We have performed a comprehensive redesign of the Energetic Particle Radiation Environment Model (EPREM) to address certain limitations of the original implementation. This new implementation, written in C++, introduces enhancements to address grid resampling artifacts at the inner boundary, as well as time-variable, pitch-anisotropic, and spatially-distributed seed functions to simulate...
Accurate prediction of the ambient solar wind at Earth is a key requirement for space-weather forecasting but is limited by uncertainties in coronal boundary conditions and, to a lesser extent, heliospheric transport. We present a solar wind forecasting approach that uses near-Earth in situ solar wind observations to estimate the inner boundary condition for the Heliospheric Upwind...
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...
EUROMAP (Mikhailov and Perrone, Radio Science, 2014) is an an empirical forecasting model designed to predict foF2 over the European region. The system is based on local prediction models developed for individual European ionospheric stations, enabling continuous monitoring of foF2 across the entire continent.
The model is driven by geomagnetic and solar activity parameters, including the...
RIMAP (Reverse In situ data and MHD APproach) is a data-driven model designed to reconstruct the ambient solar-wind conditions in the solar equatorial plane starting from in situ measurements, coupling analytical backmapping with numerical simulations performed with the PLUTO MHD code. Unlike models primarily driven by remote-sensing observations, RIMAP is built to preserve longitudinal...
The ESA Space HPC is the European Space Agency’s new high performance computing platform designed to accelerate research, development, and innovation in the space sector. With a flexible architecture including three different partitions, the system enables complex simulations, large scale data analysis, and advanced modelling with high speed and precision. It offers scalable computational...
We present a comprehensive overview of Italy’s national capabilities in Space Weather modelling, compiled by the Space Weather Italian Community (SWICo) that consolidates contributions from universities, research institutes, agencies, and private partners across the country. It spans the full Sun–Earth chain, covering modelling efforts from solar activity and flare forecasting to heliospheric...
Spacecraft do not sample Earth’s magnetosphere uniformly. Measurements cluster along orbital tracks and mission-targeted regions, making it hard to build global maps of plasma environments directly from data. In this talk, I will describe a fully data-driven approach for constructing a magnetospheric atlas from a decade of NASA Magnetospheric Multiscale (MMS) observations. We first compress...
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...
Transitioning from fundamental heliophysics research to reliable operational flare forecasting requires overcoming critical methodological bottlenecks. A primary obstacle is the "Big Flare Syndrome", wherein traditional forecasting models relying on extensive magnetic parameters are inherently biased by the macroscopic size of an Active Region (AR), rather than accurately sensing its...
The Earth’s ionosphere affects the propagation of signals from the Global Navigation Satellite Systems (GNSS). The part of the ionosphere above the F2-layer peak, known as the topside ionosphere, contains a major portion of the total electron content and is therefore crucial for both scientific and practical applications. One of the major challenges for modeling the topside ionosphere has been...
Early identification of flare-productive solar active regions is essential for operational space-weather forecasting. We present an integrated deep-learning framework for automated active-region detection, localization, magnetic classification, and short-term flare forecasting, designed for continuous monitoring and near-real-time deployment.
The system combines three complementary...
Accurate and timely forecasts of coronal mass ejection (CME) arrival times at Earth are are essential for operational space weather services aimed at mitigating impacts on spacecraft, ground-based infrastructure, and radiation-sensitive systems. We present two complementary machine-learning-based tools that address both CME arrival-time prediction and the identification of interplanetary CMEs...
In this contribution, we will present two machine learning models that employ measurements from already-operated space instruments analogous to those Vigil will have on board (Majirsky et al., 2025a). The first model predicts the occurrence of geomagnetic storms by combining coronagraph images and in situ solar wind and IMF data (Majirsky et al., 2025b). The second model classifies in situ...
Operational space weather forecasting requires magnetospheric models that balance physical accuracy with computational feasibility. Magnetohydrodynamic (MHD) simulations are affordable in terms of computational cost but miss critical kinetic effects, while hybrid/kinetic models provide accuracy at prohibitive costs.
We intend to pursue a novel approach based on the existing muphy2 framework....
Abstract
Machine learning (ML) methods are increasingly utilized in space weather research; however, their performance is often limited by sparse observations during extreme events and a lack of physical constraints. Physics-based models, on the other hand, rely on empirical parameterizations and simplifying assumptions that can limit their
performance. Bridging these two approaches offers a...
The properties and the spatial distribution of the large-scale structures of the solar corona determine the observed solar wind structure at 1~au. Coronal holes are a major source of fast solar wind, an important geo-effective component, and appear as large dark patches in extreme ultraviolet images. The solar observatories provide images of the solar corona at different wavelengths, enabling...
Accurate specification and forecasting of Earth’s radiation belt electron environment remain critical for understanding radiation belt dynamics and mitigating space weather hazards to spacecraft. The Versatile Electron Radiation Belt (VERB) code has become a widely used physics-based model for simulating the evolution of relativistic electrons through radial diffusion and wave–particle...
A regional three-dimensional (3D) ionospheric model developed by the INGV Upper Atmosphere Physics and Radiopropagation Unit, has been developed for real-time monitoring over the Italian territory. The model combines a climatological background with real-time data ingestion from multiple ionosondes to reconstruct high-fidelity electron density profiles. Based on the Advanced Ionospheric...
S2WARM (St Andrews Space Weather Active Region Monitor) recognises eruptive solar active regions by assimilating magnetogram data into 3D NLFFF simulations and projecting their evolution. It computes first a theoretical and then a specific metrics to issue green, amber, or red alerts, factoring magnetic flux changes and Lorentz force evolution. Tested on a full rotation with 23 cases, S2WARM...
The open-source MPI-AMRVAC software [https://amrvac.org and Keppens et al, A&A 673, A66, 2023] is widely used for numerical plasma-astrophysical research, and has a fair number of offspin codes (BHAC, Gmunu, GR-AMRVAC) that routinely perform up to general relativistic magnetohydrodynamic (MHD) simulations. Its modular design is a key feature of the framework, along with the automated...
We will present our project Solar Cast@CEA that regroups several forecasting tools developed over many years by our team at CEA under CNES, ERC, french funding agency (ANR) and ESA fundings to be able to anticipate the solar activity and its influence of the inner heliosphere and our technological society.
Solar Cast is at present including three tools: Solar Predict, Wind Predict and Flare...
The Space Physics Group at the University of L’Aquila conducts advanced research on Sun–Earth coupling processes within the magnetosphere–ionosphere–thermosphere (MIT) system, with particular attention to phenomena that directly affect technological infrastructures and space-based services.
A core research line focuses on the formation and evolution of high-latitude ionospheric...
I will present the pipelines at the Austrian Space Weather Office for solar wind forecasting, using a combination of empirical-, physics- and data based models for modeling the propagation, automatic detection and flux rope characterization of CMEs (ELEvo(HI), ARCANE, 3DCORE). A particular emphasis is given on the usage of sub-L1 and far upstream data, which has just recently become available...
COolfluid COrona uNstrUcTured (COCONUT) [1-10] is a data-driven physics-based model for plasma simulations implemented within the open source COOLFluiD platform (https://github.com/andrealani/COOLFluiD/wiki). The core C++ solver implements a second-order accurate Finite Volume (FV) discretization for arbitrary unstructured grids, is fully parallel through the Message Passing Interface (MPI)...
The ESA Vigil mission will be the first operational space weather mission positioned at the Sun-Earth L5 Lagrange point. From this unique vantage point, Vigil will continuously observe solar activity and monitor regions near the Sun-Earth line, enhancing both near real-time space weather nowcasting and forecasting, as well as long-term scientific studies. This presentation will provide an...
Monitoring the thermospheric state is a highly relevant problem due to its impact on ionospheric dynamics and satellite drag. A novel approach based on routine ionosonde observations, originally developed for mid-latitudes (Perrone and Mikhailov, 2018), has proven effective in retrieving a self-consistent set of key aeronomic parameters and is now widely used in our analyses.
The method,...
Over the past several decades, there has been a growing recognition of the adverse impacts that space weather can pose to human infrastructure, activity, and health. Consequently, as modern society becomes increasingly reliant on vulnerable technological systems, there is a corresponding demand for accurate and reliable space weather forecasting capabilities. Successful space weather...
