Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Retrieval and Validation of Precipitable Water Vapor using GPS Datasets of Mobile Observation Vehicle on the Eastern Coast of Korea

  • Kim, Yoo-Jun (High Impact Weather Research Center, Observation Research Division, National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Kim, Seon-Jeong (High Impact Weather Research Center, Observation Research Division, National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Kim, Geon-Tae (High Impact Weather Research Center, Observation Research Division, National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Choi, Byoung-Choel (High Impact Weather Research Center, Observation Research Division, National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Shim, Jae-Kwan (High Impact Weather Research Center, Observation Research Division, National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Kim, Byung-Gon (Department of Atmospheric and Environmental Sciences, Gangneung-Wonju National University)
  • Received : 2016.06.09
  • Accepted : 2016.07.16
  • Published : 2016.08.31
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Abstract

The results from the Global Positioning System (GPS) measurements of the Mobile Observation Vehicle (MOVE) on the eastern coast of Korea have been compared with REFerence (REF) values from the fixed GPS sites to assess the performance of Precipitable Water Vapor (PWV) retrievals in a kinematic environment. MOVE-PWV retrievals had comparatively similar trends and fairly good agreements with REF-PWV with a Root-Mean-Square Error (RMSE) of 7.4 mm and $R^2$ of 0.61, indicating statistical significance with a p-value of 0.01. PWV retrievals from the June cases showed better agreement than those of the other month cases, with a mean bias of 2.1 mm and RMSE of 3.8 mm. We further investigated the relationships of the determinant factors of GPS signals with the PWV retrievals for detailed error analysis. As a result, both MultiPath (MP) errors of L1 and L2 pseudo-range had the best indices for the June cases, 0.75-0.99 m. We also found that both Position Dilution Of Precision (PDOP) and Signal to Noise Ratio (SNR) values in the June cases were better than those in other cases. That is, the analytical results of the key factors such as MP errors, PDOP, and SNR that can affect GPS signals should be considered for obtaining more stable performance. The data of MOVE can be used to provide water vapor information with high spatial and temporal resolutions in the case of dramatic changes of severe weather such as those frequently occurring in the Korean Peninsula.

Keywords

References

  1. Bennitt, G.V. and A. Jupp, 2012. Operational assimilation GPS zenith total delay observations into the Met. Office numerical weather prediction models, Monthly Weather Review, 140: 2706-2719. https://doi.org/10.1175/MWR-D-11-00156.1
  2. Bevis, M., S. Businger, T.A. Herring, C. Rocken, R.A. Anthes, and R.H. Ware, 1992. GPS Meteorology: Remote sensing of atmospheric water vapor using the global positioning system, Journal of Geophysical Research, 97: 15787-15801. https://doi.org/10.1029/92JD01517
  3. Bock, O., M.N. Bouin, E. Doerfinger, P. Collard, F. Masson, R. Meynadier, S. Nahmani, M. Koite, K. G.L. Balawan, F. Dide, D. Ouedraogo, S. Pokperlaar, J.-B. Ngamini, J.P. Lafore, S. Janicot, F. Guichard, and M. Nuret, 2008. West African monsoon observed with ground-based GPS receivers during African Monsoon Multidisciplinary Analysis (AMMA), Journal of Geophysical Research, 113: D21105, doi: 10.1029/2008JD010327.
  4. Boniface, K., C. Champollion, J. Chery, V. Ducrocq, C. Rocken, E. Doerflinger, and P. Collard, 2012. Potential of shipborne GPS atmospheric delay data for prediction of Mediterranean intense weather events, Atmospheric Science Letters, 13: 250-256. https://doi.org/10.1002/asl.391
  5. Cai, C. and Y. Gao, 2013. Modeling and assessment of combined GPS/GLONASS precise point positioning, GPS Solutions, 17: 223-236. https://doi.org/10.1007/s10291-012-0273-9
  6. Chen, H.Y., S.B. Yu, H. Tung, T. Tsujii, and M. Ando, 2011. GPS medium-range kinematic positioning for the seafloor geodesy off eastern Taiwan, Engineering Journal, 15: 17-24.
  7. Choi, B.K., S.M. Yoo, K.M. Roh, S.J. Lee, 2013. Realtime GPS ionospheric TEC estimation over South Korea, Journal of Astronomy and Space Sciences, 30(3): 207-212. https://doi.org/10.5140/JASS.2013.30.3.207
  8. Cucurull, L., F. Vandenberghe, D. Barker, E. Vilaclara, and A. Rius, 2014. Three-dimensional variational data assimilation of ground-based GPS ZTD and meteorological observations during the 14 December 2001 storm event over the western Mediterranean Sea, Monthly Weather Review, 132: 749-763.
  9. Dach, R., U. Hugentobler, P. Fridez, and M. Meindl, 2007. Bernese GPS software version 5.0, Astronomical Institute, University of Bern.
  10. Daud, M.E., T. Sagiya, F. Kimata, and T. Kato, 2008. Long-baseline quasi-real time kinematic GPS data analysis for early tsunami warning, Earth Planets and Space, 60: 1191-1195. https://doi.org/10.1186/BF03352877
  11. Davis, J.L., T.A. Herring, I.I. Shapiro, A.E.E. Gogers, and G. Elgered, 1985. Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length, Radio Science, 20: 1593-1607. https://doi.org/10.1029/RS020i006p01593
  12. Dietrich, S.V.R., K.P. Johnson, J. Miao, and G. Heygster, 2004. Comparison of tropospheric water vapor over Antarctica derived from AMSU-B data, ground-based GPS data and the NCEP/NCAR reanalysis, Journal of the Meteorological Society of Japan, 82: 259-267. https://doi.org/10.2151/jmsj.2004.259
  13. Eckl, M.C., R.A. Snay, T. Soler, M.W. Cline, and G.L. Mader, 2001. Accuracy of GPS-derived relative positions as a function of interstation distance and observing-session duration, Journal of Geodesy, 75: 633-640. https://doi.org/10.1007/s001900100204
  14. Estey, L.H. and C.M. Meertens, 1999. TEQC: The multi-purpose toolkit for GPS/GLONASS data, GPS Solutions, 3: 42-49. https://doi.org/10.1007/PL00012778
  15. Fujita, M., F. Kimura, K. Yoneyama, and M. Yoshizaki, 2008. Verification of precipitable water vapor estimated from shipborne GPS measurements, Geophysical Research Letters, 35: L13803, doi:10.1029/2008GL033764.
  16. Groves, P.D., 2007. Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems, Artech House, 64.
  17. Ha, J.H., K.D. Park, and B.H. Heo, 2008. Comparisons of a local mean temperature equation for GPSbased precipitable water vapor over the Korean Peninsula, Journal of Astronomy and Space Sciences, 25: 425-434 (in Korean with English abstract). https://doi.org/10.5140/JASS.2008.25.4.425
  18. Huber, K., F. Heuberger, C. Abart, A. Karabatic, R. Weber, and P. Berglez, 2010. PPP: Precise Point Positioning - Constraints and Opportunities, Proc. of International Federation of Surveyors Congress 2010, Sydney, TS 10C, 1-17.
  19. Kim, D.S., J.H. Won, H.I. Kim, K.H. Kim, and K.D. Park, 2010. Accuracy analysis of GPS-derived precipitable water vapor according to interpolation methods of meteorological data, Journal of Korea Spatial Information Society, 18: 33-41 (in Korean with English abstract).
  20. Kim, M.C., J.W. Seo, and J.Y. Lee, 2014. A comprehensive method for GNSS data quality determination to improve ionospheric data analysis, Sensors, 14: 14971-14993. https://doi.org/10.3390/s140814971
  21. Kim, Y.J., S.O. Han, K.H. Kim, S.J. Kim, G.T. Kim, and B.G. Kim, 2014. An analysis of the least observing-session duration of GPS for the retrieval of precipitable water vapor, Atmosphere, 24(3): 391-402 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2014.24.3.391
  22. Kjorsvik, N.S., 2006. Handling of the tropospheric delay in kinematic precise point positioning, Proc. of the Institute of Navigation (ION) GNSS 2006, 2279-2285, Fort Worth, Texas, USA.
  23. Kuo, Y.H., Y.R. Guo, and E.R. Westwater, 1993. Assimilation of precipitable water measurements into a mesoscale numerical model, Monthly Weather Review, 121: 1215-1238. https://doi.org/10.1175/1520-0493(1993)121<1215:AOPWMI>2.0.CO;2
  24. Kwon, B.M., J.H. Moon, Y.S. Shin, H.D. Choi, and G.R. Cho, 2011. Day-to-day repeatability of the navigation solution and SNR from the GPS receiver installed on KSLV-I, Journal of the Korean Society for Aeronautical Space Sciences, 39(8): 774-787 (in Korean with English abstract). https://doi.org/10.5139/JKSAS.2011.39.8.774
  25. Kwon, H.T., T. Iwabuchi, and G.H. Lim, 2007. Comparison of precipitable water derived from ground-based GPS measurements with radiosonde observations over the Korean Peninsula, Journal of the Meteorological Society of Japan, 85: 733-746. https://doi.org/10.2151/jmsj.85.733
  26. Larson, K. and D.L. Hartmann, 2003. Interactions among cloud, water vapor, radiation, and largescale circulation in the tropical climate. Part II: Sensitivity to spatial gradients of sea surface temperature, Journal of Climate, 16: 1441-1455. https://doi.org/10.1175/1520-0442(2003)016<1441:IACWVR>2.0.CO;2
  27. Lee, S.W., J. Kouba, B. Schutz, D.H. Kim, and Y.J. Lee, 2013. Monitoring precipitable water vapor in real-time using global navigation satellite systems, Journal of Geodesy, 87: 923-934. https://doi.org/10.1007/s00190-013-0655-y
  28. Macpherson, S.R., G. Deblonde, and J.M. Aparicio, 2008. Impact of NOAA ground-based GPS observations on the Canadian regional analysis and forecast system, Monthly Weather Review, 136: 2727-2746. https://doi.org/10.1175/2007MWR2263.1
  29. Martin, A., A.B. Anquela, J.L. Berne, and M. Sanmartin, 2012. Kinematic GNSS-PPP results from various software packages and raw data configurations, Scientific Research and Essays, 7(3): 419-431.
  30. Moore, A.W., I.J. Small, S.I. Gutman, Y. Bock, J.L. Dumas, P. Fang, J.S. Haase, M.E. Jackson, and J.L. Laber, 2015. National weather service forecasters use GPS precipitable water vapor for enhanced situational awareness during the Southern California summer monsoon, Bulletin of the American Meteorological Society, 96: 1867-1877. https://doi.org/10.1175/BAMS-D-14-00095.1
  31. Rocken, C., J. Johnson, T.V. Hove, and T. Iwabuchi, 2005. Atmospheric water vapor and geoid measurements in the open ocean with GPS, Geophysical Research Letters, 32: L12813.
  32. Saastamoinen, J.H., 1972. Atmospheric correction for the troposphere and stratosphere in radio ranging of satellites, Geophysical Monograph Series, 15: 247-252.
  33. Shi, J., C. Xu, J. Guo, and Y. Gao, 2015. Real-time GPS precise point positioning-based precipitable water vapor estimation for rainfall monitoring and forecasting, IEEE Transactions on Geoscience and Remote Sensing, 53(6): 3452-3459. https://doi.org/10.1109/TGRS.2014.2377041
  34. Skone, S., Y. Gao, O. Al-Fanek, W. Tao, Y. Zhang, P. H?roux, and P. Collins, 2006. Atmospheric moisture estimation using GPS on a moving platform, Proceedings of the 19th International Technical Meeting of the Satellite Division of the Institute of Navigation, Vol. 2, Institute of Navigation, 1891-1901.
  35. Takasu, T., N. Kubo, and A. Yasuda, 2007. Development, evaluation and application of RTKLIB: A program library for RTK-GPS, Proc. of GPS/GNSS Symposium 2007 (in Japanese).
  36. Takasu, T., and A. Yasuda, 2010. Kalman-filter-based integer ambiguity resolution strategy for longbaseline RTK with ionosphere and troposphere estimation, Proc. of Institute of Navigation (ION) GNSS 2010, Portland, Oregon, 161-171.
  37. Webb, S.R., N.T. Penna, P.J. Clarke, S. Webster, I. Martin, and G.V. Bennitt, 2016. Kinematic GNSS estimation of zenith wet delay over a range of altitudes, Journal of Atmospheric and Oceanic Technology, 33: 3-15. https://doi.org/10.1175/JTECH-D-14-00111.1
  38. Yao, Y., C. Yu, and Y. Hu, 2014. A new method to accelerate PPP convergence time by using a global zenith troposphere delay estimation model, Journal of Navigation, 67: 899-910. https://doi.org/10.1017/S0373463314000265
  39. Zhang, K., S. Wu, and F. Wu, 2007. The latest development of a network-based RTK system in Australia, International Journal of Scientific Research, 2(1): 87-94.

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