Monitoring of the tropospheric water vapor in the western cross-border zone of Ukraine

2016;
: pp. 21-33
1
Lviv Polytechnic National University
2
Department «Higher geodesy and astronomy» of Lviv Polytechnic National University

Aim. Identifying of reliable estimates of zenith tropospheric delay (ZTD) by the data of GNSS observations (remote monitoring of the troposphere) on the active reference stations of the west cross-border zone of Ukraine. Methods. The zenith tropospheric delays, and their direct link with integrated / precipitated water vapor are important products that are obtained in GNSS-meteorology. They allow to get the rapid information for numerical weather prediction. The reliability of the estimates of the integrated / precipitated water vapor from the GNSS data analysis is one of the main problems in the use of these results. Accordingly, strategy of analysis GNSS data should provide such ZTD estimations, which meet requirements of GNSS-meteorology. The determination of ZTD values was grounded traditionally on data analysis in the mode of packet network solution using least square method and observation technique which based on double differencing (DD) in the NRT mode. The absolute method of PPP which required precise corrections for satellite clocks together with predicted orbits, wasn’t applied practically. From point of view of analysis strategy of GNSS data the PPP method is popular thanks to creating by the International GNSS Service (IGS) and others institutions of a such products as accurate satellite orbits and clock corrections in RT mode. For comparing the ZTD data,  obtained by the packages NRT-DD Bernese GNSS software and RT-PPP ALBERDING GNSS STATUS Software were selected for the February-March of 2016. The maximum amount of data at each observation station (2880 values) was by the selection criterion in this period. 17 GNSS stations were selected for comparison. The graphs of ZTD change over this period have been constructed for each station, as well as the hour changes of ZTD differences obtained  by two packages.  Results. Comparison results established the following: the use of different processing strategies GNSS data not significantly affect on the accuracy of zenith tropospheric delay. The obtained estimates of 1-2 cm fully satisfy the requirements for the indicated product in meteorology and climatology. Scientific novelty. The realized studies of two fundamentally different processing strategies GNSS data revealed the real accuracy of the zenith tropospheric delay, which allows to consider the results more reliable in comparison with results obtained by other researchers. Practical significance. Estimated values of ZTD obtained from a regional network of permanent GNSS stations of the western cross-border zone of Ukraine can become a valuable information in problems of numerical weather prediction.

1. Baker, H. C., Dodson, A. H., Penna, N. T., Higgins, M., Offiler, D. Ground-based GPS water vapour esti-mation: potential for meteorological forecasting, J. Atmos. Sol.-Terr. Phy, 2001, 63, 1305–1314.
https://doi.org/10.1016/S1364-6826(00)00249-2
2. Benevides, P., Catalao, J., Miranda, P., Chinita, M. J. (Analysis of the relation between GPS tropospheric delay and intense precipitation, SPIE Remote Sensing, International Society for Optics and Photonics, 88900Y–88900Y, 2013.
3. Bock, O., Bouin, M. N., Walpersdorf, A., Lafore, J. P., Janicot, S., Guichard, F., Agusti-Panareda, A. Comparison of groundbased GPS precipitable water vapour to independent observations and NWP model reanalyses over Africa, Q. J. Roy. Meteor. Soc., 2007, 133, 2011–2027.
https://doi.org/10.1002/qj.185
4. Bevis, B. G., S. Bussinger, T. A. Herring, C. Rocken, R. A. Anthes, and R. H. Ware, GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System, J. Geophys. Res., 1992, 97, 15787–15801.
https://doi.org/10.1029/92JD01517
5. Bevis, M., Businger, S., Chiswell, S., Herring, T. A., Anthes, R. A., Rocken, C., and Ware, R. H. GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water. Journal of Applied Meteorology, 1994), 3(3):379–386.
https://doi.org/10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2
6. J. Böhm, R. Heinkelmann, and H. Schuh. Short Note: A global model of pressure and temperature for geodetic applications.J. Geodesy, 2007, 81(10):679–683.
https://doi.org/10.1007/s00190-007-0135-3
7. J. Böhm, J. Kouba, and H. Schuh. Forecast Vienna mapping functions 1 for real-time analysis of space geodetic observations. J. Geodesy, 2009, 83(5).
https://doi.org/10.1007/s00190-008-0216-y
8. J. Böhm, A. Niell, P. Tregoning, and H. Schuh. Global mapping function (GMF): a new empirical mapping function based on numerical weather model data. Geophys. Res. Lett., 2006, 33:L07304.
https://doi.org/10.1029/2005GL025546
9. Businger, S., S. R. Chiswell, M. Bevis, J. Duan, R. A. Anthes, C. Rocken, R. H. Ware, M. Exner, T. Van Hove, and F. S. Solheim. The Promise of GPS in Atmospheric Monitoring, Bull. Amer. Meteor. Soc., 1996, 77, 5–18.
https://doi.org/10.1175/1520-0477(1996)077<0005:TPOGIA>2.0.CO;2
10. Dousa J, Vaclavovic P. Real-time zenith tropospheric delays in support of numerical weather prediction applications. Advances in Space Research. 2014, Vol. 53, No 9, pp 1347–1358.
https://doi.org/10.1016/j.asr.2014.02.021
11. Herring, T., King, R. W., Floyd, M. A., McClusky, S. C. GAMIT Reference Manual – GPS Analysis at MIT – Release 10.6, Dep. of Earth, Atm. and Pla¬netary Sciences, Massachusetts Institute of Technology, USA, 2015.
12. Hofmann-Wellenhof, B., Lichtenegger, H., and Wasle, E. GNSS: Global Navigation Satellite Systems: GPS, GlONASS,Galileo, and More, ISBN-10: 3211730125, Springer, Wien, 2008.
13. Kuo, Y., Y. Guo, and E.R. Westwater. Assimilation of Precipitable Water Vapor Measurments into a Mesoscale Numerical Model, Mon. Wea. Rev., 1993, 121, 1215–1238.
https://doi.org/10.1175/1520-0493(1993)121<1215:AOPWMI>2.0.CO;2
14. A. Niell. Global mapping functions for the atmosphere delay at radio wavelengths. J. Geophys. Res., 1996, 101(B2):3227–3246.
https://doi.org/10.1029/95JB03048
15. Rocken, C., R. H. Ware, T. Van Hove, F. Solheim, C. Alber, J. Johnson, and M. G. Bevis. Sensing Atmo-spheric Water Vapor with the Global Positioning System, Geophys. Res. Lett., 1993, 20, 2631–2634.
https://doi.org/10.1029/93GL02935
16. Saastamoinen, J. Atmospheric correction for the tropo-sphere and stratosphere in radio ranging satellites, The use of artificial satellites for geodesy, Geophys.Monogr. Ser., 1972, 15, 247–251.
17. Seco, A., Ramírez, F., Serna, E., Prieto, E., García, R., Mo¬reno, A., Cantera, J. C., Miqueleiz, L., Priego, J. E. Rain pattern analysis and forecast model based on GPS estimated atmospheric water vapor content, Atmos. Environ., 2012, 49, 85–93.
https://doi.org/10.1016/j.atmosenv.2011.12.019
18. Seeber, G. N. Satellite geodesy: foundations, methods, and applications, ISBN-10: 3110175495, Walter de Gruyter, 2003
https://doi.org/10.1515/9783110200089
19. Steigenberger, P., Boehm, J., and Tesmer, V. Com¬parison of GMF/GPT with VMF1/ECMWF and implications for atmospheric loading. Journal of Geodesy, 2009, 83(10):943–951.
https://doi.org/10.1007/s00190-009-0311-8
20. Tregoning, P., Boers, R., O'Brien, D., Hendy, M. Ac-curacy of absolute precipitable water vapor estimates from GPS observations, J. Geophys. Res., 1998, 103, 28701–28710, doi:10.1029/98JD02516.
https://doi.org/10.1029/98JD02516
21. Vedel H., Huang X.-Y. Impact of ground based GPS data on numerical weather prediction. J. Met. Soc. Japan, 2004, 82(1B):459–472.
https://doi.org/10.2151/jmsj.2004.459
22. Vedel, H., Huang, X. Y., Haase, J., Ge, M., Calais, E. Impact of GPS zenith tropospheric delay data on precipitation forecasts in Mediterranean France and Spain, Geophys. Res. Lett., 2004, 31.
https://doi.org/10.1029/2003GL017715
23. Vedel, H., Mogensen, K. S., and Huang, X. Calculation of zenith delays from meteorological data compa-rison of NWP model, radiosonde and GPS delays. Physics and Chemistry of the Earth A, 2001, 26(6–8):497–502.
https://doi.org/10.1016/S1464-1895(01)00091-6
24. Yan, X., Ducrocq, V., Poli, P., Hakam, M., Jaubert, G., Walpersdorf, A. Impact of GPS zenith delay assimilation on convectivescale prediction of Me-diter¬ranean heavy rainfall, J. Geophys. Res., 2009, 114, D03104, doi:10.1029/2008JD011036.
https://doi.org/10.1029/2008JD011036
25. Yuan, L. L., R. A. Anthes, R. H. Ware, C. Rocken, W. D. Bonner, M. G. Bevis, and S. Bissinger. Sensing Climate Change Using Global Positioning System, J. Geophys. Res., 1993, 98, 14925-14937.
https://doi.org/10.1029/93JD00948
26. Zumberge, J. F., Heflin, M. B., Jefferson, D. C., Watkins, M. M., Webb, F. H. Precise point positioning for the efficient and robust analysis of GPS data from large networks, J Geoph. Res., 1997, 102, 5005-5018.
27. Alberding GNSS Status Software: http://194.42.206.27/cgi-bin/beacon.cgi?mod=show_map&lang.
28. Global Forecast System (GFS): https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-f....
29. SES Project: http://www.meteognss.net/
30. Zablotskyj F. D. GNSS-meteorologia. Navchalnyy posib¬nyk [Meteorology. Textbook]. Lviv: Lviv Polytechnic Publishing House, 2013, 96 p.