DOI QR코드

DOI QR Code

GNSS 가강수량 추정시 건조 지연 모델에 의한 복원 정밀도 해석

Retrieval Biases Analysis on Estimation of GNSS Precipitable Water Vapor by Tropospheric Zenith Hydrostatic Models

  • Nam, JinYong (MetaGIS Consulting Co., Ltd.) ;
  • Song, DongSeob (Dept. of Earth and Environmental Engineering, Kangwon National University)
  • 투고 : 2019.07.28
  • 심사 : 2019.08.21
  • 발행 : 2019.08.31

초록

GNSS를 이용한 가강수량 복원에 있어서 가중 평균 기온과 더불어 천정 건조 지연 모델은 가강수량의 정확도에 중요한 매개변수 중 하나이다. 천정 습윤 지연은 천정 건조 지연 모델의 오차가 축적되는 경향을 가지고 있으므로, 천정 건조 지연의 편의량은 GNSS 가강수량의 정확도에 영향을 미치게 된다. 본 연구에서는 Saastamoinen, Hopfield 및 Black의 세 가지 천정 건조 지연 모델을 이용하여 GNSS 가강수량을 산출하고 라디오존데 가강수량과의 정확도를 비교하였다. 그리고 이 과정에서 가강수량 산출에 필요한 가중 평균 기온을 한국형 가중 평균 기온 모델과 라디오존데로부터 실제로 관측한 가중 평균 기온을 각각 적용하여 다르게 평가하였다. 이를 위해 국내 상시관측소 5개소의 1년 분량의 GNSS 관측데이터를 취득한 후 천정 건조 지연 모델별로 가강수량을 산출하고 정밀도를 분석하였다. 분석 결과, 한국형 가중 평균 기온 모델에 기반하여 복원한 GNSS 가강수량이 라디오존데의 가중 평균 기온을 적용한 것보다 편의량이 작은 것으로 확인되었다. 또한, GNSS 기상에서 널리 적용하고 있는 Saastamoinen 모델은 우리나라 관측소의 위도나 고도에 의한 편의량이 발생하여 가장 유효한 모델이 아닐 가능성이 있음을 확인하였다.

ZHD (Zenith Hydrostatic Delay) model is important parameter in estimating of GNSS (Global Navigation Satellite System) PWV (Precipitable Water Vapor) along with weighted mean temperature. The ZWD (Zenith Wet Delay) is tend to accumulate the ZHD error, so that biases from ZHD will be affected on the precision of GNSS PWV. In this paper, we compared the accuracy of GNSS PWV with radiosonde PWV using three ZHD models, such as Saastamoinen, Hopfield, and Black. Also, we adopted the KWMT (Korean Weighted Mean Temperature) model and the mean temperature which was observed by radiosonde on the retrieval processing of GNSS PWV. To this end, GNSS observation data during one year were processed to produce PWVs from a total of 5 GNSS permanent stations in Korea, and the GNSS PWVs were compared with radiosonde PWVs for the evaluating of biases. The PWV biases using mean temperature estimated by the KWMT model are smaller than radiosonde mean temperature. Also, we could confirm the result that the Saastamoinen ZHD which is most used in the GNSS meteorology is not valid in South Korea, because it cannot be exclude the possibility of biases by latitude or height of GNSS station.

키워드

참고문헌

  1. Bevis, M., Businger, S., Chiswell, S., Herring, T.A., Anthes, R.A., Rocken, C., and Ware, R.H. (1994), GPS meteorology: mapping zenith wet delays onto precipitable water, Journal of Applied Meteorology, Vol. 33, No. 3, pp. 379-386. https://doi.org/10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2
  2. Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R., and Ware, R.H. (1992), GPS meteorology-remote sensing of atmospheric water vapor using the Global Positioning System, Journal of Geophysical Research, Vol. 97, No. D14, pp. 15787-15801. https://doi.org/10.1029/92JD01517
  3. Black, H.D. (1978), An easily implemented algorithm for the tropospheric range correction, Journal of Geophysical Research, Vol. 83, No. B4, pp. 1825-1828. https://doi.org/10.1029/JB083iB04p01825
  4. Davis, J.L., Herring, T.A., Shapiro, I.I., Rogers, A.E.E., and Elgered, G. (1985), Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length, Radio Science, Vol. 20, No. 6, pp. 1593-1607. https://doi.org/10.1029/RS020i006p01593
  5. Emardson, T.R. and Derks, H.J. (2000), On the relation between the wet delay and the integrated precipitable water vapour in the European atmosphere, Meteorological Applications, Vol. 7, No. 1, pp. 61-68. https://doi.org/10.1017/S1350482700001377
  6. Guiraud, F.O., Howard, J., and Hogg, D.C. (1979), A dualchannel microwave radiometer for measurement of precipitable water vapor and liquid, IEEE Transactions on Geoscience Electronics, Vol. 17, No. 4, pp. 129-136. https://doi.org/10.1109/TGE.1979.294639
  7. Ha, J.H., Park, K.D., Park, P.H., and Park, J.U. (2005), Analysis of error sources in determination of tropospheric slant wet delay using GPS, Asia-Pacific Journal of Atmospheric Sciences, Vol. 41, No. 3, pp. 337-346. (in Korean with English abstract)
  8. Hofmann-Wellenhof, B., Lichtenegger, H., and Collines, J. (2001), GPS Theory and Practice, Springer-Verlag Wien New York, Wien, Austria.
  9. Hopfield, H.S. (1971), Tropospheric effect on electromagnetically measured range: prediction from surface weather data, Radio Science, Vol. 6, No. 3, pp. 357-367. https://doi.org/10.1029/RS006i003p00357
  10. Park, J.G., Ahn, J.S., Jeon, H.S., Kim, D.M, Seong, S.K., and Lee, Y.J. (2012), Performance analysis of tropospheric delay error models using GPS measurement, The Korean Society for Aeronautical and Space Sciences Fall meeting, 14-16 November, Jeju, Korea, pp. 654-660. (in Korean with English abstract)
  11. Kim, D.S., Won, J.H., Kim, H.I., Kim, K.H., and Park, K.D. (2010), Accuracy analysis of GPS-derived precipitable water vapor according to interpolation methods of meteorological data, Spatial Information Research, Vol. 18, No. 4, pp. 33-41. (in Korean with English abstract)
  12. Kim, K.H., Kim, Y.H., and Chang, D.E. (2009), The analysis of changma structure using radiosonde observational data from KEOP-2007: Part I. the assessment of the radiosonde data, Atmosphere, Vol. 19, No. 2, pp. 213-226. (in Korean with English abstract)
  13. Labraga, J.C., Frumento, O., and Lopez, M. (2000), The atmospheric water vapor cycle in South America and the tropospheric circulation, Journal of Climate, Vol. 13, No. 11, pp. 1899-1915. https://doi.org/10.1175/1520-0442(2000)013<1899:TAWVCI>2.0.CO;2
  14. Lee, H.G. (2018), Impact of tropospheric modeling schemes into accuracy of estimated ellipsoidal heights by GPS baseline processing: experimental analysis and results, Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 4, pp. 245-254. (in Korean with English abstract)
  15. Lee, Y.C. (2002), A comparison of correction models for the prediction of tropospheric propagation delay of GPS signal, Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 20, No. 3, pp. 283-291. (in Korean with English abstract)
  16. Niell, A.E., Coster, A.J., Solheim, F.S., Mendes, V.B., Toor, P.C., Langley, R.B., and Upham, C.A. (2001), Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI, Journal of Atmospheric and Oceanic Technology, Vol. 18, No. 6, pp. 830-850. https://doi.org/10.1175/1520-0426(2001)018<0830:COMOAW>2.0.CO;2
  17. Ruihua, L. and Jie, Y. (2009), Principle, application and development of the ground-based GPS meteorology, Proceedings of 14th Youth Conference on Communication, Scientific Research, 24-26 July, Dalian, China, Vol. 1, pp. 784-788.
  18. Saastamoinen, J. (1973), Contribution to theory of atmospheric refraction, Bulletin Geodesique, Vol. 107, pp. 13-34. https://doi.org/10.1007/BF02522083
  19. Schuler, T. (2001), On Ground-based GPS Tropospheric Delay Estimation, Ph.D. dissertation, der Bundeswehr Munchen. (Universty FAF Munich), Neubiberg, Germany, 364p.
  20. Song, D.S. (2009), Improvement of GPS PWV retrieval capability using the reverse sea level corrections of airpressure, Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 27, No. 5, pp. 535-544. (in Korean with English abstract)
  21. Song, D.S. (2012), Comparison analysis of empirical tropospheric wet delay models, Proceedings of Annual Conference of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 26-27 April, Gangneung, Korea, pp. 177-182. (in Korean with English abstract)
  22. Song, D.S. and Grejner-Brzezinska, D.A. (2009), Remote sensing of atmospheric water vapor variation from GPS measurements during a severe weather event, Earth Planets and Space, Vol. 61, No. 10, pp. 1117-1125. https://doi.org/10.1186/BF03352964
  23. Xu, G., Cui, C., Wan, R., Lai, A., Wan, X., Fu, Z., and Feng, G. (2012), Applicability of methods for estimating GPS precipitable water in the Qinghai-Tibet Plateau, Journal of Atmospheric and Solar-Terrestrial Physics, Vol. 89, pp. 76-82. https://doi.org/10.1016/j.jastp.2012.08.008

피인용 문헌

  1. 딥러닝 기반 GNSS 천정방향 대류권 습윤지연 추정 연구 vol.39, pp.1, 2019, https://doi.org/10.7848/ksgpc.2021.39.1.23