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Proceedings of the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) Workshop, online, February 15-16, 2022
Języki publikacji
Abstrakty
Precise positioning and navigation on the Earth’s surface and in space require accurate earth orientation parameters (EOP) data and predictions. In the last few decades, EOP prediction has become a subject of increased attention within the international geodetic community, e.g., space agencies, satellite operators, researchers studying Earth rotation dynamics, and users of navigation systems. Due to this fact, many research centres from around the world have developed dedicated methods for the forecasting of EOP. An assessment of the various EOP prediction capabilities is currently being pursued in the frame of the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), which began in September 2021 and will be continued until the end of the year 2022. The new campaign was prepared by the EOP PCC Office run by Centrum Badań Kosmicznych Polskiej Akademii Nauk (CBK PAN) in Warsaw, Poland, in cooperation with GeoForschungsZentrum (GFZ) and under the auspices of the International Earth Rotation and Reference Systems Service (IERS). In this paper, we provide an overview of the 2nd EOP PCC five months after its start. We discuss the technical aspects and present statistics about the participants and valid prediction files received so far. Additionally, we present the results of preliminary comparisons of different reference solutions with respect to the official IERS 14 C04 EOP series. Root mean square values for different solutions for polar motion, length of day, and precession-nutation components show discrepancies at the level from 0.04 to 0.36 mas, from 0.01 to 0.10 ms, and from 0.01 to 0.18 mas, respectively.
Słowa kluczowe
Rocznik
Strony
237--253
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
autor
- Centrum Badań Kosmicznych Polskiej Akademii Nauk, Warsaw, Poland
autor
- Centrum Badań Kosmicznych Polskiej Akademii Nauk, Warsaw, Poland
autor
- Warsaw University of Technology, Faculty of Civil Engineering, Warsaw, Poland
autor
- Centrum Badań Kosmicznych Polskiej Akademii Nauk, Warsaw, Poland
autor
- Section 1.3: Earth System Modelling, GFZ German Research Centre for Geosciences, Potsdam, Germany
autor
- Centrum Badań Kosmicznych Polskiej Akademii Nauk, Warsaw, Poland
Bibliografia
- Akyilmaz, O., Kutterer, H., Shum, C. K., & Ayan, T. (2011). Fuzzy-wavelet based prediction of Earth rotation parameters. Applied Soft Computing Journal, 11(1), 837-841. https://doi.org/10.1016/j.asoc.2010.01.003.
- Altamimi Z, Rebischung P, Métivier L, Collilieux X (2016) A new release of the International Terrestrial Reference Frame modeling nonlinear station motions. Journal of Geophysical Research: Solid Earth 8(121):6109-6131. https://doi.org/10.1002/2016JB013098.
- Belda, S., Ferrándiz, J. M., Heinkelmann, R., & Schuh, H. (2018). A new method to improve the prediction of the celestial pole offsets. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-32082-1.
- Bizouard, C. (2020). Geophysical Modelling of the Polar Motion, Berlin, Boston: De Gruyter, 2020. https://doi.org/10.1515/9783110298093.
- Bizouard, C., Lambert, S., Gattano, C., Becker, O., & Richard, J. Y. (2019). The IERS EOP 14C04 solution for Earth orientation parameters consistent with ITRF 2014. Journal of Geodesy, 93(5). https://doi.org/10.1007/s00190-018-1186-3.
- Chen, W., Li, J., Ray, J., & Cheng, M. (2017). Improved geophysical excitations constrained by polar motion observations and GRACE/SLR time-dependent gravity. Geodesy and Geodynamics, 8(6), 377-388. https://doi.org/10.1016/j.geog.2017.04.006.
- Chin, T. M., Gross, R. S., & Dickey, J. O. (2004). Modeling and forecast of the polar motion excitation functions for short-term polar motion prediction. Journal of Geodesy, 78(6). https://doi.org/10.1007/s00190-004-0411-4.
- Dill, R., Dobslaw, H., & Thomas, M. (2013). Combination of modeled short-term angular momentum function forecasts from atmosphere, ocean, and hydrology with 90-day EOP predictions. Journal of Geodesy, 87(6). https://doi.org/10.1007/s00190-013-0631-6.
- 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). https://doi.org/10.1007/s00190-018-1158-7 .
- Fey, A. L., Gordon, D., Jacobs, C. S., Ma, C., Gaume, R. A., Arias, E. F., Bianco, G., Boboltz, D. A., Böckmann, S., Bolotin, S. et al. (2015). The second realization of the international celestial reference frame by very long baseline interferometry. The Astronomical Journal 2(150):58. https://doi.org/10.1088/0004-6256/150/2/58.
- Gross, R. (2015). Theory of earth rotation variations. Sneeuw, N., Novák, P., Crespi, M., Sansò, F. (Eds.), VIII Hotine-Marussi Symposium on Mathematical Geodesy. https://doi.org/10.1007/1345_2015_13.
- IERS Conventions (2010). Gérard Petit and Brian Luzum (eds.). (IERS Technical Note ; 36) Frankfurt am Main: Verlag des Bundesamts für Kartographie und Geodäsie, 2010. 179 pp., ISBN 3-89888-989-6.
- 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., & Zotov, L. (2010). Achievements of the Earth orientation parameters prediction comparison campaign. Journal of Geodesy, 84(10). https://doi.org/10.1007/s00190-010-0387-1.
- Karbon, M., Soja, B., Nilsson, T., Deng, Z., Heinkelmann, R., & Schuh, H. (2017). Earth orientation parameters from VLBI determined with a Kalman filter. Geodesy and Geodynamics, 8(6). https://doi.org/10.1016/j.geog.2017.05.006.
- Kouba, J., Mireault, Y. (1998). IGS Orbit, Clock and EOP Combined Products: An Update. In: Brunner, F.K. (Eds.) Advances in Positioning and Reference Frames. International Association of Geodesy Symposia, 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03714-0_39.
- Luzum, B. (2010). Future of Earth Orientation Predictions. Artificial Satellites, 45(2), pp.107-110. https://doi.org/10.2478/v10018-010-0011-x.
- Malkin, Z. (2001). On Computation of Combined IVS EOP Series. In: D. Behrend, A. Rius (Eds.), Proc. 15th Working Meeting on European VLBI for Geodesy and Astrometry, Barcelona, Spain, Sep 07-08, 2001, 55-62, https://doi.org/10.48550/arXiv.physics/0610251.
- McCarthy, D. D., & Luzum, B. J. (1991). Observations of Luni-Solar and Free Core Nutation. International Astronomical Union Colloquium, 127. https://doi.org/10.1017/s025292110006406x.
- Modiri, S., Belda, S., Heinkelmann, R., Hoseini, M., Ferrándiz, J. M., & Schuh, H. (2018). Polar motion prediction using the combination of SSA and Copula-based analysis. Earth, Planets and Space, 70(1). https://doi.org/10.1186/s40623-018-0888-3.
- Nastula, J., Chin, T. M., Gross, R., Śliwińska, J., & Wińska, M. (2020). Smoothing and predicting celestial pole offsets using a Kalman filter and smoother. Journal of Geodesy, 94(3). https://doi.org/10.1007/s00190-020-01349-9.
- Nastula, J., Wińska, M., Śliwińska, J., & Salstein, D. (2019). Hydrological signals in polar motion excitation - Evidence after fifteen years of the GRACE mission. Journal of Geodynamics, 124, 119-132. https://doi.org/10.1016/j.jog.2019.01.014.
- Nilsson, T., Heinkelmann, R., Karbon, M., Raposo-Pulido, V., Soja, B., & Schuh, H. (2014). Earth orientation parameters estimated from VLBI during the CONT11 campaign. Journal of Geodesy, 88(5), 491–502. https://doi.org/10.1007/s00190-014-0700-5.
- Quinn K.J., Ponte R.M., Heimbach P., Fukumori I., Campin J-M. (2019). Ocean angular momentum from a recent global state estimate, with assessment of uncertainties, Geophysical Journal International, 216(1). https://doi.org/10.1093/gji/ggy452.
- Ratcliff, J. T. & Gross, R. S. (2019) Combinations of Earth Orientation Measurements: SPACE2018, COMB2018, and POLE2018. Jet Propulsion Laboratory, California Institute of Technology, Publication 19-7. https://trs.jpl.nasa.gov/bitstream/handle/2014/46964/19-7020.pdf.
- Sciarretta, C., Luceri, V., Pavlis, E. C., Bianco, G. (1020). The ILRS EOP Time Series. Artificial Satellites, 45(2), 41-48, https://doi.org/10.2478/v10018-010-0004-9.
- Shen, Y., Guo, J., Liu, X., Wei, X., & Li, W. (2017). One hybrid model combining singular spectrum analysis and LS + ARMA for polar motion prediction. Advances in Space Research, 59(2), 513-523. https://doi.org/10.1016/j.asr.2016.10.023.
- Stamatakos, N., Luzum, B., Stetzler, B., & Shumate, N. (2011). Recent Improvements in the Iers Rapid Service Prediction Center Products for 2010 and 2011. Journées Systèmes de Référence Spatio-Temporels, 125-128. https://syrte.obspm.fr/jsr/journees2011/pdf/stamatakos.pdf.
- Wang, G., Liu, L., Tu, Y., Xu, X., Yuan, Y., Song, M., & Li, W. (2018). Application of the radial basis function neural network to the short term prediction of the Earth’s polar motion. Studia Geophysica et Geodaetica, 62(2), 243-254. https://doi.org/10.1007/s11200-017-0805-4.
- Wooden W. & Gambis D. (2004). Explanatory supplement to IERS Bulletins A and B, https://hpiers.obspm.fr/iers/bul/bulb/explanatory.pdf.
- 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. Journal of Geodynamics, 62, 83-86. https://doi.org/10.1016/j.jog.2011.12.001.
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-a21a15c1-fcaf-46ea-a686-486586503fe0