Pro- and Retrospective highly accurate and consistent Earth Orientation parameters for Geodetic Research within the Earth System Sciences (PROGRESS)

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The accurate knowledge of the Earth’s orientation and rotation in space is essential for a broad variety of scientific and societal applications such as near Earth and deep space communication, global positioning and navigation as well as monitoring of global change phenomena. The Earth Orientation Parameters (EOPs) describe the position of the Earth’s rotation axis as seen from space (Celestial Intermediate Pole (CIP) offsets delta XCIP and delta YCIP) and as seen from the Earth’s surface (Polar Motion (PM) or Terrestrial Intermediate Pole (TIP) offsets xpole and ypole). Moreover, the EOPs describe the rotation of the Earth around its rotation axis (angular velocity, delta UT1). Beside the offsets, the EOPs also comprise the temporal variation of the Earth’s orientation and rotation in form of CIP and PM rates and LOD (Length Of Day). While the CIP offsets can be modelled very accurately, the Earth Rotation Parameters (ERPs), namely PM, delta UT1 and their rates, are accessible via the four space geodetic techniques Satellite and Lunar Laser Ranging (SLR, LLR), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), Global Navigation Satellite Systems (GNSS), and Very Long Baseline Interferometry (VLBI). Current EOP combination approaches and predictions lack consistency and do not longer fulfil state-of-the-art accuracy requirements. This project aims at the development of an optimal combination strategy of final and rapid ERPs together with a prediction based on Effective Angular Momentum (EAM) data. Thereby, the focus will be put on a consistent mathematical modelling of all unknown parameters and homogeneous background models. Moreover, the focus of the project is on the development of a refined combination at normal equation (NEQ) level extended by an appropriate filtering (Kalman- or information-filter based). For the prediction, approaches based on EAM data will be further developed, investigating the potential of the use of Artificial Intelligence (AI) or Artificial Neural Network (ANN) methods.