Improved Probabilistic Forecasts of Solar Energetic Particles with MagPy

NASA's strategy for returning to the Moon is part of its broader Moon-to-Mars exploration, of which the Artemis program is a critical component. A crucial aspect of this concerns the safety of the crew and their hardware, of which solar energetic particles (SEPs) play a major role. MagPy (based on MAG4) is a probabilistic forecasting tool designed to predict solar events such as flares, (fast) CMEs, and SEPs using SDO/HMI SHARP magnetogram data. It forecasts event probabilities within 24 hours by analysing magnetic parameters like strong-field neutral line length, magnetic flux areas over 100 Gauss, and magnetic field shear and gradient. MagPy is the Python-based evolution of MAG4 and is now maintained and developed by NASA's Space Radiation Analysis Group (SRAG). It offers operational outputs, including graphical active region representations and a "Threat Gauge" for event probabilities, making it a core tool for predicting solar activity. We propose to refine and substantially improve MagPy's capabilities. During Phase I, we will focus on developing a robust implementation of the MagPy tool, incorporating field line connectivity, investigating other refinements, and demonstrating a successful prototype tool in preparation for Phase II. During Phase II, we will add a number of other refinements, including, but not limited to (1) improved datasets incorporating Solar Orbiter's PHI images off the Sun-Earth line; (2) a flux transport model to capture the evolution of ARs better as they propagate across the solar disk; (3) a complementary approach for calculating the likelihood of a flare/CME; (4) an alternate PIL detection routine; and (4) robust statistical and machine learning (ML) methodologies for both input data selection and model validation and verification. Additionally, we will work with NASA personnel to develop and refine robust metrics and skill scores for forecast verification and validation.

The proposed innovations for MagPy, especially field-line connectivity, additional input data, model components, robust statistical (ML) approaches, and skill-score/metric capabilities, could significantly enhance NASA's space mission safety by improving the accuracy of SEP predictions. These advances would lead to more effective astronaut radiation protection strategies, such as during spacewalks, but, more generally, informing the design of spacecraft shielding, refining risk assessment models for crew health, and aiding in mission planning to avoid hazardous solar events. Such improvements are crucial for supporting NASA's long-duration missions to the Moon, Mars, and beyond, ensuring that astronauts are better protected against the unpredictable nature of space weather. NASA's Space Radiation Analysis Group (SRAG) at Johnson Space Center (JSC) is responsible for monitoring and providing forecasts of radiation levels that astronauts may be subjected to, and the enhancements to MagPy we are proposing would be directly beneficial to their efforts. Additionally, other NASA centres, notably the Community Coordinated Modeling Center (CCMC) at Goddard Space Flight Center (GSFC), use MagPy within their SEP scoreboard to evaluate SEP forecasts. The enhancements proposed for the MagPy tool could have far-reaching benefits beyond NASA, particularly for commercial spaceflight companies, satellite communications businesses, and the energy sector. With more accurate SEP forecasts, satellite operators could significantly improve radiation protection measures, extending the operational lifespans of satellites and reducing the incidence of radiation-induced malfunctions. SpaceX, for example, known for its ambitious satellite constellations and human spaceflight endeavours, would find MagPy's improved SEP forecasts critical for mission planning, safeguarding sensitive electronics onboard their rockets and satellites, optimising launch windows to avoid SEP events, and enhancing astronaut safety for missions to the Moon and, ultimately, Mars. In aviation, more precise SEP predictions would enable safer planning of polar flights, minimising radiation exposure risks for crew and passengers. For example, companies like Boeing and Airbus could incorporate MagPy's data into their flight management systems to reroute planes around poles during high-risk periods. Additionally, an improved understanding of space weather phenomena, facilitated by MagPy's advances, could bolster the resilience of terrestrial power grids and would be broadly valuable for utility companies. This would be instrumental in preparing for and mitigating the impacts of geomagnetic storms, potentially averting widespread electrical outages and enhancing the reliability of critical infrastructure. These enhancements, thus, hold the promise of improving operational safety, reliability, and cost-efficiency across industries that are increasingly dependent on space-based technologies and vulnerable to space weather. Finally, by developing within an open-source environment, we anticipate interest from international government stakeholders, such as the UK's Met Office and the Space Weather Service Mexico (SCIESMEX).

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