Space Weather and Heliophysics Modelling Workshop

Session

Geomagnetic Storms and Indices

8 Apr 2026, 10:00

ESOC Press Centre

29. Space Weather forecasting models for onboard operations

Monica Laurenza

08/04/2026, 10:00

In-person oral presentation

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...

26. Multi-Horizon Operational Forecasting of Geomagnetic Storms Using L1 Solar-Wind Observations

Michele Piana (University of Genova - Department of Mathematics)

08/04/2026, 10:15

In-person oral presentation

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...

13. Data Driven Advances in SERENADE: Larger Datasets, Stable Forecasts

Guillerme Bernoux (ONERA)

08/04/2026, 10:30

In-person oral presentation

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...

31. Days ahead forecast of geomagnetic activity

Peter Wintoft (Swedish Institute of Space Physics)

08/04/2026, 10:45

In-person oral presentation

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...

15. Disturbance Storm Time Index Prediction with Interpretable Machine Learning

Jonah Ekelund (KTH Royal Institute of Technology)

08/04/2026, 11:00

In-person oral presentation

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...