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...
Providing actionable forecasts of geomagnetic storm occurrence on timescales of several hours is essential for space weather services aimed at protecting critical infrastructure and supporting operational decision-making. We present GeoStormAlert, a machine-learning-based forecasting system that leverages real-time in-situ solar-wind measurements at L1 to predict geomagnetic storm conditions...
Machine‑learning techniques, whether supervised, self‑supervised, or unsupervised, have become indispensable tools in the modelling of space weather in recent years. By tapping into the vast, heterogeneous archives collected over decades, they produce prediction models that are both swift and highly accurate, often rivalling or even surpassing traditional physics‑based approaches, though...
The geomagnetic index Kp has widespread use in space weather due to the apparent simple interpretation and due to the close relation to the upstream solar wind. Clearly, Kp also has limitations for space weather but we will not discuss that here.
From an L1 monitor, Kp can be forecast with high accuracy with a lead time of a couple of hours, under the assumption that high resolution...
The Disturbance Storm Time (Dst) index quantifies geomagnetic storm intensity by measuring global magnetic field variations. In this study, we apply interpretable machine-learning (ML) techniques to derive data-driven models describing the temporal evolution of the Dst index. We use historical data from the NASA OMNIWeb database, including solar wind density, bulk velocity, convective electric...
