1. ITT: SEP forecasting Mike Marsh

2. Solar radiation storms Solar energetic particles (SEPs)

3. SEP Event Forecasting Eruptive event at the Sun Particle arrival at Earth can be within ~15 mins - days Event Triggered Forecasting CMEs (too long ~hours) X-ray flares (8 mins) Observations Correlations Flare location, magnitude Proton peak flux Empirical Forecast

4. Proton flux These forecasts are largely based on assessment of Near Real Time data from GOES satellite

5. Event Triggered Forecast Models Xray profile Proton profile Important correlation factors: Flares – integrated flux, peak flux, location. CMEs – Velocity, Angular width Important correlation factors: Flares – integrated flux, peak flux, location. CMEs – Velocity, Angular width

6. Future Goal: Enhanced SEP Forecasting Challenge: Optimised Empirical Forecast Methodology. Can image processing/machine learning/computational intelligence/statistics identify relevant parameters/relationships for SEP forecasting? Input data Flares Location, peak flux, integrated flux, x-ray time profile, energy ranges. CMEs Velocity, angular width, acceleration. Active region properties Complexity, magnetic field gradients, history. Your analysis methods? Improved Forecasting

7. AR Complexity and SEP Productivity Challenge: Mid-term forecasting capability Quantitative measures of AR complexity have been found to be indicators of flare productivity. Can quantitative metrics of AR morphology/complexity be identified as predictors of SEP productivity? Quantative morphology Nerural network Fractal dimension Spatial power spectra Multi-scale measures SEP Productivity Forecasting (~4 day forecast)

8. Initial Challenge: How can you quantify complexity/morphology within magnetogram imagery?

9. SEP Topic Reading List Abramenko, V. I. (2005), Relationship between Magnetic Power Spectrum and Flare Productivity in Solar Active Regions, Astroph. J., 629, 1141. M. Dierckxsens, K. Tziotziou, S. Dalla, I. Patsou, M. S. Marsh, N. B. Crosby, O. Malandraki, G. Tsiropoula (2015), Relationship between Solar Energetic Particles and Properties of Flares and CMEs: Statistical Analysis of Solar Cycle 23 Events, Solar Physics, 290, 841. Falconer, D., Barghouty, A., F., Khazanov, I.,Moore, R. (2011), A tool for empirical forecasting of major flares, coronal mass ejections, and solar particle events from a proxy of active-region free magnetic energy, Space Weather, 9, S04003. Georgoulis, M. K., Rust, D. M. (2007), Quantitative Forecasting of Major Solar Flares, Astrophys. J., 661, L109. Ireland, J., Young, C.A., McAteer, R.T.J., et al. (2008), Multiresolution Analysis of Active Region Magnetic Structure and its Correlation with the Mount Wilson Classification and Flaring Activity, Solar Phys., 252, 121. McAteer, R.T.J., Gallagher, P.T. & Conlon, P.A. (2010), Turbulence, complexity, and solar flares, Advances in Space Research, 45, 1067. Ruzmaikin, A., Feynman, J., Jun, I. (2011b), Distribution of extreme solar energetic proton fluxes, J. Atmos. and Sol.-Terr. Phys., 73, 300-307. Sammis, I., Tang, F., Zirin, H. (2000), The Dependence of Large Flare Occurrence on the Magnetic Structure of Sunspots, Astrophys. J., 540, 583.