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Tytuł artykułu

A study on along-track and cross-track noise of altimetry data by maximum likelihood: Mars Orbiter Laser Altimetry (MOLA) example

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The work investigates the spatial correlation of the data collected along orbital tracks of Mars Orbiter Laser Altimeter (MOLA) with a special focus on the noise variance problem in the covariance matrix. The problem of different correlation parameters in along-track and crosstrack directions of orbital or profile data is still under discussion in relation to Least Squares Collocation (LSC). Different spacing in along-track and transverse directions and anisotropy problem are frequently considered in the context of this kind of data. Therefore the problem is analyzed in this work, using MOLA data samples. The analysis in this paper is focused on a priori errors that correspond to the white noise present in the data and is performed by maximum likelihood (ML) estimation in two, perpendicular directions. Additionally, correlation lengths of assumed planar covariance model are determined by ML and by fitting it into the empirical covariance function (ECF). All estimates considered together confirm substantial influence of different data resolution in along-track and transverse directions on the covariance parameters.
Słowa kluczowe
Rocznik
Strony
143--155
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Department of Satellite Geodesy and Navigation Faculty of Geodesy, Geospatial and Civil Engineering University of Warmia and Mazury ul. Oczapowskiego 2 10-719 Olsztyn, Poland
autor
  • Department of Satellite Geodesy and Navigation Faculty of Geodesy, Geospatial and Civil Engineering University of Warmia and Mazury ul. Oczapowskiego 2 10-719 Olsztyn, Poland
Bibliografia
  • Aharonson, O., Zuber, M.T., Rothman, D.H., 2000. Statistics of Mars’ topography from MOLA: Slopes, correlations and physical models. Journal of Geophysical Research 106(E10), 23723-23736.
  • Andersen, O.B., Knudsen, P., 1998. Global marine gravity field from the ERS-1 and Geosat geodetic mission altimetry. Journal of Geophysical Research 103 (C4): 8129-8137.
  • Clark, I., 2010. Statistics or geostatistics? Sampling error or nugget effect? Journal of the Southern African Institute of Mining and Metallurgy 110, 307-312.
  • Darbeheshti, N., Featherstone, W.E., 2009. Non-stationary covariance function modelling in 2D least squares collocation. Journal of Geodesy 83(6): 495-508.
  • Dermanis, A., 1984. Kriging and Collocation - A Comparison. Manuscripta Geodaetica 9 (3), 159-167.
  • Dibarboure, G., Le Traon P.Y., Galin N., 2013. Exploring the Benefits of Using CryoSat-2's Cross-Track Interferometry to Improve the Resolution of Multisatellite Mesoscale Fields. Journal of Atmospheric and Oceanic Technology 30, 1511-1526.
  • Fok, H.S., Baki Iz H., Shum, C.K., Yi Y., Andersen, O., Braun, A., Yi C., Han, G., Kuo, C.Y., Matsumoto, K., Tony Song, Y., 2010. Evaluation of Ocean Tide Models Used for Jason-2 Altimetry Corrections. Marine Geodesy 33(1), 285-303.
  • Forsberg, R., Olesen, A.V., Keller, K., Møller, M., Gidskehaug A., Solheim, D., 2001. Airborne gravity and geoid surveys in the Arctic and Baltic seas. Proceedings of International Symposium on Kinematic Systems in Geodesy, Geomatics and Navigation (KIS-2001), Banff, pp. 586-593.
  • Hughes Clarke, J.E., 2003. Dynamic motion residuals in swath sonar data: Ironing out the creases. International Hydrographic Review 4(1):6-23.
  • Jarmołowski, W., 2013. A priori noise and regularization in least squares collocation of gravity anomalies. Geodesy and Cartography 62(2), 199-216.
  • Jarmołowski, W., Bakuła, M., 2014. Precise estimation of covariance parameters in leastsquares collocation by restricted maximum likelihood. Studia Geophysica et Geodaetica 58, 171-189.
  • Kim, J.W., Roman D.R., Lee B.Y., Kim Y., 2008. Altimetry Enhanced Free-air Gravity Anomalies in the High Latitude Region. Terrestrial, Atmospheric and Oceanic Sciences 19(1-2), 111-116.
  • Koch, K.R., 2007. Introduction to Bayesian Statistics. Second Edition. Springer-Verlag, Berlin, Germany.
  • Kusche, J., 2003. A Monte-Carlo technique for weight estimation in satellite geodesy. Journal of Geodesy 76, 641-652.
  • Mardia, K.V., 2007. Should geostatistics be model-based? In: Chen Q., Zhao P. and Agterberg F. (Eds.), Proceedings of the IAMG 2007 Conference: Geomathematics and GIS Analysis of Resources, Environment and Hazards. International Association for Mathematical Geosciences, Houston, TX (http://www1.maths.leeds.ac.uk/~sta6kvm/IAMGBeijingFinal.pdf).
  • McGovern, P.J., Solomon S.C., Smith D.E., Zuber M.T., Simons M., Wieczorek M.A., Phillips R.J., Neumann G.A., Aharonson O., Head J.W., 2002. Localized gravity/topography admittance and correlation spectra on Mars: Implications for regional and global evolution, Journal of Geophysical Research 107(E12), 5136, doi:10.1029/2002JE001854.
  • Matsumoto, K., Takanezawa, T., Ooe M., 2000. Ocean Tide Models Developed by Assimilating TOPEX/POSEIDON Altimeter Data into Hydrodynamical Model: A Global Model and a Regional Model around Japan, Journal of Oceanography 56, 567-581.
  • Moritz, H., 1980. Advanced Physical Geodesy. Herbert Wichmann Verlag, Karlsruhe.
  • Neumann, G.A., Rowlands D.D., Lemoine F.G., Smith D.E., Zuber M.T., 2001. Crossover analysis of Mars Orbiter Laser Altimeter data. Journal of Geophysical Research 106 (E10), 23753-23768.
  • Olesen, A.V., Forsberg, R., Keller, K., Gidskehaug, A., 2000. Airborne gravity survey of Lincoln sea and Wandel Sea, North Greenland. Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 25(1), 25-29.
  • Paciorek, C.J., Schervish, M.J., 2006. Spatial modelling using a newclass of non-stationary covariance functions. Environmetrics 17(5), 483-506.
  • Popielarczyk, D., Templin, T., 2014. Application of Integrated GNSS/Hydroacoustic Measurements and GIS Geodatabase Models for Bottom Analysis of Lake Hancza: the Deepest Inland Reservoir in Poland, Pure and Applied Geophysics 171, 997-1011.
  • Sandwell, D.T., Smith, W.H.F., 1997. Marine gravity anomaly from Geosat and ERS-1 satellite altimetry. Journal of Geophysical Research 102, 10039-10054.
  • Searle, S.R., Casella, G., McCulloch, C.E., 1992. Variance Components, Wiley, New York.
  • Smith, D.E., et al. 2001. Mars Orbiter Laser Altimeter: Experiment summary after the first year of global mapping of Mars. Journal of Geophysical Research 106(E10), 23689-23722, doi:10.1029/2000JE001364.
  • Som, S.M., Greenberg, H.M., Montgomery, D.R., 2008. The Mars Orbiter Laser Altimeter dataset: Limitations and improvements. Mars 4, 14-26, doi:10.1555/mars.2008.0002.
  • van Loon, J., 2008. Functional and stochastic modelling of satellite gravity data. PhD thesis, Delft University of Technology.
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-55f43a8f-019c-46e6-ab22-ac3b8de01d1b
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