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Non linear prediction of Earth Orientation Parameters

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Prediction of Earth Orientation Parameters mixing Empirical Orthogonal Function decomposition with SSA and Neural Networks

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Non linear prediction of Earth Orientation Parameters

  1. 1. Non linear Prediction of Earth Orientation Parameters N. Rouveyrollis, D. Gambis IERS Orientation Center - Paris Observatory UMR 8630 (France) $&# 6800$5 'LIIHUHQWDSSURDFKHVDUHXVHGIRUWKHSUHGLFWLRQRI(DUWK2ULHQWDWLRQ3DUDPHWHUV(23
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  5. 5. 3974/:.9 ,QWURGXFWLRQ Different approaches are used for prediction of the Earth rotation parameters. The IERS product center uses the following techniques : N Polar Motion: The formalism uses at first a floating period fit (Bevington, 1969 [2]) for both the Chandler and annual components estimation over a past time interval of several 0 years. An autoregressive filter is then applied on the short-term residuals series and used for the prediction. The predictions of the nutation offsets d and dH are based on an empirical model (Conventions 1996). N Universal Time: The present formalism used is based on the assumption that the long- term fluctuations (annual and semi-annual) of the preceding year are valid over the next few months. For short-term variations prediction, an autoregressive process is used.
  6. 6. 42 RPELQLQJ 66$ DQG 3UHGLFWLRQ ,3/ !7 Another approach to predict EOP series, is using SSA method (R. Vautard et al [1]) to SUHGLFWHGVROXWLRQEDVHGRQQRQOLQHDUDQDOVLVLVWKHQREWDLQHGIURPWKHVXPRIWKH extract significant components instead of adjusting the data to an a-priori model. A SUHGLFWLRQVSHUIRUPHGRYHUHDFKFRPSRQHQW We obtain the following algorithm : 6LJQLILFDQW 3UHGLFWLRQ FRPSRQHQW ,3 15 $ 6 '( ,' $ 6 *LYHQ $ 6 9, , 8 *OREDO VLJQDO 3UHGLFWLRQ $ 6LJQLILFDQW '7 3UHGLFWLRQ 0 FRPSRQHQWQ 8, Q $2 /1 5HVLGXDO 3UHGLFWLRQ Q
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