English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Findings on celestial pole offsets predictions in the second earth orientation parameters prediction comparison campaign (2nd EOP PCC)

Authors

Wińska,  Małgorzata
External Organizations;

Kur,  Tomasz
External Organizations;

Śliwińska-Bronowicz,  Justyna
External Organizations;

Nastula,  Jolanta
External Organizations;

/persons/resource/dobslaw

Dobslaw,  Henryk
1.3 Earth System Modelling, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Partyka,  Aleksander
External Organizations;

Belda,  Santiago
External Organizations;

Bizouard,  Christian
External Organizations;

Boggs,  Dale
External Organizations;

Chin,  Mike
External Organizations;

/persons/resource/sdhar

Dhar,  Sujata
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Ferrandiz,  Jose M.
External Organizations;

Gou,  Junyang
External Organizations;

Gross,  Richard
External Organizations;

Guessoum,  Sonia
External Organizations;

/persons/resource/rob

Heinkelmann,  R.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Modiri,  Sadegh
External Organizations;

Ratcliff,  Todd
External Organizations;

/persons/resource/raut

Raut,  Shrishail
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Schartner,  Matthias
External Organizations;

/persons/resource/schuh

Schuh,  H.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Kiani Shahvandi,  Mostafa
External Organizations;

Soja,  Benedikt
External Organizations;

Thaller,  Daniela
External Organizations;

Wu,  Yuanwei
External Organizations;

Xu,  Xueqing
External Organizations;

Yang,  Xinyu
External Organizations;

Zhao,  Xin
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)

5028795.pdf
(Publisher version), 9MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Wińska, M., Kur, T., Śliwińska-Bronowicz, J., Nastula, J., Dobslaw, H., Partyka, A., Belda, S., Bizouard, C., Boggs, D., Chin, M., Dhar, S., Ferrandiz, J. M., Gou, J., Gross, R., Guessoum, S., Heinkelmann, R., Modiri, S., Ratcliff, T., Raut, S., Schartner, M., Schuh, H., Kiani Shahvandi, M., Soja, B., Thaller, D., Wu, Y., Xu, X., Yang, X., Zhao, X. (2024): Findings on celestial pole offsets predictions in the second earth orientation parameters prediction comparison campaign (2nd EOP PCC). - Earth Planets and Space, 76, 100.
https://doi.org/10.1186/s40623-024-02042-3


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5028795
Abstract
In 2021, the International Earth Rotation and Reference Systems Service (IERS) established a working group tasked with conducting the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) to assess the current accuracy of EOP forecasts. From September 2021 to December 2022, EOP predictions submitted by participants from various institutes worldwide were systematically collected and evaluated. This article summarizes the campaign's outcomes, concentrating on the forecasts of the dX, dY, and dψ, dε components of celestial pole offsets (CPO). After detailing the campaign participants and the methodologies employed, we conduct an in-depth analysis of the collected forecasts. We examine the discrepancies between observed and predicted CPO values and analyze their statistical characteristics such as mean, standard deviation, and range. To evaluate CPO forecasts, we computed the mean absolute error (MAE) using the IERS EOP 14 C04 solution as the reference dataset. We then compared the results obtained with forecasts provided by the IERS. The main goal of this study was to show the influence of different methods used on predictions accuracy. Depending on the evaluated prediction approach, the MAE values computed for day 10 of forecast were between 0.03 and 0.16 mas for dX, between 0.03 and 0.12 mas for dY, between 0.07 and 0.91 mas for dψ, and between 0.04 and 0.41 mas for dε. For day 30 of prediction, the corresponding MAE values ranged between 0.03 and 0.12 for dX, and between 0.03 and 0.14 mas for dY. This research shows that machine learning algorithms are the most promising approach in CPO forecasting and provide the highest prediction accuracy (0.06 mas for dX and 0.08 mas for dY for day 10 of prediction).