Objectives

Objectives of the study group ”AI for EOP Prediction”:

• Investigate the results of the 2nd Earth Orientation Parameter Prediction Comparison Campaign (2nd EOP PCC): The study group aims to thoroughly investigate the results of 2nd EOP PCC, analyzing the accuracy, consistency, and reliability of the obtained parameters. By evaluating the performance of the EOP PCC method, the group aims to identify areas for improvement and propose refinements to
  enhance the quality of EOP predictions.
• Study the impact of geophysical and meteorological parameters on EOP prediction: The group seeks to investigate the influence of various geophysical and meteorological parameters on the prediction of Earth orientation parameters. By analyzing their correlations and dependencies, the study group aims to enhance the understanding of how these parameters affect EOP predictions, enabling more accurate
  and robust forecasting models.
• Explore effective angular momentum prediction: The study group will focus on studying and improving the prediction of effective angular momentum, which plays a critical role in Earth’s orientation dynamics. By developing advanced prediction models and leveraging machine learning techniques, the group aims to enhance the accuracy and reliability of effective angular momentum predictions, contributing to
  improved EOP forecasts.
• Study machine learning methods to derive the physical meaning and dependency structure between EOP and other parameters: The study group aims to investigate machine learning methods specifically designed to extract the physical meaning and underlying dependency structure between EOP and other geophysical and meteorological parameters. By applying these methods, the group seeks to uncover valuable insights and enhance the interpretability of AI-driven models, facilitating a deeper understanding of EOP dynamics.
• Develop hybrid models to improve EOP prediction: The study group aims to
  develop hybrid prediction models that combine traditional methods with AI techniques.
  By integrating the strengths of different approaches, such as data-driven machine learn-
  ing algorithms and physical models, the group seeks to enhance the accuracy, robust-
  ness, and reliability of EOP predictions, providing more comprehensive forecasting
  capabilities.
• Improve EOP theory using AI: By leveraging AI methodologies, the study group
  aims to advance the theoretical understanding of Earth orientation parameters. Through
  the analysis of complex datasets and the application of AI-driven techniques, the group
  seeks to refine and expand EOP theories, enabling a deeper comprehension of the un-
  derlying dynamics and factors influencing Earth’s orientation.
• Conduct sensitivity analysis of EOP prediction models: The study group will
  perform sensitivity analyses to evaluate the stability and robustness of EOP prediction
  models. By systematically assessing the impact of various input parameters and as-
  sumptions, the group aims to identify critical factors that influence the accuracy and
  reliability of predictions, providing insights for model refinement and validation.
• Explore commercial applications, such as satellite orbit determination and real-time applications: The study group aims to investigate the commercial applications of improved EOP predictions, particularly in areas such as satellite orbit determination and real-time applications. By understanding the impact of accurate EOP predictions on these domains, the group seeks to contribute to the development of innovative solutions and technologies that can enhance satellite operations and realtime positioning systems.
• Identify the best prediction model for each Earth Orientation Parameter: The study group aims to evaluate and compare different prediction models for each Earth Orientation Parameter. By conducting comprehensive assessments and benchmarking exercises, the group seeks to identify the most accurate and reliable prediction
  methods for specific parameters, allowing for tailored approaches and optimized EOP predictions.
• Foster knowledge sharing and collaboration: The study group will actively promote knowledge sharing, collaboration, and exchange of ideas among researchers, industry professionals, and policymakers. Through workshops, conferences, and publications, the group aims to facilitate discussions and interactions that  accelerate advancements in EOP prediction, promoting the adoption of AI techniques and fostering innovation in the field.