EOP Prediction with special focus on using EOP products by different space geodetic techniques as input

Variations in Earth orientation parameters (EOP) are related to mass redistribution, gravitational, and geodynamic processes in the Earth system and have gained a great deal of attention in Earth science, astronomy, and climate change studies. In addition, real-time EOP information is needed for many space geodetic applications, including satellite navigation from the ground and low-Earth orbit, like tracking interplanetary spacecraft and forecasting the weather. Currently, the EOP can be estimated at the best possible accuracy with modern high-precision space geodetic techniques like Very Long Baseline Interferometry (VLBI), Global Navigation Satellite Systems (GNSS), and Satellite Laser Ranging (SLR). However, the complex nature of data processing and the time it takes to process it always lead to delays. Consequently, predicting EOP is of great scientific and practical importance. Accordingly, several methods have been developed and applied to EOP prediction. In spite of this, the accuracy of EOP still needs to meet our expectations, even for forecasts of a few days into the future. We will therefore have to face two major challenges in order to provide the best prediction data: which input data to use and which prediction methods are superior to others. In order to answer these two questions, new methods or a combination of existing approaches are investigated to improve the accuracy of the predicted EOP time seires. Such in-depth investigations are currently conducted within the “Second EOP Prediction Comparison Campaign (EOP-PCC)” organized by IAG and IERS. In this study, we investigate a redesigned prediction package (input data and method) to improve the possibility of bridging the existing gap between the observation and the final estimated product.

We will briefly present our contribution to EOP-PCC and illustrate the result of EOP data obtained from single space geodetic techniques provided by the department of geodesy at BKG. Then, we run our prediction algorithm with the official IERS EOP series and our BKG’s single-technique analysis products for VLBI and SLR using the combination of a deterministic and a stochastic method and compare it with different prediction techniques. Finally, we will show the potential of using a combination of VLBI and GNSS techniques to obtain real-time EOP estimates.