Scope

The proposed workshop focuses on the integration of data-driven methodologies, high-performance computing (HPC), and physics-based models for atmospheric science applications, with particular emphasis on air quality, climate interactions, and anthropogenic impacts.

In recent years, atmospheric modelling has undergone a paradigm shift driven by the availability of large datasets (satellite observations, sensor networks), advances in machine learning, and the development of scalable computing infrastructures. These developments have enabled the emergence of hybrid approaches that combine numerical modelling, statistical inference, and artificial intelligence, leading to improved predictive capabilities and new scientific insights.

The workshop aims to explore this transition from traditional modelling frameworks to integrated eScience approaches, emphasizing reproducibility, scalability, and interoperability.