Wickert, J. (2009). Integrated water vapor from IGS ground-based GPS observations: Initial results from a global 5-minute data set. (Geophysical Research Abstracts, Vol. 11, EGU2009-5330), General Assembly European Geosciences Union (Vienna, Austria 2009).

    Google Scholar 

  • Hourdin, F., Musat, I., Bony, S., Braconnot, P., Codron, F., Dufresne, J. L., et al. (2006). The LMDZ4 general circulation model: Climate performance and sensitivity to parametrized physics with emphasis on tropical convection. Climate Dynamics, 27(7–8), 787–813.

    Article  Google Scholar 

  • Hourdin, F., Foujols, M. A., Codron, F., Guemas, V., Dufresne, J. L., Bony, S., et al. (2013a). Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model. Climate Dynamics, 40(9–10), 2167–2192.

    Article  Google Scholar 

  • Hourdin, F., Grandpeix, J. Y., Rio, C., Bony, S., Jam, A., Cheruy, F., et al. (2013b). LMDZ5B: The atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection. Climate Dynamics, 40(9–10), 2193–2222.

    Article  Google Scholar 

  • ICAO. (1993). Manual of the ICAO standard atmosphere, Doc 7488/3 (3rd ed.). International Civil Aviation Organisation, Montreal.

    Google Scholar 

  • IGSMAIL-6298, Reprocessed IGS Trop Product now available with Gradients. (2012, November 11). http://igscb.jpl.nasa.gov/pipermail/igsmail/2010/007488.html

  • Ingleby, N. B. (1995). Assimilation of station level pressure and errors in station height. Weather and Forecasting 10: 172-182.

    Article  Google Scholar 

  • Jaruskova, D. (1997). Some problems with application of change-point detection methods to environmental data. Environmetrics, 8(5), 469–483.

    Article  Google Scholar 

  • Järvinen, J. and Undén, P. (1997) Observation screening and background quality control in the ECMWF 3D Var data assimilation system. Technical memorandum of the European Centre for Medium-Range Weather Forecasts, 1997, Vol. 236.

    Google Scholar 

  • Jeong, J.-H., Walther, A., Nikulin, G., Chen, D., & Jones, C. (2011). Diurnal cycle of precipitation amount and frequency in Sweden: Observation versus model simulation. Tellus A, 63, 664–674. https://doi.org/10.1111/j.1600-0870.2011.00517.x.

    Article  Google Scholar 

  • Jiang, J. H., Su, H., Zhai, C., Perun, V. S., Del Genio, A., Nazarenko, L. S., et al. (2012). Evaluation of cloud and water vapour simulations in CMIP5 climate models using NASA “A-Train” satellite observations. Journal of Geophysical Research: Atmospheres, 117(D14).

    Google Scholar 

  • Joshi, S., Kumar, K., Pande, B., & Pant, M. C. (2013). GPS-derived precipitable water vapour and its comparison with MODIS data for Almora, Central Himalaya, India. Meteorology and Atmospheric Physics, 120, 177–187. https://doi.org/10.1007/s00703-013-0242-z.

    Article  Google Scholar 

  • Kalakoski, N., Kujanpää, J., Sofieva, V., Tamminen, J., Grossi, M., & Valks, P. (2016). Validation of GOME-2/Metop total column water vapour with ground-based and in situ measurements. Atmospheric Measurement Techniques, 9, 1533–1544. https://doi.org/10.5194/amt-9-1533-2016.

    Article  Google Scholar 

  • Kestin, J., Sengers, J. V., Kamgar-Parsi, B., & Sengers, J. L. (1984). Thermophysical properties of fluid H2O. Journal of Physical and Chemical Reference Data, 13(1), 175–183.

    Article  Google Scholar 

  • King, R. W., & Bock, Y. (2005). Documentation for the GAMIT GPS processing software release 10.2. Massachusetts Institute of Technology, Cambridge.

    Google Scholar 

  • King, R., Herring, T., & Mccluscy, S. (2010). Documentation for the GAMIT GPS analysis software 10.4. Technical Report. Massachusetts Institute of Technology, Cambridge, MA, USA.

    Google Scholar 

  • Klos, A., Bogusz, J., Figurski, M., & Kosek, W.. (2015) On the handling of outliers in the GNSS time series by means of the noise and probability analysis. In C. Rizos, & P. Willis (Eds.), IAG 150 Years. International Association of Geodesy Symposia (Vol. 143). Cham: Springer.

    Google Scholar 

  • Kouba J (2003) A guide to using International GPS Service (IGS) products. Pasadena: IGS Central Bureau (available at http://igscb.jpl.nasa.gov/igscb/resource/pubs/GuidetoUsingIGSProducts.pdf)

  • Kwon, H-. T., Iwabuchi, T., & Lim, G. –H. (2007). Comparison of precipitable water derived from ground-based GPS measurements with radiosonde observations over the Korean Peninsula. Journal of the Meteorological Society of Japan Series II, 85(6), 733–746.

    Google Scholar 

  • Lagler, K., Schindelegger, M., Böhm, J., Krásná, H., & Nilsson, T. (2013). GPT2: Empirical slant delay model for radio space geodetic techniques. Geophysical Research Letters, 40, 1069–1073. https://doi.org/10.1002/grl.50288.

    Article  Google Scholar 

  • Lanzante, J. (1996). Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. International Journal of Climatology, 16, 1197–1226.

    Article  Google Scholar 

  • Leduc, D. J. (1987). A comparative analysis of the reduced major axis technique of fitting lines to bivariate data. Canadian Journal of Forest Research, 17, 654–659.

    Article  Google Scholar 

  • Li, X., Zus, F., Lu, C., Ning, T., Dick, G., Ge, M., Wickert, J., & Schuh, H. (2015). Retrieving high-resolution tropospheric gradients from multiconstellation GNSS observations. Geophysical Research Letters, 42(10), 4173–4181.

    Article  Google Scholar 

  • Liu, H., Shah, S., & Jiang, W. (2004). On-line outlier detection and data cleaning. Computers and Chemical Engineering, 28(9), 1635–1647. https://doi.org/10.1016/j.compchemeng.2004.01.009.

    Article  Google Scholar 

  • Martínez, M. A., & Calbet, X. (2016). Algorithm theoretical basis document for the clear air products processor of the NWC/GEO. [online] Available from: http://www.nwcsaf.org/AemetWebContents/ScientificDocumentation/Documentation/GEO/v2016/NWC-CDOP2-GEO-AEMET-SCI-ATBD-ClearAir_v1.1.pdf

  • Meindl, M., Schaer, S., Hugentobler, U., & Beutler, G. (2004). Tropospheric gradient estimation at CODE: Results from global solutions. Journal of the Meteorological Society of Japan, 82, 331–338.

    Article  Google Scholar 

  • Mestre, O., Gruber, C., Prieur, C., Caussinus, H., & Jourdain, S. (2011). SPLIDHOM: A method for homogenization of daily temperature observations. Journal of Applied Meteorology and Climatology, 50, 2343–2358.

    Article  Google Scholar 

  • Mieruch, S., Schröder, M., Noël, S., & Schulz, J. (2014). Comparison of decadal global water vapour changes derived from independent satellite time series. Journal of Geophysical Research – Atmospheres, 119(22), 12489–12499. https://doi.org/10.1002/2014JD021588.

    Article  Google Scholar 

  • Miller, M. A., & Slingo, A. (2007). The arm mobile facility and its first international deployment: Measuring radiative flux divergence in West Africa. Bulletin of the American Meteorological Society, 88, 1229–1244. https://doi.org/10.1175/BAMS-88-8-1229.

    Article  Google Scholar 

  • Mircheva, B., Tsekov, M., Meyer, U., & Guerova, G. (2017). Anomalies of hydrological cycle components during the 2007 heat wave in Bulgaria. Journal of Atmospheric and Solar-Terrestrial Physics, 165-166, 1–9. https://doi.org/10.1016/j.jastp.2017.10.005.

    Article  Google Scholar 

  • Morel, L., Pottiaux, E., Durand, F., Fund, F., Follin, J. M., Durand, S., Bonifac, K., Oliveira, P. S., van Baelen, J., Montibert, C., Cavallo, T., Escaffit, R., & Fragnol, L. (2015). Global validity and behaviour of tropospheric gradients estimated by GPS. Presentation at the 2nd GNSS4SWEC Workshop held in Thessaloniki, Greece, May 11–14.

    Google Scholar 

  • Morland, J., Liniger, M. A., Kunz, H., Balin, I., Nyeki, S., Mätzler, C., & Kämpfer, N. (2006). Comparison of GPS and ERA40 IWV in the Alpine region, including correction of GPS observations at Jungfraujoch (3584 m). Journal of Geophysical Research, 111, D04102. https://doi.org/10.1029/2005JD006043.

    Article  Google Scholar 

  • Munn, R. E. (1966). Descriptive micrometeorology. New York: Academic Press.

    Google Scholar 

  • Nahmani, S., & Bock, O. (2014). Sensitivity of GPS measurements and estimates during extreme meteorological events: The issue of stochastic constraints used for ZWD estimation in West Africa. Joint ES1206 MC and MC meeting, Golden Sands Resort, 11/09/2014 to 12/09/2014, Varna, Bulgaria.

    Google Scholar 

  • Nahmani, S., Rebischung, P., & Bock, O. (2016). Statistical modelling of ZWD in GNSS processing. 3rd ES1206 Workshop, Rugbrauðsgerdin, 08-03-2016 to 10-03-2016, Reykjavik, Iceland.

    Google Scholar 

  • Nahmani, S., Rebischung, P., & Bock, O. (2017). Bayesian approach to apply optimal constraints on tropospheric parameters in GNSS data processing: Implications for meteorology. ES1206 Final Workshop, ESTEC, 2017-02-21 to 2017-02-23. Noordwijk, Netherlands.

    Google Scholar 

  • Niell, A. E. (1996) Global mapping functions for the atmospheric delay at radio wavelengths, Journal of Geophysical Research, 101(B2), doi: https://doi.org/10.1029/95JB03048, 1996, pp 3227–3246.

    Article  Google Scholar 

  • Niell, A. E. (2000). Improved atmospheric mapping functions for VLBI and GPS. Earth Planet Sp, 52, 699–702. https://doi.org/10.1186/BF03352267.

    Article  Google Scholar 

  • Nilsson, T., Soja, B., Karbon, M., Heinkelmann, R., & Schuh, H. (2015). Application of Kalman filtering in VLBI data analysis. Earth, Planets and Space, 67(136), 1–9. https://doi.org/10.1186/s40623-015-0307-y.

    Article  Google Scholar 

  • Ning, T., & Elgered, G. (2012). Trends in the atmospheric water vapour content from ground-based GPS: the impact of the elevation cutoff angle. IEEE J-STARS, 5(3), 744–751. https://doi.org/10.1109/JSTARS.2012.2191392.

    Article  Google Scholar 

  • Ning, T., Elgered, G., & Johansson, J. (2011). The impact of microwave absorber and radome geometries on GNSS measurements of station coordinates and atmospheric water vapour. Advances in Space Research, 47, 186–196.

    Article  Google Scholar 

  • Ning, T., Haas, R., Elgered, G., & Willén, U. (2012). Multi-technique comparisons of 10 years of wet delay estimates on the west coast of Sweden. Journal of Geodesy, 86, 565–575. https://doi.org/10.1007/s00190-011-0527-2.

    Article  Google Scholar 

  • Ning, T., Elgered, G., Willén, U., & Johansson, J. M. (2013). Evaluation of the atmospheric water vapour content in a regional climate model using ground-based GPS measurements. Journal of Geophysical Research, 118, 1–11. https://doi.org/10.1029/2012JD018053.

    Article  Google Scholar 

  • Ning, T., Wang, J., Elgered, G., Dick, G., Wickert, J., Bradke, M., Sommer, M., Querel, R., and Smale, D. (2016a) The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations, Atmospheric Measurement Techniques, 9, 79–92, doi:https://doi.org/10.5194/amt-9-79-2016 (licensed under CC BY 3.0, https://creativecommons.org/licenses/by/3.0/).

  • Ning, T., Wickert, J., Deng, Z., Heise, S., Dick, G., Vey, S., & Schöne, T. (2016b). Homogenized Time Series of the Atmospheric Water Vapour Content Obtained from the GNSS Reprocessed Data. Journal of Climate, 29, 2443–2456. https://doi.org/10.1175/JCLI-D-15-0158.1.

    Article  Google Scholar 

  • Ning, T., Elgered, G., & Heise, S. (2017). Trends in the atmospheric water vapour estimated from two decades of ground-based GPS data: Sensitivity to the elevation cutoff angle. In Proceedings of 6th international colloquium scientific and fundamental aspects of the Galileo programme, Valencia, Spain, 25–27 October 2017.

    Google Scholar 

  • Ningombam, S. S., Jade, S., Shrungeshwara, T. S., & Song, H.-J. (2016). Validation of water vapour retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region. Journal of Atmospheric and Solar-Terrestrial Physics, 137, 76–85,. ISSN 1364-6826. https://doi.org/10.1016/j.jastp.2015.11.019.

    Article  Google Scholar 

  • Nyeki, S., Vuilleumier, L., Morland, J., Bokoye, A., Viatte, P., Mätzler, C., & Kämpfer, N. (2005). A 10-year integrated atmospheric water vapour record using precision filter radiometers at two high-alpine sites. Geophysical Research Letters, 32, L23803. https://doi.org/10.1029/2005GL024079.

    Article  Google Scholar 

  • Ohtani, R., & Naito, I. (2000). Comparisons of GPS-derived precipitable water vapors with radiosonde observations in Japan. Journal of Geophysical Research, 105(D22), 26917–26929. https://doi.org/10.1029/2000JD900362.

    Article  Google Scholar 

  • Owens, J. C. (1967). Optical Refractive Index of Air: Dependence on Pressure, Temperature and Composition. Applied Optics, 6(1), 51–59.

    Article  Google Scholar 

  • Pacione R., & Douša J. (2017). SINEX-TRO V2.00 format description, COST Action 1206 Final Report, J. Jones et al eds.

    Google Scholar 

  • Pacione, R., Pace, B., de Haan, S., Vedel, H., Lanotte, R., & Vespe, F. (2011). Combination methods of tropospheric time series. Advances in Space Research, 47, 323–335. https://doi.org/10.1016/j.asr.2010.07.021.

    Article  Google Scholar 

  • Pacione, R., Pace, B., & Bianco, G.. (2014a). Homogeneously reprocessed ZTD long-term time series over Europe, EGU GA 2014. http://meetingorganizer.copernicus.org/EGU2014/EGU2014-2945.pdf

  • Pacione, R., O. Bock, & Douša, J. (2014b). GNSS atmospheric water vapor retrieval methods. COST Action ES1206 – GNSS4SWEC, 1st Workshop, Munich, 26–28 February 2014.

    Google Scholar 

  • Pacione, R., Araszkiewicz, A., Brockmann E., & Douša J. (2017). EPN-Repro2: a reference tropospheric data set over Europe, Atmospheric Measurement Techniques, 10, 1689–1705, https://doi.org/10.5194/amt-10-1689-2017 (licensed under CC BY 3.0, https://creativecommons.org/licenses/by/3.0/)..

    Article  Google Scholar 

  • Parracho, A. (2017). Study of trends and variability of atmospheric integrated water vapour with climate models and observations from global GNSS network. PhD report from Université Pierre et Marie Curie, Paris, France.

    Google Scholar 

  • Parracho, A. C., Bock, O., & Bastin, S. (2018). Global IWV trends and variability in atmospheric reanalyses and GPS observations, Atmospheric Chemistry and Physics Discussions. https://doi.org/10.5194/acp-2018-137, in review (licensed under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).

    Google Scholar 

  • Pérez-Ramírez, D., Whiteman, D. N., Smirnov, A., Lyamani, H., Holben, B. N., Pinker, R., Andrade, M., & Alados-Arboledas, L. (2014). Evaluation of AERONET precipitable water vapour versus microwave radiometry, GPS, and radiosondes at ARM sites. Journal of Geophysical Research – Atmospheres, 119, 9596–9613. https://doi.org/10.1002/2014JD021730.

    Article  Google Scholar 

  • Petit, G., & Luzum, B. (2010). IERS Conventions, 2010. IERS Technical Note No. 36. Frankfurt am Main: Verlag des Bundesamts für Kartographie und Geodäsie, ISBN 3-89888-989-6.

    Google Scholar 

  • Pettitt, A. N. (1979). A nonparametric approach to the change-point problem. Applied Statistics, 28, 126–135. https://doi.org/10.2307/2346729.

    Article  Google Scholar 

  • Pierce, D. W., Barnett, T. P., AchutaRao, K. M., Gleckler, P. J., Gregory, J. M., & Washington, W. M. (2006). Anthropogenic warming of the oceans: Observations and model results. Journal of Climate, 19(10), 1873–1900.

    Article  Google Scholar 

  • Prasad, A. K., & Singh, R. P. (2009). Validation of MODIS Terra, AIRS, NCEP/DOE AMIP-II Reanalysis-2, and AERONET Sun photometer derived integrated precipitable water vapour using ground-based GPS receivers over India. Journal of Geophysical Research, 114, D05107. https://doi.org/10.1029/2008JD011230.

    Article  Google Scholar 

  • Román, R., Antón, M., Cachorro, V. E., Loyola, D., Ortiz de Galisteo, J. P., de Frutos, A., & Romero-Campos, P. M. (2015). Comparison of total water vapour column from GOME-2 on MetOp-A against ground-based GPS measurements at the Iberian Peninsula. Science of the Total Environment, 533, 317–328,. ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2015.06.124.

    Article  Google Scholar 

  • Rüeger, J. (2002) Refractive index formulae for electronic distance measurements with radio and millimetre waves. Unisurv Rep. 109, pp. 758–766, Univerity of New South Wales, Sydney, Australia.

    Google Scholar 

  • Saastamoinen, J. (1972). Atmospheric correction for the troposphere and stratosphere in radio ranging of satellites. The use of artificial satellites for geodesy, American Geophysics Union. Geophysics Monograph Series, 15, 274–251.

    Google Scholar 

  • Schmid, R., Dach, R., Collilieux, X., Jäggi, A., Schmitz, M., & Dilssner, F. (2016). Absolute IGS antenna phase center model igs08.atx: status and potential improvements. Journal of Geodesy, 90, 343–364. https://doi.org/10.1007/s00190-015-0876-3.

    Article  Google Scholar 

  • Schneider, M., Romero, P. M., Hase, F., Blumenstock, T., Cuevas, E., & Ramos, R. (2010). Continuous quality assessment of atmospheric water vapour measurement techniques: FTIR, Cimel, MFRSR, GPS, and Vaisala RS92. Atmospheric Measurement Techniques, 3, 323–338. https://doi.org/10.5194/amt-3-323-2010.

    Article  Google Scholar 

  • Schröder, M., Lockhoff, M., Forsythe, J. M., Cronk, H. Q., Vonder Haar, T. H., & Bennartz, R. (2016). The GEWEX water vapour assessment: Results from intercomparison, trend, and homogeneity analysis of total column water vapour. Journal of Applied Meteorology and Climatology, 55, 1633–1649. https://doi.org/10.1175/JAMC-D-15-0304.1.

    Article  Google Scholar 

  • Selle, C. & Desai, S. (2016). Optimization of tropospheric delay estimation parameters by comparison of GPS-based precipitable water vapour estimates with microwave radiometer measurements. IGS Workshop 2016, 8–12 February 2016, Sydney, NSW, Australia.

    Google Scholar 

  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389,. JSTOR 2285891, MR 0258201. https://doi.org/10.2307/2285891.

    Article  Google Scholar 

  • Smith, E., & Weintraub, S. (1953). The constants in the equation for atmospheric refractive index at radio frequencies. Proceedings of the IRE, 41, 1035–1037.

    Article  Google Scholar 

  • Soden, B. J., Jackson, D. L., Ramaswamy, V., Schwarzkopf, M. D., & Huang, X. (2005). The radiative signature of upper tropospheric moistening. Science, 310(5749), 841–844.

    Article  Google Scholar 

  • Sohn, D.-H., Park, K.-D., Won, J., Cho, J., & Roh, K.-M. (2012). Comparison of the characteristics of precipitable water vapour measured by Global Positioning System and microwave radiometer. Journal of Astronomy Space Science, 29, 1–10. https://doi.org/10.5140/JASS.2012.29.1.001.

    Article  Google Scholar 

  • Steigenberger, P., Boehm, J., & Tesmer, V. (2009). Comparison of GMF/GPT with VMF1/ECMWF and Implications for Atmospheric Loading. Journal of Geodesy, 83, 943–951. https://doi.org/10.1007/s00190-009-0311-8.

    Article  Google Scholar 

  • Steigenberger, P., Lutz, S., Dach, R., Schaer, S., & Jäggi, A. (2014). CODE repro2 product series for the IGS. Published by Astronomical Institute, University of Bern. URL: http://www.aiub.unibe.ch/download/REPRO_2013; https://doi.org/10.7892/boris.75680.

  • Stepniak, K., Bock, O., & Wielgosz, P. (2015). Assessment of ZTD screening methods and analysis of water vapour variability over Poland. 2nd COST GNSS4SWEC Workshop, Thessaloniki, Greece, 11–14 May 2015.

    Google Scholar 

  • Stepniak, K., Bock, O., & Wielgosz, P. (2016). Improved methods for reprocessing of GNSS data for climate monitoring over Poland. Presented at 3rd ES1206 Workshop, 8–11.03.2016, Reykjavik, Iceland.

    Google Scholar 

  • Stepniak, K., Bock, O., & Wielgosz, P. (2017). Reduction of ZTD outliers through improved GNSS data processing and screening strategies. Atmospheric Measurement Techniques Discussions. https://doi.org/10.5194/amt-2017-371, 2017 (licensed under CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).

  • Thayer, G. (1974). An improved equation for the refractive index of air. Radio Science, 9(10), 803–807. https://doi.org/10.1029/RS009i010p00803.

    Article  Google Scholar 

  • Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis, I, II, III, Nederl. Akad Wetensch Proceedings, 53, 386–392, 521–525, 1397–1412, MR 0036489. https://doi.org/10.1007/978-94-011-2546-8_20.

    Article  Google Scholar 

  • Thorne, P. W., & Vose, R. S. (2010). Reanalyses suitable for characterizing long-term trends: Are they really achievable? Bulletin of the American Meteorological Society, 91(3), 353–361.

    Article  Google Scholar 

  • Torres, B., Cachorro, V. E., Toledano, C., Ortiz de Galisteo, J. P., Berjón, A., de Frutos, A. M., Bennouna, Y., & Laulainen, N. (2010). Precipitable water vapour characterization in the Gulf of Cadiz region (southwestern Spain) based on Sun photometer, GPS, and radiosonde data. Journal of Geophysical Research, 115, D18103. https://doi.org/10.1029/2009JD012724.

    Article  Google Scholar 

  • Tregoning, P., & Watson, C. (2009). Atmospheric effects and spurius signals in GPS analyses. Journal of Geophysical Research, 114. https://doi.org/10.1029/2009JB006344.

  • Trenberth, K. E., Fasullo, J., & Smith, L. (2005). Trends and variability in column-integrated atmospheric water vapour. Climate Dynamics, 24(7–8), 741–758. https://doi.org/10.1007/s00382-005-0017-4.

    Article  Google Scholar 

  • Urban, J. (2013). Satellite sensors measuring atmospheric water vapour. In Monitoring Atmospheric Water Vapour, ISSI Scientific Report Series, 10, Kämpfer, N. https://doi.org/10.1007/978-1-4614-3909-7_9.

    Google Scholar 

  • Václavovic, P., & Douša, J. (2016) G-Nut/Anubis – Open-source tool for multi-GNSS data monitoring. In Ch. Rizos, & P. Willis (Eds.), IAG 150 Years. IAG Symposia Series (Vol. 143, pp. 775–782). Springer.

    Google Scholar 

  • Van Malderen, R., Brenot, H., Pottiaux, E., Beirle, S., Hermans, C., De Mazière, M., Wagner, T., De Backer, H., & Bruyninx, C. (2014). A multi-site intercomparison of integrated water vapour observations for climate change analysis. Atmospheric Measurement Techniques, 7, 2487–2512. https://doi.org/10.5194/amt-7-2487-2014.

    Article  Google Scholar 

  • Vaquero-Martínez, J., Antón, M., Pablo Ortiz de Galisteo, J., Cachorro, V. E., Costa, M. J., Román, R., & Bennouna, Y. S. (2017a). Validation of MODIS integrated water vapour product against reference GPS data at the Iberian Peninsula. International Journal of Applied Earth Observation and Geoinformation, 63, 214–221,. ISSN 0303-2434. https://doi.org/10.1016/j.jag.2017.07.008.

    Article  Google Scholar 

  • Vaquero-Martínez, J., Antón, M., Pablo Ortiz de Galisteo, J., Cachorro, V. E., Wang, H., Abad, G. G., Román, R., & Costa, M. J. (2017b). Validation of integrated water vapour from OMI satellite instrument against reference GPS data at the Iberian Peninsula. Science of the Total Environment, 580, 857–864,. ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2016.12.032.

    Article  Google Scholar 

  • Vaquero-Martínez, J., Antón, M., de Galisteo, J. P. O., Victoria E. Cachorro, Álvarez-Zapatero, P., Román, R., Loyola, D., Costa, M. J., Wang, H., Abad, G. G., & Noël, S. (2017c) Inter-comparison of integrated water vapour from satellite instruments using reference GPS data at the Iberian Peninsula. In Remote Sensing of Environment. ISSN 0034-4257. https://doi.org/10.1016/j.rse.2017.09.028

    Article  Google Scholar 

  • Vázquez B, G. E., & Grejner-Brzezinska, D. A. (2013). GPS-PWV estimation and validation with radiosonde data and numerical weather prediction model in Antarctica. GPS Solutions, 17, 29–39. https://doi.org/10.1007/s10291-012-0258-8.

    Article  Google Scholar 

  • Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., & Brandsma, T. (2012). Benchmarking homogenization algorithms for monthly data. Climate of the Past, 8, 89–115. https://doi.org/10.5194/cp-8-89-2012.

    Article  Google Scholar 

  • Vey, S., Dietrich, R., Fritsche, M., Rülke, A., Rothacher, M., & Steigenberger, P. (2006). Influence of mapping functions parameters on global GPS network analyses: Comparison between NMF and IMF. Geophysical Research Letters, 33, L01814. https://doi.org/10.1029/2005GL024361.

    Article  Google Scholar 

  • Vey, S., Dietrich, R., Fritsche, M., Rülke, A., Steigenberger, P., & Rothacher, M. (2009). On the homogeneity and interpretation of precipitable water time series derived from global GPS observations. Journal of Geophysical Research, 114, D10101. https://doi.org/10.1029/2008JD010415.

    Article  Google Scholar 

  • Vey, S., Dietrich, R., Rülke, A., Fritsche, M., Steigenberger, P., & Rothacher, M. (2010). Validation of precipitable water vapour within the NCEP/DOE reanalysis using global GPS observations from one decade. Journal of Climate, 23, 1675–1695. https://doi.org/10.1175/2009JCLI2787.1.

    Article  Google Scholar 

  • Vincent, L. A., Zhang, X., Bonsal, B. R., & Hogg, W. D. (2002). Homogenization of daily temperatures over Canada. Journal of Climate, 15, 1322–1334. https://doi.org/10.1175/1520-0442(2002)015<1322:HODTOC>2.0.CO;2.

    Article  Google Scholar 

  • Voelksen, C. (2011). An update on the EPN reprocessing project: Current achievements and status. Presented at EUREF 2011 symposium, 25–28 May 2011, Chisinau, Republic of Moldova. Available at: http://www.epncb.oma.be/_documentation/papers/eurefsymposium2011/an_update_on_epn_reprocessing_project_current_

  • Von Neumann, J. (1941). Distribution of the ratio of the mean square successive difference to the variance. Annals of Mathematical Statistics, 13, 367–395.

    Article  Google Scholar 

  • Wang, J., Zhang, L., & Dai, A. (2005). Global estimates of water-vapour-weighted mean temperature of the atmosphere for GPS applications. Journal of Geophysical Research: Atmosphers, 110(D21).

    Google Scholar 

  • Wang, J., Zhang, L., Dai, A., Van Hove, T., & Van Baelen, J. (2007a). A near-global, 2-hourly data set of atmospheric precipitable water from ground-based GPS measurements. Journal of Geophysical Research, 112, D11107. https://doi.org/10.1029/2006JD007529.

    Article  Google Scholar 

  • Wang, X. L., Wen, Q. H., & Wu, Y. (2007b). Penalized maximal t test for detecting undocumented mean change in climate data series. Journal of Applied Meteorology and Climatology, 46, 916–931. https://doi.org/10.1175/JAM2504.1.

    Article  Google Scholar 

  • Wang, J., Zhang, L., Dai, A., Van Hove, T., & Van Baelen, J. (2007c). A near-global, 2-hourly data set of atmospheric precipitable water from ground-based GPS measurements. Journal of Geophysical Research, 112, D11107. https://doi.org/10.1029/2006JD007529.

    Article  Google Scholar 

  • Wang, H., Gonzalez Abad, G., Liu, X., & Chance, K. (2016a). Validation and update of OMI total column water vapour product. Atmospheric Chemistry and Physics, 16, 11379–11393. https://doi.org/10.5194/acp-16-11379-2016.

    Article  Google Scholar 

  • Wang, X., Zhang, K., Wu, S., Fan, S., & Cheng, Y. (2016b). Water vapour-weighted mean temperature and its impact on the determination of precipitable water vapour and its linear trend. Journal of Geophysical Research – Atmospheres, 121, 833–852. https://doi.org/10.1002/2015JD024181.

    Article  Google Scholar 

  • Wang, X., Zhang, K., Wu, S., He, C., Cheng, Y., & Li, X. (2017). Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapour. Atmospheric Measurement Techniques, 10, 2807–2820. https://doi.org/10.5194/amt-10-2807-2017.

    Article  Google Scholar 

  • Webb, F. H. and Zumberge, J. F. (1997) An introduction to GIPSY/OASIS II, Jet Propulsion Laboratory Document JPL D-11088. California Institute of Technology.

    Google Scholar 

  • Wijngaard, J. B., Klein Tank, A. M. G., & Können, G. P. (2003). Homogeneity of 20th century European daily temperature and precipitation series. International Journal of Climatology, 23, 679–692. https://doi.org/10.1002/joc.906.

    Article  Google Scholar 

  • Willis, P., Gobinddass, M.-L., Garayt, B., & Fagard, H. (2012). Recent improvements in DORIS data processing in view of ITRF2008, the ignwd08 solution. IAG Symposium, 136, 43–49. https://doi.org/10.1007/978-3-642-20338-1_6.

    Article  Google Scholar 

  • Willis, P., Bock, O., & Bar-Sever, Y. E. (2014). DORIS tropospheric estimation at IGN: current strategies, GPS intercomparisons and perspectives. IAG Symposium, 139, 11–18. https://doi.org/10.1007/978-3-642-37222-3_2.

    Article  Google Scholar 

  • Willis, P., Heflin, M. B., Haines, B. J., Bar-Sever, Y. E., Bertiger, W. B., & Mandea, M. (2016a). Is the Jason2 DORIS oscillator still affected by the South Atlantic Anomaly? Advances in Space Research, 58(12), 2617–2627. https://doi.org/10.1016/j.asr.2016.09.015.

    Article  Google Scholar 

  • Willis, P., Zelensky, N. P., Ries, J. C., Soudarin, L., Cerri, L., Moreaux, G., Lemoine, F. G., Otten, M., Argus, D. F., & Heflin, M. B. (2016b). DPOD2008, A DORIS-oriented terrestrial reference frame for precise orbit determination. IAG Symposium, 143, 175–181. https://doi.org/10.1007/1345_2015_125.

    Article  Google Scholar 

  • Wolter, K., & Timlin, M. S. (1993) Monitoring ENSO in COADS with a seasonally adjusted principal component index. In Proceedings of the 17th climate diagnostics workshop (Vol. 5257).

    Google Scholar 

  • Wolter, K., & Timlin, M. S. (1998). Measuring the strength of ENSO events: How does 1997/98 rank? Weather, 53(9), 315–324. https://doi.org/10.1002/j.1477-8696.1998.tb06408.x.

    Article  Google Scholar 

  • Yao, Y.-C., & Davis, R. A. (1986). The asymptotic behaviour of the likelihood ratio statistic for testing a shift in mean in a sequence of independent normal variates. Sankhya, 48, 339–353.

    Google Scholar 

  • Zumberge, J. F., Heflin, M. B., Jefferson, D. C., & Watkins, M. M. (1997). Precise point positioning for the efficient and robust analysis of GPS data from large networks. Journal of Geophysical Research – Solid Earth, 102, 5005–5017.

    Article  Google Scholar 

  • Zus, F., Dick, G., Douša, J., Heise, S., & Wickert, J. (2014). The rapid and precise computation of GPS slant total delays and mapping factors utilizing a numerical weather model. Radio Science, 49, 207–216. https://doi.org/10.1002/2013RS005280.

    Article  Google Scholar 

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Authors and Affiliations

  1. Geodesy and Geospatial Engineering, Institute of Civil Engineering and Environment, University of Luxembourg, Luxembourg City, Luxembourg

    F. Ahmed

  2. Centre of Applied Geomatics, Warsaw Military University of Technology, Warszawa, Poland

    A. Araszkiewicz, Z. Bałdysz, J. Bogusz, M. Figurski, M. Gruszczynska, A. Klos & G. Nykiel

  3. GFZ German Research Centre for Geosciences, Potsdam, Germany

    K. Balidakis, R. Heinkelmann, R. T. Nilsson & H. Schuh

  4. Instituto Português do Mar e da Atmosfera, Lisbon, Portugal

    C. Barroso, J. P. Martins & P. Viterbo

  5. Université Paris-Saclay, Saint-Aubin, France

    S. Bastin

  6. Sorbonne Universités, Paris, France

    S. Bastin

  7. Max-Planck-Institute for Chemistry, Mainz, Germany

    S. Beirle

  8. Royal Meteorological Institute of Belgium, Brussels, Belgium

    J. Berckmans

  9. IGN Institut national de l’information géographique et forestière, Paris, France

    O. Bock, S. Nahmani, A. Parracho, P. Rebischung & P. Willis

  10. Department of Geodesy and Geoinformation, TU Wien, Wien, Austria

    J. Böhm, D. Landskron & A. Xaver

  11. University of Beira Interior, Covilhã, Portugal

    M. Bos, R. Fernandes & H. Valentim

  12. Swiss Federal Office of Topography, Köniz, Switzerland

    E. Brockmann

  13. Argonne National Laboratory, Lemont, IL, USA

    M. Cadeddu

  14. Central Institute for Meteorology and Geodynamics, Veinna, Austria

    B. Chimani

  15. Geodetic Observatory Pecný, RIGTC, Ondřejov, Czech Republic

    J. Douša

  16. Geodetic Observatory Pecný, Research Institute of Geodesy, Topography and Cartography, Ondřejov, Czech Republic

    M. Eliaš

  17. Chalmers University of Technology, Göteborg, Sweden

    G. Elgered

  18. Fondazione Ugo Bordoni, Rome, Italy

    E. Fionda

  19. Physics Faculty, Department of Meteorology and Geophysics, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria

    G. Guerova & B. Mircheva

  20. AEMET, Madrid, Spain

    J. Guijarro

  21. United States Naval Observatory, Washington, DC, USA

    C. Hackman

  22. Met Office, Exeter, UK

    J. Jones

  23. Karadeniz Technical University, Trabzon, Turkey

    S. Zengin Kazancı

  24. Centre of Excellence Telesensing of Environment and Model Prediction of Severe Events, University of L’Aquila, L’Aquila, AQ, Italy

    V. Mattioli

  25. The Swedish Mapping, Cadastral and Land Registration Authority, Stockholm, Sweden

    T. Ning

  26. e-GEOS/Centro di Geodesia Spaziale-Agenzia Spaziale Italiana, Matera, MT, Italy

    R. Pacione

  27. Royal Observatory of Belgium, Brussels, Belgium

    E. Pottiaux

  28. Instituto Dom Luiz, University of Lisbon, Lisbon, Portugal

    A. Ramos

  29. Polytechnic Institute of Guarda, Guarda, Portugal

    A. Sá

  30. Department of Geodesy and Geoinformation, TU Wien, Wien, Austria

    W. Dorigo

  31. Vilnius University, Vilnius, Lithuania

    G. Stankunavicius

  32. Advanced Methods for Satellite Positioning Laboratory, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland

    K. Stępniak

  33. Royal Meteorological Institute of Belgium, Brussels, Portugal

    R. Van Malderen

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  1. O. Bock
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  2. R. Pacione
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  3. F. Ahmed
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Corresponding author

Correspondence to O. Bock .

Editor information

Editors and Affiliations

  1. Met Office, Exeter, UK

    Jonathan Jones

  2. Physics Faculty, Department of Meteorology and Geophysics, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria

    Guergana Guerova

  3. Geodetic Observatory Pecný, RIGTC, Ondřejov, Czech Republic

    Jan Douša

  4. GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, Potsdam, Germany

    Galina Dick

  5. Royal Netherlands Meteorological Institute, De Bilt, The Netherlands

    Siebren de Haan

  6. Royal Observatory of Belgium, Brussels, Belgium

    Eric Pottiaux

  7. IGN Institut national de l’information géographique et forestière, Paris, France

    Olivier Bock

  8. e-GEOS/Centro di Geodesia Spaziale-Agenzia Spaziale Italiana, Matera, Matera, Italy

    Rosa Pacione

  9. Royal Meteorological Institute (RMI), Brussels, Belgium

    Roeland van Malderen

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Bock, O. et al. (2020). Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3). In: Jones, J., et al. Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate. Springer, Cham. https://doi.org/10.1007/978-3-030-13901-8_5

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