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UNH Space Weather Center

The growing number of UNH space weather satellites and space weather data has simultaneously increased the need for enhanced data processing and organization, particularly as data volume, velocity, and instrument diversity continue to expand. In previous years, the space weather team focused on processing real-time data and using it to create predictive machine learning models for the DSCOVR satellite, as well as creating interactive visuals of solar wind and geomagnetic information for researchers. ​ However, the addition of its next generation satellite SWFO-L1 demanded a more dynamic data processing and user interaction system. We built off the work of previous groups by optimizing and fine-tuning their predictive models, extending the existing Flask-based interface with new publicly accessible pages documenting the magnetometer and its data, reorganizing project structure to align with modern software engineering best practices, and expanding documentation, including a Read The Docs implementation and pyproject.toml configuration, to improve collaboration, onboarding, and overall developer experience.​ The work completed in this project will allow the Space Weather Center to deliver more accurate, and accessible insights for scientific research and industrial stakeholders. At the same time, the improved system architecture and documentation create a scalable foundation that supports continued innovation as new satellites, instruments, and analytical models are incorporated. ​ ​

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