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ESMGFZ EAM Products for EOP Prediction: Toward the Quantification of Time Variable EAM Forecast Errors Cover

ESMGFZ EAM Products for EOP Prediction: Toward the Quantification of Time Variable EAM Forecast Errors

Artificial Satellites
Volume 58 (2023): Issue 4 (December 2023)
By: Robert Dill,  Henryk Dobslaw and  Maik Thomas  
Open Access
|Jan 2024
Authors

Authors

Robert Dill

dill@gfz-potsdam.de

Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany

Henryk Dobslaw

Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany

Maik Thomas

Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
DOI: https://doi.org/10.2478/arsa-2023-0013 | Journal eISSN: 2083-6104 | Journal ISSN: 1509-3859
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Language: English
Page range: 330 - 340
Submitted on: May 25, 2023
Accepted on: Dec 4, 2023
Published on: Jan 19, 2024
Published by: Polish Academy of Sciences, Space Research Centre
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
effective angular momentum function,
machine learning,
forecast error,
level of uncertainty
Related subjects:
Geosciences,
Geosciences, other

© 2024 Robert Dill, Henryk Dobslaw, Maik Thomas, published by Polish Academy of Sciences, Space Research Centre
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 58 (2023): Issue 4 (December 2023)
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