Estimation of precipitable water vapour by GNSS meteorology in Black Sea region of Türkiye
-
Emine Tanır Kayıkçı
,
Kutubuddin Ansari
,
Vincenza Tornatore
Abstract
This study investigates the estimation of precipitable water vapour (PWV) using Global Navigation Satellite System (GNSS) meteorology in the Black Sea region of Türkiye, an area characterized by high rainfall and significant climatic variability. GNSS observations from four permanent stations (SAME, SOMU, MACK, and TRAB) between October 2017 and December 2019 were processed with Bernese 5.2 software to retrieve zenith total delay and derive PWV. The Vienna Mapping Function 1 (VMF1) was applied with a 10° elevation cut-off angle, and orography-dependent pressure and temperature fields were used for tropospheric modeling. The results were compared with radiosonde measurements from Samsun station and ERA-Interim reanalysis data to assess reliability. Findings show that GNSS-derived PWV varied between 1 and 46 mm, with strong seasonal and diurnal cycles higher in summer and daytime, and lower in winter and nighttime. Radiosonde PWV exhibited strong correlation with GNSS PWV (r > 0.9), while ERA-Interim tended to overestimate values, particularly at higher elevation sites. GNSS PWV demonstrated a strong positive correlation with surface temperature (∼0.8) and an inverse relationship with atmospheric pressure. These results confirm the potential of GNSS meteorology as a reliable tool for continuous and high-resolution monitoring of atmospheric water vapour, supporting weather forecasting and climate studies in the Black Sea region.
Funding source: The Scientific and Technological Research Council of Türkiye (TÜBİTAK) under the 1001 – Scientific and Technological Research Projects Funding Program.
Award Identifier / Grant number: 116Y186
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: ETK and KA: writing and draft preparation; VT, SZK and MYK: contributed to improve the manuscript.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The author states no conflict of interest.
-
Research funding: A major part of this study was conducted within the scope of the research project “116Y186 – Using Regional GNSS Networks to Strengthen Severe Weather Prediction”, supported by The Scientific and Technological Research Council of Türkiye (TÜBİTAK) under the 1001 – Scientific and Technological Research Projects Funding Program.
-
Data availability: The GNSS data collected in the Black Sea region are available from TUSAGA-Aktif (https://www.tkgm.gov.tr/tr/icerik/tusaga-aktif-0), while the data from SAME, SOMU, TRAB and MACK GNSS stations were obtained within the scope of the project and archived on a high-capacity server located at the IT Department of Karadeniz Technical University (KTU), under an FTP address designated for the 116Y186 research project project (GNSS FTP Server). The ERA-Interim data set is obtained from ECMWF archives (http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/).
References
1. Norquist, DC, Chang, SS. Diagnosis and correction of systematic humidity error in a global numerical weather prediction model. Mon Weather Rev 1994;122:2442–60. https://doi.org/10.1175/1520-0493(1994)122<2442:DACOSH>2.0.CO;2.10.1175/1520-0493(1994)122<2442:DACOSH>2.0.CO;2Suche in Google Scholar
2. Ansari, K, Walo, J, Wezka, K, Marjanska, D, Jamjareegulgarn, P. Thin-plate spline interpolation spatial modelling of GNSS retrieved water vapor over Northeast Japan. Int J Climatol 2025;45:1–14. https://doi.org/10.1002/joc.8704.Suche in Google Scholar
3. Sierk, B. Solar spectrometry for determination of tropospheric water vapor [Dissertation No. 13850]. Zürich: Swiss Federal Institute of Technology Zürich; 2001:62 p.Suche in Google Scholar
4. Troller, MR. GPS based determination of the integrated and spatially distributed water vapor in the troposphere [Ph.D. thesis]. Zurich, Switzerland: Swiss Zurich Federal Institute of Technology; 2004.Suche in Google Scholar
5. Lutz, SL. High-resolution GPS tomography in view of hydrological hazard assessment [Ph.D. thesis]. Zurich, Switzerland: Swiss Zurich Federal Institute of Technology; 2008:219 p.Suche in Google Scholar
6. Mekik, C, Yildirim, O, Bakici, S. The Turkish real time kinematic GPS network (TUSAGA-Aktif) infrastructure. Sci Res Essays 2011;6:3986–99. https://doi.org/10.5897/sre10.923.Suche in Google Scholar
7. Abdellaoui, H, Zaourar, N, Kahlouche, S. Contribution of permanent stations GPS data to estimate the water vapor content over Algeria. Arabian J Geosci 2019;12:81. https://doi.org/10.1007/s12517-019-4226-2.Suche in Google Scholar
8. Bevis, M, Businger, S, Herring, TA, Rocken, C, Anthes, RA, Ware, RH. GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J Geophys Res Atmos 1992;97:15787–801. https://doi.org/10.1029/92JD01517.Suche in Google Scholar
9. Ansari, K, Jamjareegulgarn, P. Effect of weighted PDOP on performance of linear Kalman filter for RTK drone data. IEEE Geosci Remote Sens Lett 2022;19:1–4. https://doi.org/10.1109/LGRS.2022.3204323.Suche in Google Scholar
10. Bai, W, Wang, G, Huang, F, Sun, Y, Du, Q, Xia, J, et al.. Review of assimilating spaceborne global navigation satellite system remote sensing data for tropical cyclone forecasting. Remote Sens 2025;17:118. https://doi.org/10.3390/rs17010118.Suche in Google Scholar
11. Le Marshall, J, Xiao, Y, Norman, R, Zhang, K, Rea, A, Cucurull, L, et al.. The beneficial impact of radio occultation observations on Australian region forecasts. Aust Meteorol Oceanogr J 2010;60:121–5. https://doi.org/10.22499/2.6002.004.Suche in Google Scholar
12. Zhang, K, Fu, E, Silcock, D, Wang, Y, Kuleshov, Y. An investigation of atmospheric temperature profiles in the Australian region using collocated GPS radio occultation and radiosonde data. Atmos Meas Tech 2011;4:2087–92. https://doi.org/10.5194/amt-4-2087-2011.Suche in Google Scholar
13. Ansari, K, Corumluoglu, O, Panda, SK, Verma, P. Spatiotemporal variability of water vapor over Turkey from GNSS observations during 2009–2017 and predictability of ERA-interim and ARMA model. J Glob Position Syst 2018;16:1–23. https://doi.org/10.1186/s41445-018-0017-4.Suche in Google Scholar
14. Awange, J. GNSS environmental sensing. Berlin: Springer International Publishers; 2018.10.1007/978-3-319-58418-8Suche in Google Scholar
15. Li, Z, Muller, JP, Cross, P. Comparison of precipitable water vapor derived from radiosonde, GPS, and moderate-resolution imaging spectroradiometer measurements. J Geophys Res Atmos 2003;108. https://doi.org/10.1029/2003JD003372.Suche in Google Scholar
16. Durre, I, Vose, RS, Wuertz, DB. Overview of the integrated global radiosonde archive. J Clim 2006;19:53–68. https://doi.org/10.1175/JCLI3594.1.Suche in Google Scholar
17. Tasoglu, E, Ozturk, MZ, Yazici, O. High resolution Koppen-Geiger climate zones of Türkiye. Int J Climatol 2024;44:5248–65. https://doi.org/10.1002/joc.8635.Suche in Google Scholar
18. Chandio, AA, Ozdemir, D, Tang, X. Modelling the impacts of climate change on horticultural crop production: evidence from Türkiye. Food Energy Secur 2025;14:e70040. https://doi.org/10.1002/fes3.70040.Suche in Google Scholar
19. Sensoy, F, Demircan, M, Ulupınar, Y, Balta, I. Türkiye İklimi. Istanbul: Turkish State Meteorological Service; 2023.Suche in Google Scholar
20. Boehm, J, Werl, B, Schuh, H. Troposphere mapping functions for GPS and very long baseline interferometry from European centre for medium-range weather forecasts operational analysis data. J Geophys Res Solid Earth 2006;111. https://doi.org/10.1029/2005JB003629.Suche in Google Scholar
21. Ahmed, F, Teferle, FN, Bingley, R, Bareiss, J. GNSS meteorology in Luxembourg. Luxembourg: Cahier Scientifique-Revue Technique Luxembourgeoise; 2012.Suche in Google Scholar
22. Bevis, M, Businger, S, Chiswell, S, Herring, TA, Anthes, RA, Rocken, C, et al.. GPS meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteorol Climatol 1994;33:379–86. https://doi.org/10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2.10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2Suche in Google Scholar
23. Bosy, J, Kaplon, J, Rohm, W, Sierny, J, Hadas, T. Near real-time estimation of water vapour in the troposphere using ground GNSS and the meteorological data. Ann Geophys 2012;30:1379–91. https://doi.org/10.5194/angeo-30-1379-2012.Suche in Google Scholar
24. Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. The use of artificial satellites for geodesy. Washington D.C.: AGU; 1972, vol. 15:247–51 pp.Suche in Google Scholar
25. Fernandez, LI, Salio, P, Natali, MP, Meza, AM. Estimation of precipitable water vapour from GPS measurements in Argentina: validation and qualitative analysis of results. Adv Space Res 2010;46:879–94. https://doi.org/10.1016/j.asr.2010.05.012.Suche in Google Scholar
26. Askne, J, Nordius, H. Estimation of tropospheric delay for microwaves from surface weather data. Radio Sci 1987;22:379–86. https://doi.org/10.1029/RS022i003p00379.Suche in Google Scholar
27. Ifadis, IM. Space to earth techniques: some considerations on the zenith wet path delay parameters. Surv Rev 1993;32:130–44. https://doi.org/10.1179/sre.1993.32.249.130.Suche in Google Scholar
28. Davis, JL, Herring, TA, Shapiro, II, Rogers, AEE, Elgered, G. Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 1985;20:1593–607. https://doi.org/10.1029/RS020i006p01593.Suche in Google Scholar
29. Norazmi, MFB, Opaluwa, YD, Musa, TA, Othman, R. The concept of operational near real-time GNSS meteorology system for atmospheric water vapour monitoring over Peninsular Malaysia. Arabian J Sci Eng 2015;40:235–44. https://doi.org/10.1007/s13369-014-1481-0.Suche in Google Scholar
30. Yao, Y, Hu, Y. An empirical zenith wet delay correction model using piecewise height functions. Ann Geophys 2018;36:1507–19. https://doi.org/10.5194/angeo-36-1507-2018.Suche in Google Scholar
31. Dousa, J, Elias, M. An improved model for calculating tropospheric wet delay. Geophys Res Lett 2014;41:4389–97. https://doi.org/10.1002/2014GL060271.Suche in Google Scholar
32. Liu, C, Zheng, N, Zhang, K, Liu, J. A new method for refining the GNSS-derived precipitable water vapor map. Sensors 2019;19:698. https://doi.org/10.3390/s19030698.Suche in Google Scholar PubMed PubMed Central
33. Alshawaf, F, Balidakis, K, Dick, G, Heise, S, Wickert, J. Estimating trends in atmospheric water vapor and temperature time series over Germany. Atmos Meas Tech 2017;10:3117–32. https://doi.org/10.5194/amt-10-3117-2017.Suche in Google Scholar
34. Raudkivi, AJ. Hydrology: an advanced introduction to hydrological processes and modelling. Amsterdam: Elsevier; 2013.Suche in Google Scholar
35. Chen, B, Liu, Z. Analysis of precipitable water vapor (PWV) data derived from multiple techniques: GPS, WVR, radiosonde and NHM in Hong Kong. In: China satellite navigation conference (CSNC) 2014 proceedings: volume I. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014:159–75 pp.10.1007/978-3-642-54737-9_16Suche in Google Scholar
36. Ortiz de Galisteo, JP, Cachorro, V, Toledano, C, Torres, B, Laulainen, N, Bennouna, Y, et al.. Diurnal cycle of precipitable water vapor over Spain. Q J R Meteorol Soc 2011;137:948–58. https://doi.org/10.1002/qj.811.Suche in Google Scholar
37. Ansari, K, Althuwaynee, OF, Corumluoglu, O. Monitoring and prediction of precipitable water vapor using GPS data in Turkey. J Appl Geodesy 2016;10:233–45. https://doi.org/10.1515/jag-2016-0037.Suche in Google Scholar
38. Wang, H, Wei, M, Li, G, Zhou, S, Zeng, Q. Analysis of precipitable water vapor from GPS measurements in Chengdu region: distribution and evolution characteristics in autumn. Adv Space Res 2013;52:656–67. https://doi.org/10.1016/j.asr.2013.04.005.Suche in Google Scholar
39. Mendes, VB, Prates, G, Santos, L, Langley, RB. An evaluation of the accuracy of models for the determination of the weighted mean temperature of the atmosphere. In: Proceedings of the 2000 national technical meeting of the institute of navigation; 2000:433–8 pp.Suche in Google Scholar
40. Schueler, T, Pósfay, A, Hein, GW, Biberger, R. A global analysis of the mean atmospheric temperature for GPS water vapor estimation. In: Proceedings of the 14th international technical meeting of the satellite division of the institute of navigation (ION GPS 2001); 2001:2476–89 pp.Suche in Google Scholar
41. Sapucci, LF. Evaluation of modeling water-vapor-weighted mean tropospheric temperature for GNSS-integrated water vapor estimates in Brazil. J Appl Meteorol Climatol 2014;53:715–30. https://doi.org/10.1175/jamc-d-13-048.1.Suche in Google Scholar
42. Deniz, İ, Mekik, Ç. Preliminary study of modeling the precipitable water vapor based on radiosonde data. In: FIG congress 2014 engaging the challenges – enhancing the relevance Kuala Lumpur, Malaysia 16 – 21 June 2014; 2014.Suche in Google Scholar
43. Campmany, E, Bech, J, Rodríguez-Marcos, J, Sola, Y, Lorente, J. A comparison of total precipitable water measurements from radiosonde and sunphotometers. Atmos Res 2010;97:385–92. https://doi.org/10.1016/j.atmosres.2010.04.016.Suche in Google Scholar
44. Yeh, TK, Hong, JS, Wang, CS, Chen, CH, Chen, KH, Fong, CT. Determining the precipitable water vapor with ground-based GPS and comparing its yearly variation to rainfall over Taiwan. Adv Space Res 2016;57:2496–507. https://doi.org/10.1016/j.asr.2016.04.002.Suche in Google Scholar
45. Ansari, K. Extended Kalman Filter analysis of sea-level changes along the Black Sea coast and comparison with satellite altimetry. Disc Geosci 2025;3:1–12. https://doi.org/10.1007/s44288-025-00316-1.10.1007/s44288-025-00316-1Suche in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
