Enhancing EOP Prediction through Technique-Specific and Multi-Technique Combined EOP series

Accurate prediction of EOP is essential for bridging the gap between real-time applications and the inherent latency of observational data processing. The quality of EOP predictions is significantly affected by the input data used. Merging multiple sources due to data latency can introduce inconsistencies in the input data. Therefore, ensuring internal consistency within the datasets is critical for achieving reliable predictions.

This study investigates EOP prediction using consistent input datasets derived from technique-specific solutions provided by the ILRS and IVS Analysis Centers at BKG and GNSS data from CODE (Center for Orbit Determination in Europe). Additionally, combined multi-technique EOP data developed by the BKG Combination Center are incorporated to evaluate their potential for enhancing predictive performance. Comparative analysis is performed against the official IERS datasets IERS 20 C04 and Bulletin A to evaluate these approaches' relative accuracy and reliability.

The results demonstrate the advantages of using internally consistent technique-specific and multi-technique combined datasets for EOP prediction. This work contributes to refining EOP prediction consistency, offering strategies to further improve the quality of operational EOP products.