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A neural network relation of GPS results with continental hydrology

Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents the application of a neural network methodology to historical time series of GPS data from the IGS (International GPS Service) network, based on terrestrial water storage information. Hydrology signals at the GPS sites are important for including water loading corrections in GPS data processing. However, it is quite common that a correct global water storage model may not be available for this purpose, due to lack of science data. It is therefore mostly assumed that water mass redistribution is one of the potential contributors to the seasonal variations in GPS station position results, particularly, in the vertical direction. Presently, the IERS Special Bureau for Hydrology (SBH) has archived continental water storage data from some of the latest model developments. Examples include the monthly (GRACE, NOAA CPC, NCEP/NCAR CDAS-1) and daily (NCEP/NCAR and ECMWF reanalyses) solutions. It is valuable to study the relationship between these solutions and long-term geodetic results, especially as the water storage models continue to be refined. Using neural networks offers an effective approach to correlate the non-linear input of hydrology signals and output of geodetic results by recognizing the historic patterns between them. In this study, a neural network model is developed to enable the prediction of GPS height residuals based on the input of NOAA CPC hydrology data. The model is applied to eight GPS sites with satisfactory results.
Rocznik
Strony
23--32
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • Geospatial & Earth Monitoring Division Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia
Bibliografia
  • Andersen O. B., Seneviratne S. I., Hinderer J., and Viterbo, P. (2005). GRACE-derived terrestrial water storage depletion associated with the 2003 European heat wave, Geophys. Res. Lett., 32, L18405, doi:10.1029/2005GL023574.
  • Bose N. and Liang P. (1998). Neural Network Fundamentals with Graphs, Algorithms and Applications, Tata McGraw-Hill Publication, 478 pp.
  • David R. L. and Gregory M. J. (1999). Evaluating the use of “Goodness-of-Fit” measures in hydrologic and hydroclimatic model validation, Water Resources Research, 35(1), 233-241.
  • Dong D., Fang P., Bock Y., Cheng M. K., and Miyazaki S. (2002). Anatomy of apparent seasonal variations from GPS-derived site position time series, J. Geophys. Res, 107 (B4), doi:10.1029/2001JB000573.
  • Hammerstrom D. (1993). Working with neural networks, IEEE Spectrum, July, 46-53.
  • Han S.-C., Shum C. K., and Braun A. (2005). High-resolution continental water storage recovery from low-low satellite-to-satellite tracking, Journal of Geodynamics, 39, 11-28.
  • Haykin S. (1999). Neural Network: A Comprehensive Foundation, Prentice hall, New Jersey.
  • Khalil A. F., McKee M., Kemblowski M., and Asefa T. (2005). Basin scale water management and forecasting using artificial neural networks, J. American Water Resources Association, 41(1), 195-208.
  • Mathworks, Inc., 2004. Matlab 7.0 (Release 14), www.mathworks.com.
  • Takle E. S., Jha M., and Anderson C. J. (2005). Hydrological cycle in the upper Mississippi River basin: 20th century simulations by multiple GCMs, Geophys. Res. Lett., 32, L18407, doi:10.1029/2005GL023630.
  • Tapley B. D., Bettadpur S., Ries J. C., Thompson P. F., and Watkins M. M. (2004). GRACE measurements of mass variability in the Earth system, Science, 305(5683), doi:10.1126/science.1099192.
  • van Dam T. M., Wahr J. M., Milly P. C. D., Shmakin A. B., Blewitt G., Lavallee D., and Larson K. M. (2001). Crustal displacements due to continental water loading, Geophys. Res. Lett., 28, 651-654.
  • Velicogna I., Wahr J., Hanna E., and Huybrechts P. (2005). Short term mass variability in Greenland, from GRACE, Geophys. Res. Lett., 32, L05501, doi:10.1029/2004GL021948.
  • Wasserman P. (1993). Advanced methods in neural computing, Van Nostrand Reinhold, New York, 255 pp.
  • Wilson R. W. and Chen J. (2003). The Hydrology Bureau of the Global Geophysical Fluids Center. In: Richter et al. (eds.), IERS Technical Note No. 30. Proc. IERS Workshop on Combination Research and Global Geophysical Fluids, ISBN 3-89888-877-0, 153-156.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-adbf1798-8146-45e1-8863-6a7e6013fe34
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