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Edward Semones

Publications and source records attributed to Edward Semones.

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Simulated Operational Testing of the Prototype Implementation of the SOFIE Model: The 2025 Space Weather Prediction Testbed Exercise

The CLEAR Space Weather Center of Excellence's solar energetic particle (SEP) model, SOlar wind with FIeld lines and Energetic particles (SOFIE), was run and evaluated on-site during the Space Weather Prediction Testbed (SWPT) exercise at the National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) in May 2025. As a physics-based SEP model, SOFIE simulates the acceleration and transport of energetic particles by the coronal mass ejection (CME)-driven shock in the solar corona and inner heliosphere, and has been validated against historical events. However, questions remain regarding whether a physics-based model, traditionally considered computationally expensive, could meet operational needs. The SWPT exercise offered a valuable opportunity to evaluate SOFIE under simulated operational conditions. On-site interactive feedback from SWPC forecasters, Space Radiation Analysis Group (SRAG) console operators, Community Coordinated Modeling Center (CCMC) personnel, and Moon-to-Mars Space Weather Analysis Office (M2M SWAO) analysts led to significant strategic improvements in the model configuration. The simulation grid was optimized by combining a coarser background grid with higher-resolution regions along the CME path and toward Earth, reducing computational cost without compromising accuracy. In this work, we present the simulated operational performance of SOFIE and its capability to predict SEP fluxes significantly faster than real time. During the SWPT exercise, SOFIE completed a 4-day SEP simulation within 5 hours using 1,000 central processing unit cores, although the earliest SEP forecast was obtained a few hours after CME onset. This marks a milestone in demonstrating SOFIE's operational usefulness and robustness to support future human space exploration.

astro-ph.SR

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.

astro-ph.SR