Daniel da Silva

We investigate the impact of Solar Orbiter's far-side magnetic field observations on coronal holes and solar wind predictions at multiple heliospheric locations during the period of 2024 March 30–May 21. Far-side data, obtained from the Polarimetric and Helioseismic Imager on board Solar Orbiter, were incorporated into the Air Force Data Assimilative Photospheric Flux Transport Helioseismic and Magnetic Imager maps and used as input to the Wang–Sheeley–Arge model (v6.2). Solar wind predictions produced with and without the inclusion of far-side magnetogram information were compared against in situ measurements from Solar Orbiter, ACE, STEREO-A, and Parker Solar Probe. Time intervals where the two simulations differ substantially were identified and such differences were related to changes in coronal hole morphology and magnetic connectivity driven by the assimilation of far-side data. Results highlighted here demonstrate that far-side observations can significantly improve solar wind predictions not only near Solar Orbiter but also at locations up to 165° away in heliographic longitude, as demonstrated at ACE's location. We further show that far-side data can lead to notable changes in the modelled polarity of the coronal magnetic field and the shape of the heliospheric current sheet through magnetic flux redistribution. These findings underscore the importance of incorporating far-side observations into global models and highlight their potential to improve both background solar wind and coronal mass ejection modeling for enhanced space weather forecasting throughout the inner heliosphere.

Magnetic reconnection at the dayside magnetopause is the primary pathway for transferring mass, momentum, and energy from the solar wind into the terrestrial magnetosphere. Previous studies have shown that the spatiotemporal dynamics of dayside magnetic reconnection can be inferred remotely from properties of Earth's cusps, specifically their ion dispersion signatures, as well as structure and location within the magnetosphere. Despite the abundance of in situ cusp observations from multiple space‐based instruments, intermittent sampling spacecraft moving quickly along their trajectories precludes characterization of the cusp's global response to dynamic upstream drivers. To overcome this limitation, we leveraged existing data sets to develop a machine learning‐based model of three‐dimensional ion fluxes in the high‐altitude northern cusp that are modulated by solar‐wind forcing. This model is based on a residual neural network trained on solar wind parameters from NASA's OMNIWeb database, and ion flux measurements from the CIS/HIA instrument aboard ESA's Cluster mission. The model reproduces longitudinal and latitudinal displacements of the cusp under ‐ and ‐dominated interplanetary magnetic field regimes, in close agreement with reconnection theory. To quantify uncertainty and evaluate performance, we use an ensemble of 100 identical networks with randomized training and validation splits, reserving a fixed test set shared by all models. The statistics indicate consistent predictions within the central cusp region. Also, we used Shapley Additive exPlanations to visualize input feature contributions to the model's outputs. The analysis suggests that the magnitude of ion fluxes within the cusp is most strongly associated with , and solar wind parameters.

Numerical models are essential for advancing fundamental research in heliophysics and for operational forecasting of space weather events. Open science can accelerate progress, but applying an open science approach to numerical models requires careful consideration; models are not data analysis software and often demand substantial domain expertise, experience, and computational resources to be used correctly. This commentary distills the recommendations from Corti et al. (2026), https://doi.org/10.22541/essoar.176824639.92354528/v2 and proposes a pathway for advancing heliophysics and space weather modeling through open science. Rather than focusing on open science policies and practices, we recommend concrete actions to make models usable by the broader community (open use), increase confidence and transparency throughout the Research‐to‐Operations‐to‐Research (R2O2R) pipeline (open validation), enable collaborative model improvement (open development), and strengthen partnerships (open collaboration). Together, these recommendations address several of the open science challenges experienced by contributing authors in developing, validating, using, and hosting advanced models, as identified during the 2024 Open Science Workshop held at College Park, USA. The recommendations also highlight the importance of a culture of shared responsibility, adherence to best practices, and proper recognition of prior work. The Heliophysics Open Modeling Environment will serve as a platform for modelers and model users to work together, facilitate community modeling, and increase the scientific return on modeling investment.

We present a community‐wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potential to enhance the quality and pace of scientific discovery, its application to scientific modeling requires more careful consideration regarding open data and open software guidelines, as scientific models differ significantly from data analysis software. We gathered feedback from modeling teams worldwide through a living survey and discussion sessions at the 2024 Open Science Workshop in College Park, USA, and at the 2025 COSPAR ISWAT Working Meeting in Cape Canaveral, USA. We complement these findings with lessons learned from almost 25 years of experience at the Community Coordinated Modeling Center in enabling open use of models. We identify key roadblocks in current open science practices and guidelines and offer recommendations for future progress across four overlapping themes: open use of models and simulation results, open validation, open development, and open collaboration. An essential outcome of the discussion is the need for model developers and model users to speak with a united voice and promote the role of models in future open science efforts. We introduce a new cross‐domain community initiative called the Heliophysics Open Modeling Environment (HOME), which will be integrated as an overarching activity within COSPAR ISWAT. HOME will serve as a platform for modelers and model users to work together, facilitate community modeling, improve the scientific return on modeling investment, and advance understanding, modeling, and forecasting in heliophysics and space weather.

We present a community effort to assess how open science can advance heliophysics and space weather modeling. Open science has the potential to enhance the quality and pace of scientific discovery, but its application to scientific modeling requires more careful consideration with respect to open data and open software guidelines, as complex scientific models are not ordinary software. We gathered feedback from modeling teams worldwide through a living survey and discussion sessions at the Open Science Workshop in College Park, USA, in 2024, and the COSPAR ISWAT Initiative Working Meeting in Cape Canaveral, USA, in 2025. We complement these findings with lessons learned from almost 25 years of experience at the Community Coordinated Modeling Center in enabling open use of models. We identify key roadblocks in current open science practices and guidelines and offer recommendations for future progress. Our findings are organized into four overlapping themes: open use of models and simulation results, open validation, open development, and open collaboration. An essential outcome of the discussion is the need for model developers and users to speak with a united voice and promote the role of models in future open science efforts. We introduce a new cross-domain community initiative called Heliophysics Open Modeling Environment (HOME), which will be integrated as an overarching activity within the COSPAR ISWAT Initiative. HOME will serve as a platform for modelers and model users to work together, facilitate community modeling, improve the scientific return on modeling investment, and advance innovation in heliophysics and space weather.

The solar wind speed at Earth is one of the most important parameters regarding the effects of space weather on society. Thus far, most approaches for predicting the solar wind speed produce a single‐value time series without uncertainty, or utilize ensemble methods which require custom calibration development. In this study, a method is developed that produces calibrated probabilistic forecasts of the solar wind speed using skew normal distributions and a novel extension of analog ensembles. In our extension, the single‐value predictions form a baseline model of the next days are used along with hours of recent observations and single‐value predictions to create a forecasting scenario vector that is compared against a historical database for outcomes. The baseline model used is the combined Air Force Data Assimilative Photospheric Flux Transport‐Wang Sheeley Arge (ADAPT‐WSA) model and the WSA point parcel simulation, but the method is directly applicable to other deterministic models including components such as Enlil or the Heliospheric Upwind Extrapolation with time dependence model (HUXt). The approach works notably well on the benchmark of whether observations fall within the percentile of the time (for between 0 and 100). Falling back on the mean or median of the predicted distribution as a non‐probabilistic prediction yields a direct improvement in root‐mean‐square error (RMSE) over the original WSA point parcel simulation, and is shown to beat ≈1 solar rotation recurrence for 1–5 days ahead forecasts.

The fleet of spacecraft distributed across the inner heliosphere during 2024 May–June provided a unique opportunity to analyze, over a broad range of heliolongitudes, the effects produced by the complex sunspot group associated with Active Region 13664. The intense solar activity from 2024 May 8 to 2024 June 21 produced unusually elevated solar energetic particle (SEP) intensities observed at heliocentric distances ≲1 au by Solar Orbiter, Parker Solar Probe, STEREO-A, and near-Earth spacecraft. This activity resulted in a >40 day period of enhanced ≲10 MeV proton intensities spanning at least ∼170° in longitude. Among these events, the 2024 May 20 SEP event was remarkable for its high intensity and wide spatial extent. It was associated with a fast (≳1500 km s ⁻¹ ) halo coronal mass ejection (CME) and an X16.5 solar flare located ∼171° in longitude from Earth. Near Earth, the event produced a long-lasting ∼100 MeV proton intensity enhancement with energy spectra flattening between 30 and 80 MeV. It also led to the formation of an energetic particle reservoir that persisted for ∼2 weeks and extended across at least ∼170° in longitude. Several mechanisms contributed to the broad longitudinal spread of SEPs and formation of the energetic particle reservoir, including acceleration at an extended CME-driven shock, particle drifts along the heliospheric current sheet, particle reflection and redistribution caused by the merging of preceding CMEs beyond 1 au, and predominantly cross-field particle transport processes.

High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations, leveraging two complementary modules: (1) the Inter-Spectral Windowed Graph Embedding (iSWGE), which explicitly models inter-band relationships by representing spectral channels as graph nodes with learned edge features; and (2) the Windowed Spatial Graph Attention and Convolutional Block Attention (WSGA-C), which combines sparse graph attention with convolutional attention to reduce spatial redundancy and emphasize fine-scale structures. Evaluations on the SDOML dataset across six extreme ultraviolet (EUV) channels show that our approach achieves a 20.15%reduction in Mean Spectral Information Divergence (MSID), up to 1.09% PSNR improvement, and a 1.62% log transformed MS-SSIM gain over strong learned baselines, delivering sharper and spectrally faithful reconstructions at comparable bits-per-pixel rates. The code is publicly available at https://github.com/agyat4/sgraph .