Interactive and Visual Exploration of Space Weather Model Data

Kurzfassung

Space weather phenomena degrade trans-ionospheric signal propagation, impairing the accuracy, reliability, and availability of Global Navigation Satellite Systems (GNSS)—with consequences ranging from reduced positioning precision to potential security risks for safety-critical applications. Large-scale space weather models offer valuable insights into the underlying dynamics and their impacts, yet their utility is hindered by limited exploration tools and the sheer volume of data. To address these challenges, we develop interactive visualization tools for scalable, analysis-ready, cloud-optimized (ARCO) space weather model datasets. These tools enable users to efficiently explore long-term trends and spatiotemporal patterns without local data storage, accelerating hypothesis generation and workflow integration. Crucially, they provide a practical entry point for researchers to identify, access, and subset relevant data for tailored analyses. Our end-to-end pipeline spans model data processing, enrichment, and cloud-native data storage in object stores to interactive visualization and programmatic access. A web-based application delivers intuitive map-based explo­ration, allowing users to dynamically select parameters, navigate spatial and temporal dimensions, and adjust visualization settings. Integrated space weather indices further enhance interpretability by contextualizing model outputs and supporting the identification subdatasets of interest. Programmatic access via Python APIs ensures seamless integration into computational workflows (e.g., Jupyter notebooks). Collectively, these tools empower the space weather community and related domains to discover, interact with, and leverage large-scale model data more effectively—bridging the gap between data generation and scientific insight.