Have a personal or library account? Click to login
Short-term prediction of UT1-UTC and LOD via Dynamic Mode Decomposition and combination of least-squares and vector autoregressive model Cover

Short-term prediction of UT1-UTC and LOD via Dynamic Mode Decomposition and combination of least-squares and vector autoregressive model

Open Access
|Mar 2024

References

  1. Bizouard, C., Lambert, S., Gattano, C., Becker, O., and Richard, J.-Y. (2018). The IERS EOP 14C04 solution for earth orientation parameters consistent with ITRF 2014. Journal of Geodesy, 93(5):621–633, doi:10.1007/s00190-018-1186-3.
  2. Dick, W. R. and Thaller, D. (2020). IERS Annual Report 2018. Technical report, International Earth Rotation and Reference Systems Service, Central Bureau.
  3. Dill, R., Dobslaw, H., and Thomas, M. (2018). Improved 90-day Earth orientation predictions from angular momentum forecasts of atmosphere, ocean, and terrestrial hydrosphere. Journal of Geodesy, 93(3):287–295, doi:10.1007/s00190-018-1158-7.
  4. Dobslaw, H. and Dill, R. (2018). Predicting Earth orientation changes from global forecasts of atmosphere-hydrosphere dynamics. Advances in Space Research, 61(4):1047–1054, doi:10.1016/j.asr.2017.11.044.
  5. Gambis, D. and Luzum, B. (2011). Earth rotation monitoring, UT1 determination and prediction. Metrologia, 48(4):S165–S170, doi:10.1088/0026-1394/48/4/s06.
  6. Gross, R. S., Eubanks, T., Steppe, J., Freedman, A., Dickey, J., and Runge, T. (1998). A Kalman-filter-based approach to combining independent Earth-orientation series. Journal of Geodesy, 72:215–235, doi:10.1007/s001900050162.
  7. Gross, R. S., Fukumori, I., Menemenlis, D., and Gegout, P. (2004). Atmospheric and oceanic excitation of length-of-day variations during 1980–2000. Journal of Geophysical Research: Solid Earth, 109(B1), doi:10.1029/2003jb002432.
  8. Guessoum, S., Belda, S., Ferrandiz, J. M., Modiri, S., Raut, S., Dhar, S., Heinkelmann, R., and Schuh, H. (2022). The short-term prediction of Length of Day using 1D convolutional neural networks (1D CNN). Sensors, 22(23):9517, doi:10.3390/s22239517.
  9. Holton, J. R. and Dmowska, R. (1989). El Niño, La Niña, and the southern oscillation. Academic press, Cambridge, MA, USA.
  10. Höpfner, J. (1998). Seasonal variations in length of day and atmospheric angular momentum. Geophysical Journal International, 135(2):407–437, doi:10.1046/j.1365-246X.1998.00648.x.
  11. Kalarus, M., Schuh, H., Kosek, W., Akyilmaz, O., Bizouard, C., Gambis, D., Gross, R., Jovanović, B., Kumakshev, S., Kutterer, H., Mendes Cerveira, P. J., Pasynok, S., and Zotov, L. (2010). Achievements of the Earth orientation parameters prediction comparison campaign. Journal of Geodesy, 84(10):587–596, doi:10.1007/s00190-010-0387-1.
  12. Kiani Shahvandi, M., Schartner, M., and Soja, B. (2022). Neural ODE differential learning and its application in polar motion prediction. Journal of Geophysical Research: Solid Earth, 127(11), doi:10.1029/2022jb024775.
  13. Kur, T., Dobslaw, H., Śliwińska, J., Nastula, J., Wińska, M., and Partyka, A. (2022). Evaluation of selected short-term predictions of UT1-UTC and LOD collected in the second earth orientation parameters prediction comparison campaign. Earth, Planets and Space, 74(1), doi:10.1186/s40623-022-01753-9.
  14. Lei, Y., Cai, H., and Zhao, D. (2017). Improvement of the prediction accuracy of polar motion using empirical mode decomposition. Geodesy and Geodynamics, 8(2):141–146, doi:10.1016/j.geog.2016.09.007.
  15. Liao, D., Wang, Q., Zhou, Y., Liao, X., and Huang, C. (2012). Long-term prediction of the Earth Orientation Parameters by the artificial neural network technique. Journal of Geodynamics, 62:87–92, doi:10.1016/j.jog.2011.12.004.
  16. Michalczak, M. and Ligas, M. (2021). Kriging-based prediction of the Earth’s pole coordinates. Journal of Applied Geodesy, 15(3):233–241, doi:10.1515/jag-2021-0007.
  17. Michalczak, M. and Ligas, M. (2022). The (ultra) short term prediction of length-of-day using kriging. Advances in Space Research, 70(3):610–620, doi:10.1016/j.asr.2022.05.007.
  18. Modiri, S., Belda, S., Hoseini, M., Heinkelmann, R., Ferrándiz, J. M., and Schuh, H. (2020). A new hybrid method to improve the ultra-short-term prediction of LOD. Journal of Geodesy, 94(2), doi:10.1007/s00190-020-01354-y.
  19. Nastula, J., Chin, T. M., Gross, R., Śliwińska, J., and Wińska, M. (2020). Smoothing and predicting celestial pole offsets using a Kalman filter and smoother. Journal of Geodesy, 94(3), doi:10.1007/s00190-020-01349-9.
  20. Niedzielski, T. and Kosek, W. (2011). Prediction Analysis of UT1-UTC Time Series by Combination of the Least-Squares and Multivariate Autoregressive Method, pages 153–157. Springer Berlin Heidelberg, doi:10.1007/978-3-642-22078-4_23.
  21. Okhotnikov, G. and Golyandina, N. (2019). EOP time series prediction using singular spectrum analysis. In Corpetti, T., Ienco, D., and Interdonato, R., editors, Proceedings of MACLEAN: MAChine learning for Earth observation workshop, RWTH Aahen University, CEUR Workshop Proceedings.
  22. Petit, G. and Luzum, B. (2010). Iers conventions. Technical report, IERS Technical Note 36, Verlag des Bundesamts für Kartographie und Geodäsie Frankfurt am Main, Germany.
  23. Schmid, P. J. (2010). Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics, 656:5–28, doi:10.1017/s0022112010001217.
  24. Schuh, H., Ulrich, M., Egger, D., Müller, J., and Schwegmann, W. (2002). Prediction of Earth orientation parameters by artificial neural networks. Journal of Geodesy, 76(5):247–258, doi:10.1007/s00190-001-0242-5.
  25. Soffel, M. (2013). Space-Time Reference Systems. SpringerLink. Springer, Berlin. Description based upon print version of record.
  26. Tirunagari, S., Kouchaki, S., Poh, N., Bober, M., and Windridge, D. (2017). Dynamic mode decomposition for univariate time series: analysing trends and forecasting. hal-01463744f.
  27. Tu, J. H., Rowley, W. C., Luchtenburg, D. M., Brunton, S. L., and Kutz, J. N. (2014). On dynamic mode decomposition: Theory and applications. Journal of Computational Dynamics, 1(2):391–421, doi:10.3934/jcd.2014.1.391.
  28. Xu, X., Zhou, Y., and Liao, X. (2012). Short-term earth orientation parameters predictions by combination of the least-squares, AR model and Kalman filter. Journal of Geodynamics, 62:83–86, doi:10.1016/j.jog.2011.12.001.
  29. Xu, X., Zhou, Y., and XU, C. (2022a). Earth rotation parameters prediction and climate change indicators in it. Artificial Satellites, 57(s1):262–273, doi:10.2478/arsa-2022-0023.
  30. Xu, X.-Q., Zhou, Y.-H., Duan, P.-S., Fang, M., Kong, Z.-Y., Xu, C.- C., and An, X.-R. (2022b). Contributions of oceanic and continental AAM to interannual variation in ∆LOD with the detection of 2020–2021 La Nina event. Journal of Geodesy, 96(6), doi:10.1007/s00190-022-01632-x.
  31. Śliwińska, J., Kur, T., Wińska, M., Nastula, J., Dobslaw, H., and Partyka, A. (2022). Second Earth Orientation Parameters prediction comparison campaign (2nd EOP PCC): Overview. Artificial Satellites, 57(s1):237–253, doi:10.2478/arsa-2022-0021.
DOI: https://doi.org/10.2478/rgg-2024-0006 | Journal eISSN: 2391-8152 | Journal ISSN: 0867-3179
Language: English
Page range: 45 - 54
Submitted on: Nov 7, 2023
|
Accepted on: Feb 20, 2024
|
Published on: Mar 9, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2024 Maciej Michalczak, Marcin Ligas, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.