Speaker
Description
This study traces the journey from solar flare initiation to auroral impacts on Earth. I began by analyzing magnetic reconnection in active regions using SDO/HMI and SDO/AIA data (Kazachenko et al., 2022, https://doi.org/10.1007/s11207-022-01987-6) . I then fed flare and CME parameters into ENLIL heliospheric models to forecast shock arrival. These forecasts were validated against solar-wind data from ACE and DSCOVR. Next, I drove global MHD simulations using the Space Weather Modeling Framework (SWMF) (Tóth et al., 2005, https://doi.org/10.1029/2005JA011126) . Simulated output of magnetosphere–ionosphere coupling was compared with auroral power derived from SuperMAG SME indices (Newell & Gjerloev, 2011, https://doi.org/10.1029/2011JA016779) .
Three significant Solar Cycle 25 events were examined. Strong M‑class and X‑class flares paired with rapid CMEs and sustained southward IMF produced Kp ≥ 7 storms and au corora visible down to Utah. The model reproduced timing and intensity within 15 minutes and 10% accuracy.
I then introduced data-driven emulators based on Gaussian processes to enhance forecasting. Forecast lead time improved by six hours, driven by early flare signatures such as hard X-ray bursts and rapid EUV enhancements.
This integrated Sun-to-Earth framework combines solar imagery, heliospheric models, magnetospheric simulations, and ML-based data assimilation. It aligns with ESWW 2025 SWR2’s focus on Solar Cycle 25 impacts. I recommend its adoption for operational aurora forecasting by space-weather centers.
Keywords: solar flares; auroras; space‑weather forecasting; magnetosphere‑ionosphere coupling; data assimilation
References
Kazachenko, M. D., Albelo‑Corchado, M. F., Tamburri, C. A., & Welsch, B. T. (2022). Short‑term variability from SDO/HMI: Insights into flare magnetism. Solar Physics, 297, 59. https://doi.org/10.1007/s11207-022-01987-6
Newell, P. T., & Gjerloev, J. W. (2011). Evaluation of SuperMAG auroral electrojet indices as indicators of substorms and auroral power. Journal of Geophysical Research: Space Physics, 116, A12221. https://doi.org/10.1029/2011JA016779
Tóth, G., Sokolov, I. V., Gombosi, T. I., De Zeeuw, D. L., Hansen, K. C., et al. (2005). Space Weather Modeling Framework: A new tool for the space science community. Journal of Geophysical Research: Space Physics, 110, A12226. https://doi.org/10.1029/2005JA011126
Odstrcil, D. (2003). Modeling 3‑D solar wind structure. Advances in Space Research, 32(4), 497–506. https://doi.org/10.1016/S0273-1177(03)00404-2
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