Data Driven Approaches for the Prediction of Earth's Effective Angular Momentum Functions
- Resource Type
- Conference
- Authors
- Shahvandi, Mostafa Kiani; Gou, Junyang; Schartner, Matthias; Soja, Benedikt
- Source
- IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International. :6550-6553 Jul, 2022
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Earth
Training
Radio frequency
Solid modeling
Simultaneous localization and mapping
Geology
Predictive models
geophysical excitation
time series prediction
effective angular momentum
neural networks
- Language
- ISSN
- 2153-7003
Effective Angular Momentum (EAM) functions and their predictions are essential geophysical information in describing the changes in earth's orientation. We present the frame-work for the prediction of EAM functions developed at the Chair of Space Geodesy at ETH Zurich. The framework functioning and its underlying methods are explained. In addition, the comparative prediction performance of the methods of the framework with respect to the ones provided by the German Research Centre for Geosciences GFZ is analyzed. The best-performing method is a linear recursive forecasting approach. It manages to improve the EAM predictions between 18 to 64%. The highest improvement could be obtained for the Atmospheric Angular Momentum (AAM) components, with an average improvement of > 50%.