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Investigation and Analysis of Spatiotemporal Instability of the Earth’s Atmosphere Based on Real-Time GNSS Data Processing

  • ATMOSPHERIC OPTICS AND ASTRONOMICAL CLIMATE
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Abstract—

One of the options for the practical application of GNSS technology, in addition to geodetic and navigation needs, is remote sensing of the atmosphere by radio signals from navigation satellites in order to improve the quality and detail of weather forecasts. The propagation of a radio signal from GNSS satellites to a ground receiving device (GNSS receiver) through a neutral atmosphere is accompanied by a decrease in the phase velocity of the radio waves (additional atmospheric delays). This is due to the presence of nitrogen, oxygen, carbon dioxide, and water vapor molecules in the atmosphere. Therefore, measurements of the additional delay of the radio signal in the atmosphere (tropospheric delay) provide information on the integral properties of the atmosphere along the propagation path of the radio signal. As a result of the primary processing of the GNSS measurement results, the distances from the observation station to GNSS satellites are determined. Secondary processing of GNSS measurements consists in solving a navigation problem and provides information on the location of the station. In order to obtain meteorological information, it is necessary to develop special methods of secondary data processing based on solving inverse problems. The combination of primary and secondary data (processing) along with meteorological information makes it possible to obtain a global model of the atmosphere in near-real time. The efficiency of this approach, the complete automation, and the absence of consumables during remote sensing provide opportunities for the widespread implementation of operational monitoring of the state of the atmosphere in order to improve the data’s detail and accuracy of regional short-term weather forecasts. Currently, due to cross-border cooperation with European countries in conducting joint GNSS observations in the UA-EUPOS/ZAKPOS network of stations, we are able to have an accurate, dense, and continuous sampling of tropospheric delay values, which allows us to determine and predict the dynamics of atmospheric changes in real time. The main goal of the work is to study the spatio-temporal instability of the atmosphere over an area covered by active reference stations. The results of the study can be used to improve the quality of weather prediction.

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REFERENCES

  1. N. I. Kablak, “Procedure for determining tropospheric delays in the ZAKPOS/UA-EUPOS network of active reference stations,” Kinematics Phys. Celestial Bodies 29, 202–206 (2013).

    Article  ADS  Google Scholar 

  2. N. Kablak, M. Kalyuzhnii, A. Shul’ga, and V. Vovk, “Practical realization of detection of space-time instability of the atmosphere in the network of active reference stations UA-EUPOS/ZAKPOS/,” Kosm. Nauka Tekhnol. 23 (1), 54–62 (2017).

    Article  Google Scholar 

  3. M. P. Kalyuzhnii and O. P. Aleksandrov, “Approximation of functions of multiple variables given at scattered dots by Shepard’s iterative method (ISMNAO),” Certificate of Copyright Registration of Work No. 57 976 of January 5, 2015.

  4. S. Savchuk, N. Kablak, and A. Khoptar, “Comparison of approaches to the determination of the zenith troposphere delay based on atmospheric radio data and GNSS observation,” Geodeziya, Kartografiya Ta Aerofotoziomka: Mizhvidomchii Nauk.-Tekh. Oglyad, No. 8, 24–32 (2018). https://doi.org/10.23939/istcgcap2018.02.024

    Article  Google Scholar 

  5. Y. E. Bar-Sever, P. Kroger, and J. A. Borjesson, “Estimating horizontal gradients of tropospheric path delay with a single GPS receiver,” J. Geophys. Res.: Solid Earth 103, 5019–5035 (1998).

    Article  Google Scholar 

  6. H. C. Baker, A. H. Dodson, N. T. Penna, M. Higgins, and D. Offiler, “Ground-based GPS water vapour estimation: Potential for meteorological forecasting,” J. Atmos. Sol.-Terr. Phys. 63, 1305–1314 (2001).

    Article  ADS  Google Scholar 

  7. P. Benevides, J. Catalão, P. Miranda, and M. J. Chinita, “Analysis of the relation between GPS tropospheric delay and intense precipitation,” in Proc. Remote Sensing of Clouds and the Atmosphere XVIII; and Optics in Atmospheric Propagation and Adaptive Systems XVI, Dresden, Germany, Sept. 23–26,2013 (SPIE, Bellingham, WA, 2013), in Ser.: SPIE Remote Sensing, Vol. 8890, id. 88900Y.

  8. M. Bevis, S. Businger, T. A. Herring, et al., “GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system,” J. Geophys. Res.: Atmos. 97, 15787–15801 (1992).

    Article  ADS  Google Scholar 

  9. M. Bevis, S. Businger, S. Chiswell, T. A. Herring, R. A. Anthes, C. Rocken, and R. H. Ware, “GPS meteorology: Mapping zenith wet delays onto precipitable water,” J. Appl. Meteorol. 3, 379–386 (1994).

    Article  Google Scholar 

  10. O. Bock, M. N. Bouin, A. Walpersdorf, J. P. Lafore, S. Janicot, F. Guichard, and A. Agusti-Panareda, “Comparison of ground-based GPS precipitable water vapour to independent observations and NWP model reanalyses over Africa,” Q. J. R. Meteorol. Soc. 133, 2011–2027 (2007).

    Article  ADS  Google Scholar 

  11. J. Bosy, W. Rohm, A. Borkowski, et al., “Integration and verification of meteorological observations and NWP model data for the local GNSS tomography,” Atmos. Res. 96, 522–530 (2010).

    Article  Google Scholar 

  12. J. L. Davis, G. Elgered, A. E. Niell, et al., “Ground-based measurements of gradients in the wet radio refractive index of air,” Radio Sci. 28, 1003–1018 (1993).

    Article  ADS  Google Scholar 

  13. J. Dousa and P. Vaclavovic, “Real-time zenith tropospheric delays in support of numerical weather prediction applications,” Adv. Space Res. 53, 1347–1358 (2014).

    Article  ADS  Google Scholar 

  14. C. S. Gardner, “Effects of horizontal refractivity gradients on the accuracy of laser ranging to satellites,” Radio Sci. 11, 1037–1044 (1976).

    Article  ADS  Google Scholar 

  15. C. S. Gardner, “Correction of laser tracking data for the effects of horizontal refractivity gradients,” Appl. Optics. 16, 2427–2432 (1977).

    Article  ADS  Google Scholar 

  16. J. Hauser, “Effects of deviations from hydrostatic equilibrium on atmospheric corrections to satellite and lunar laser range measurements,” J. Geophys. Res., Solid Earth Planets 94, 10182–10186 (1989).

    Article  Google Scholar 

  17. T. A. Herring, “Modeling atmospheric delays in the analysis of space geodetic data,” in Proc. Symp. on Refraction of Transatmospheric Signals in Geodesy, The Hague, The Netherlands, May 19–22,1992, Ed. by J. C. de Munck and T. A. Th. Spoelstra (Nederlandse Commissie voor Geodesie, Delft, 1992). No. 36, 157–164.

  18. Hungary’s GNSS Network — GNSSnet.hu. https://www.gnssnet.hu.

  19. R. Ichikawa, H. Ohkubo, Y. Koyama, et al., “An evaluation of atmospheric gradient using water vapor radiometers in Kashima, Japan,” J. Commun. Res. Lab. 48, 97–103 (2001).

    Google Scholar 

  20. N. Kablak, O. Reity, O. Ştefan, A. T. G. M. Rădulescu, and C. Rădulescu, “The remote monitoring of Earth’s atmosphere based on operative processing GNSS data in the UA-EUPOS/ZAKPOS network of active reference stations,” Sustainability 8, 391 (2016). https://doi.org/10.3390/su8040391

    Article  Google Scholar 

  21. M. Kacmarík, J. Douša, F. Zus, P. Václavovic, K. Balidakis, G. Dick, and J. Wickert, “Sensitivity of GNSS tropospheric gradients to processing options,” Ann. Geophys. 37, 429–446 (2019). https://doi.org/10.5194/angeo-37-429-2019

    Article  ADS  Google Scholar 

  22. G. Möller and D. Landskron, “Atmospheric bending effects in GNSS tomography,” Atmos. Meas. Tech. 12, 23–34 (2019). https://doi.org/10.5194/amt-12-23-2019

    Article  Google Scholar 

  23. Poland’s GNSS Network — ASG-EUPOS. http://www.asgeupos.pl.

  24. Romania’s GNSS Network — ROMPOS. https://www.rompos.ro.

  25. SES Project. http://www.meteognss.net/.

  26. Slovakia’s GNSS Network — SKPOS. http://www.skpos.gku.sk.

  27. Western Ukraine GNSS Network — UA-EUPOS, ZAKPOS. http://www.zakpos.zakgeo.com.ua.

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Correspondence to N. Kablak, S. Savchuk or M. Kaliuzhnyi.

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Translated by E. Seifina

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Kablak, N., Savchuk, S. & Kaliuzhnyi, M. Investigation and Analysis of Spatiotemporal Instability of the Earth’s Atmosphere Based on Real-Time GNSS Data Processing. Kinemat. Phys. Celest. Bodies 36, 195–204 (2020). https://doi.org/10.3103/S0884591320040042

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