Speaker
Description
Solar Energetic Particles (SEPs) and geomagnetic storms are major Space Weather events which require efficient forecasting for ensuring mitigation against their harmful effects for technological systems and humans. We have implemented a machine learning model (Stumpo et al., 2024), based on the Random Forest Regressor algorithm, to forecast SEP events at the Earth by using in situ observations of only energetic electrons. The model can provide a reliable prediction of the >10 MeV proton flux expected at the Earth with an advance of 1 hour (i.e., before an increase of the proton flux is directly measured). For forecasting geomagnetic storms, we have developed a method based on an Artificial Neural Network (ANN) for making a real-time regression of SYM-H index. We adapted the EDDA (Empirical Dst Data Algorithm, Pallocchia et al. 2016) algorithm, using only magnetic field data, to predict the Sym-H index 1 hour ahead every 20 minutes. We have evaluated both models and have obtained a good performance in both cases.
These models are especially suited for operations onboard next generation spacecraft aimed at Space Weather and\or human exploration. For instance, they will be operating onboard the HEliospheric pioNeer for sOlar and interplanetary threats defeNce (HENON) mission (Provinciali et. al., 2024) to provide timely and reliable alerts for potential harmful Space Weather events. In particular, SEPs will be predicted onboard for the first time, offering an unprecedented test for an advanced alert system for future constellations (e.g., SHIELD) or manned missions. Moreover, geomagnetic storms will be predicted with a 10 time improvement in the lead times with respect to current predictions.
| Numerical model | Machine Learning |
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