Identyfikatory
Warianty tytułu
Konferencja
Proceedings of the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) Workshop, online, February 15-16, 2022
Języki publikacji
Abstrakty
As one of the participants in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), we submitted two data files. One is 365 days’ predictions into the future for Earth orientation parameters (EOP) (the position parameters Px and Py, the time parameters UT1-UTC and length of day changes ΔLOD), processed by the traditional least-square and autoregressive (LS + AR) model. Another is 90 days’ predictions by the combined least-square and convolution method (LS + Convolution), with effective angular momentum (EAM) from Earth System Modelling GeoForschungsZentrum in Potsdam (ESMGFZ). Results showed that the LS + Convolution method performed better than the LS + AR model in short-term EOP predictions within 10 days, while the traditional LS + AR model presented higher accuracy in medium-term predictions over 10-90 days. Furthermore, based on the climate change information in Earth’s rotation (mainly in the interannual variations of LOD), the climate change indicators are investigated with ΔLOD observations and long-term predictions. After two intermediate La Nina events were detected in the climate-related ΔLOD observations during the period of 2020-2022, another stronger La Nina phenomenon is indicated in the climate-related ΔLOD long-term predictions.
Rocznik
Strony
262--273
Opis fizyczny
Bibliogr. 32 poz., tab., wykr.
Twórcy
autor
- Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Planetary Sciences, Chinese Academy of Sciences, Shanghai, China
- School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, China
autor
- Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Planetary Sciences, Chinese Academy of Sciences, Shanghai, China
- School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, China
autor
- Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai, China
- School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, China
Bibliografia
- Akaike H. (1971). Autoregressive model fitting for control, Ann Inst Stat Math, 23, 163-180.
- Bizouard C., Remus F., Lambert S., Seoane L., Gambis D. (2011). The Earth’s variable Chandler wobble, A&A, 526, A106.
- Brockwell P.J., Davis R.A. (1996). Introduction to time series and forecasting, Springer, New York, 420.
- Chen J.L., Wilson C.R., Kuang W.J., Chao B.F. (2019). Interannual oscillations in Earth rotation, Journal of Geophysical Research: Solid Earth, 124.
- Dickey J.O., Marcus S.L., Chin T.M. (2007). Thermal wind forcing and atmospheric angular momentum: Origin of the Earth's delayed response to ENSO, Geophysical Research Letters, 34, 17803(1-5).
- Dill R., Dobslaw H., Thomas M. (2019). Improved 90-day earth orientation predictions from angular momentum forecasts of atmosphere, ocean, and terrestrial hydrosphere, Journal of Geodesy, 93(3), 287-295.
- Dobslaw H., Dill R. (2018). Predicting Earth Orientation Changes from Global Forecasts of Atmosphere-Hydrosphere Dynamics, Adv. Space Res, 61(4), 1047-1054.
- Eubanks T.M., Smith D.E., Turcotte D.L. (1993). Variations in the orientation of the Earth, Geodynamics Series, 24, 1-54.
- Gambis D. (2004). Monitoring Earth Orientation using space-geodetic techniques: state-of-theart and prospective, Journal of Geodesy, 78, 295-303.
- Guo J.Y., Li Y.B., Dai C.L., Shum C.K. (2013). A technique to improve the accuracy of Earth orientation prediction algorithms based on least squares extrapolation, J. Geodyn .70, 36-48.
- Gerard P., Brian L. (2010). IERS Conventions (2010), 50-126.
- Gross R.S., Eubanks T.M., Steppe J.A., Freedman A.P., Dickey J.O., Runge T.F. (1998). A Kalman filter-based approach to combining independent Earth-orientation series, J Geod,72, 215-235.
- Gross R.S. (1992). Correspondence between theory and observations of polar motion, Geophys. J. In, 109, 162-170.
- Haddad M., Bonaduce A. (2017). Interannual variations in length of day with respect to El Niño- Southern Oscillation’s impact (1962-2015), Arab J Geosci, 10(11), 1-10.
- Hsu C.C., Duan P.S., Xu X.Q., Zhou Y.H., Huang C.L. (2021). A new ~7 year periodic signal in length of day from a FDSR method, Journal of Geodesy, 95:55.
- Kalarus M., Schuh H., Kosek W., Akyilmaz O., Bizouard Ch., Gambis D., Gross R.S., Jovanovic B., Kumakshev S., Kutterer H., Mendes Cerveira P.J., Pasynok S., Zotov L. (2010). Achievements of the Earth orientation parameters prediction comparison campaign, J Geod, 84, 587-596.
- Kosek W., Kalarus M., Johnson T. J., Wooden W.H., McCarthy D.D., Popinski W. (2005). A comparison of LOD and UT1-UTC forecasts by different combination prediction techniques, Artificial Satellites, 40, 119-125.
- Lambert S.B., Marcus S.L., Viron O.D. (2017). Atmospheric torques and Earth's rotation: what drove the millisecond-level length-of-day response to the 2015-2016 El Nino? Earth System Dynamics Discussions, 8, 1-14.
- Lei Y., Guo M., Hu D., Cai H., Zhao D., Hu Z., Gao Y. (2017). Short-term prediction of UT1-UTC by combination of the grey model and neural networks, Adv Space Res, 59(2), 524-531.
- Modiri S., Belda S., Hoseini M., Heinkelmann R., Ferrándiz J., Schuh H. (2020). A new hybrid method to improve the ultra-short-term prediction of LOD, J Geod, 94, 23.
- Ratcliff J., Gross R. (2019). Combinations of Earth Orientation Measurements: SPACE2018, COMB2018, and POLE2018, Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration.
- Seitz, F., & Schmidt, M. 2005. Atmospheric and oceanic contributions to Chandler Wobble excitation determined by wavelet filtering. J. Geophy. Res, 110, B11406.
- Su X., Liu L., Hsu H., Wang G. (2014). Long-term polar motion prediction using normal time- frequency transform, J Geod, 88,145-155.
- Schuh H., Ulrich M., Egger D., Muller J. (2002). Prediction of Earth orientation parameters by artificial neural networks, J Geod, 76, 247-258.
- Wang Q., Du Y., Liu J. (2014). Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model, J. Cent. South Univ, 21, 1396-1401.
- Wu F., Chang G., Deng K. (2019). One-step method for predicting LOD parameters based on LS+AR model, J Spat Sci.
- Xu X.Q., Zhou Y.H., Duan P.S., Fang M., Kong Z.Y., Xu C.C., An X.R. (2022). Contributions of oceanic and continental AAM to interannual variation in ΔLOD with the detection of 2020-2021 La Nina event, J Geod, 96, 43.
- Xu X.Q., Zhou Y.H. (2015). EOP prediction using least square fit in and autoregressive filter over optimized data intervals, Adv. Space Res, 56, 2248-2253.
- Xu X.Q., Zhou Y.H., Liao X.H. (2012). Short-term earth orientation parameters predictions by combination of the least squares, AR model and Kalman filter, J. Geodyn, 62, 83-86.
- Zhou Y.H., Chen J.L., Salstein D. (2008). Troposphere and stratospheric wind contributions to Earth’s variable rotation from NCEP/NCAR reanalyses (2000-2005), Geophysical Journal International, 174, 453-463.
- Zotov L., Bizouard C., Shum C.K., Zhang C.Y., Sidorenkov N., Yushkin V. (2022). Analysis of Earth’s polar motion and length of day trends in comparison with estimates using second degree stokes coefficients from satellite gravimetry, Advances in Space Research, 69,308-318.
- Zotov L., Xu X.Q., Zhou Y.H., Skorobogatov A. (2018). Combined SAI-SHAO prediction of Earth Orientation Parameters since 2012 till 2017, Geodesy and Geodynamics, 9(6), 485-490.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0b65bdc1-836f-4519-be2e-27510af3553f