INVESTIGATION OF THE EFFECT OF ANNUAL AVERAGE TEMPERATURE AND PRECIPITATION CHANGES ON CORS-TR STATIONS: THE CASE OF KSTM STATION

Year 2024, Volume: 12 Issue: 3, 725 - 736, 01.09.2024

Alparslan Acar , Sercan Bülbül , Fuat Başçiftçi , Ömer Yıldırım

https://doi.org/10.36306/konjes.1517144

Abstract

In this study, the effects of meteorological changes on the point positioning of CORS-TR stations were investigated. For this purpose, KURU, SINP, BOYT, CORU, CANK, CMLD, KRBK, KSTM stations were selected. The KSTM station was taken as unknown and adjusted based on other stations. Seasonal normal values of KSTM station in Kastamonu province covering the years 2016-2020 were examined in terms of temperature and precipitation amount. These values were determined according to the minimum, maximum and average value criteria by using Türkiye State Meteorological Service data. For the calculations, IGS-standardized RINEX data of the stations for 5 years and 12 months between 2016 and 2020 and for 10 days on the 11th and 20th days of each month were used. All calculations were processed with Leica Geo Office v8.x. The calculated coordinates were compared with the current coordinates of CORS-TR at the same epoch and examined according to annual temperature and precipitation. In the analyzes, it was tested by statistical method whether all measurements were compatible. When it was examined whether the temperature changes were statistically significant, it was observed that the test values were calculated according to the temperature changes were below the test distribution limit at 95% confidence interval. When it was examined whether the precipitation changes were statistically significant, it was observed that the test values were calculated according to the precipitation changes were below the test distribution limit at 95% confidence interval.

Keywords

CORS-TR, GNSS, Leica Geo Office v8.x, Precipitation, Temperature

Ethical Statement

The authors declare that the study complies with all applicable laws and regulations and meets ethical standards.

References

• F. Pektaş, “Gerçek zamanlı ulusal ve yerel Sabit GNSS ağlarına dayalı kinematik konumlama (TUSAGA-Aktif – İSKİ-UKBS ağlarının yerel ölçekte karşılaştırılması,” M. S. thesis, Yildiz Technical University, Istanbul, 2010.
• S. Bülbül, “TUSAGA-Aktif noktalarında renkli gürültülerden arındırılmış hız bileşenlerinin belirlenmesi,” Ph. D. thesis, Konya Technical University, Konya, 2018.
• B. Bilgen, S. Bülbül, and C. İnal C. “TUSAGA-Aktif istasyonlarındaki meteorolojik hava olaylarının hassas nokta konumlamaya etkisi,” Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, vol. 21, no. 6, pp. 1393-1403, 2021.
• M. S. Bos, L. Bastos, and R.M.S. Fernandes, “The ınfluence of seasonal signals on the estimation of the tectonic motion in short continuous GPS time-series,” Journal of Geodynamics, vol. 49, pp. 205-209, 2010.
• J.F. Zumberge, M.B. Heflin, D. C. Jefferson, M.M. Watkins, and F.H. Webb, “Precise point processing for the efficient and robust analysis of GPS data from large networks,” Journal of Geophysical Research, vol. 102, pp. 5005-5017, 1997.
• H. Nakamura, K. Koizumi, and N. Mannoji, “Data Assimilation of GPS precipitable water vapor into the jma mesoscale numerical weather prediction model and ıts ımpact on rainfall forecasts,” Journal of the Meteorological Society of Japan, vol. 82, no. 1, pp. 441–452, 2004.
• G. Möller, and D. Landskron, “Atmospheric bending effects in GNSS tomography,” Atmospheric Measurement Techniques, vol. 12, no. 1, pp, 23–34, 2019.
• A. Garcia Vieira de Sá, “Tomographic determination of the spatial distribution of water vapour using GNSS observations for real-time applications,” Ph. D. thesis, Wrocław University of Environmental and Life Sciences, 2018.
• H. Brenot, W. Rohm, M. Kačmařík, G. Möller, A. Sá, D. Tondaś, L. Rapant, R. Biondi, T. Manning, and C. Champollion, “Cross-comparison ve methodological ımprovement in GPS tomography,” Remote Sensing, vol. 12, no. 1, pp. 30, 2020.
• Y. H. Kuo, Y.R. Guo, and E.R. Westwater, “Assimilation of precipitable water measurements into a mesoscale numerical model,” Monthly Weather Review, vol. 121, no. 4, pp. 1215-1238, 1993.
• T. L. Smith, S. G. Benjamin, S. I. Gutman, and S. Sahm, “Short-range forecast ımpact from assimilation of GPS-IPW observations into the rapid update cycle,” Monthly Weather Review, vol. 135, no.8, pp. 2914-2930, 2007.
• M.S. F. V. De Pondeca, and X. Zou, “A case study of the variational assimilation of GPS zenit delay observations into a mesoscale model,” Journal of Applied Meteorology and Climatology, vol. 40, no. 9, pp. 1559-1576, 2001.
• S. Q. Peng, and X. Zou, “Impact on short-range precipitation forecasts from assimilation of ground-based GPS zenit total delay and rain gauge precipitation observations,” Journal of the Meteorological Society of Japan, vol. 82, no. 1B, pp. 491-506, 2004.
• H.C. Baker, A. H. Dodson, N.T. Penna, M. Higgings, and D. Offiler, “Ground-based GPS water vapour estimation: potential for meteorological forecasting,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 63, no. 12, pp. 1305-1314, 2001.
• D. Jerrett, and J. Nash, “Potential uses of surface based GPS water vapour measurements for meteorological purposes,” Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, vol. 6, no. 6-8, pp. 457- 461, 2001.
• G. Guerova, “Application of GPS derived water vapour for numerical weather prediction in switzerlve,” Ph. D. thesis, University of Bern, 2003.
• G. Gendt, G. Dick, C. Reigber, M. Tomassini, Y. Liu, and M. Ramatschi, “Near real time GPS water vapor monitoring for numerical weather prediction in Germany,” Journal of the Meteorological Society of Japan, vol. 82, no. 1B, pp. 361-370, 2004.
• H. Vedel, and X. Y. Huang, “Impact of ground based GPS data on numerical weather prediction,” Journal of the Meteorological Society of Japan, vol. 82, no. 1B, pp. 459–472, 2004.
• R. Eresmaa, H. Järvinen, and K. Salonen, “Potential of ground-based GPS slant delays for numerical weather prediction,” Atmos. Chem. Phys., vol. 7, pp. 3143–3151, 2006.
• P. Poli, P. Moll, F. Rabier, G. Desroziers, B. Chapnik, L. Berre, S. B. Healy, E. Andersson, and F. Z. ElGuelai, “Forecast impact studies of zenit total delay data from european near real-time GPS stations in météo France 4DVAR,” Journal of Geophysical Research: Atmospheres, vol. 112, pp. D06114, 2007.
• C. Faccani, R. Ferretti, R. Pacione, T. Paolucci, F. Vespe, and L. Cucurull, “Impact of a high density GPS network on the operational forecast,” Advances in Geosciences, vol. 2, pp. 73-79, 2005.
• M. Zhang, Y. Ni, and F. Zhang, “Variational assimilation of GPS precipitable water vapor ve hourly rainfall observations for a Meso-β scale heavy precipitation event during the 2002 Mei-Yu Season,” Advances in Atmospheric Sciences, vol. 24, no. 3, pp. 509-526, 2007.
• National Oceanic and Atmospheric Administration, “Space Weather Predıctıon Center,” [Online]. Available: https://www.swpc.noaa.gov/phenomena [Accessed: Apr. 20, 2024].
• F. Başçiftçi, C. Inal, Ö. Yildirim, and S. Bulbul, “Comparison of regional and global TEC values: Turkey model,” International Journal of Engineering and Geosciences, vol. 3, no. 2, pp. 61-72, 2018.
• ESA Space Weather Service Network, [Online]. Available: https://swe.ssa.esa.int/what-is-space-weather [Accessed: May. 15, 2024].
• R. Mukesh, V. Karthikeyan, P. Soma, and P. Sindhu, “Cokriging based statistical approximation model for forecasting ionospheric VTEC during high solar activity and storm days,” Astrophysics and Space Science, vol. 364, pp. 131, 2019.
• Space Weather Predıctıon Center (2024). F10.7 cm radio emissions, [Online]. Available: https://www.swpc.noaa.gov/phenomena/f107-cm-radio-emissions [Accessed: May. 16, 2024].
• Australian Space Weather Forecasting Centre [Online]. Available: https://www.sws.bom.gov.au/ [Accessed: May. 16, 2024].
• International Service of Geomagnetic Indices, “Kp index,” [Online]. Available: https://isgi.unistra.fr/indices_kp.php [Accessed: May. 16, 2024].
• F. Basciftci, and S. Bulbul, “Investigation of ionospheric TEC changes potentially related to Seferihisar-Izmir earthquake (30 October 2020, MW 6.6),” Bulletin of Geophysics & Oceanography, vol. 63, no. 3, pp. 4382–4400, 2022.
• B. Lemmerer, S. Unger, “Modeling and pricing of space weather derivatives,” Risk Management, vol. 21, pp. 265–291, 2019.
• N. Myagkova, V. R. Shirokii, R. D. Vladimirov, O. G. Barinov, and S. A. Dolenko, “Prediction of the Dst geomagnetic index using adaptive methods,” Russian Meteorology and Hydrology, vol. 46, pp. 157–162, 2021.
• International Service of Geomagnetic Indices “Dst index”, [Online]. Available: https://isgi.unistra.fr/indices_dst.php [Accessed: May. 16, 2024].
• Banerjee, A. Bej, and T. N. Chatterjee, “On the existence of a long range correlation in the Geomagnetic Disturbance storm time (Dst) index,” Astrophysics and Space Science, vol. 337, pp. 23–32, 2012.
• S. Bulbul, and F. Basciftci, “TEC anomalies observed before and after Sivrice-Elaziğ earthquake (24 January 2020, Mw: 6.8),” Arabian Journal of Geosciences, vol. 14, no. 12, pp. 1077, 2021.
• R. Dach, S. Lutz, P. Walser, and P. Fridez, Bernese GNSS Software Version 5.2. User manual, Astronomical Institute, University of Bern, Bern Open Publishing, 2015.