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Near-Earth solar-wind conditions, including disturbances generated by coronal mass ejections (CMEs), are routinely forecast using three-dimensional, numerical magnetohydrodynamic (MHD) models of the heliosphere. The resulting forecast errors are largely the result of uncertainty in the near-Sun boundary conditions, rather than heliospheric model physics or numerics. Thus ensembles of heliospheric model runs with perturbed initial conditions are used to estimate forecast uncertainty. MHD heliospheric models are relatively cheap in computational terms, requiring tens of minutes to an hour to simulate CME propagation from the Sun to Earth. Thus such ensembles can be run operationally. However, ensemble size is typically limited to 10 1 to 10 2 members, which may be inadequate to sample the relevant high-dimensional parameter space. Here, we describe a simplified solar-wind model that can estimate CME arrival time in approximately 0.01 seconds on a modest desktop computer and thus enables significantly larger ensembles. It is a one-dimensional, incompressible, hydrodynamic model, which has previously been used for the steady-state solar wind, but it is here used in time-dependent form. This approach is shown to adequately emulate the MHD solutions to the same boundary conditions for both steady-state solar wind and CME-like disturbances. We suggest it could serve as a “surrogate” model for the full three-dimensional MHD models. For example, ensembles of 10 5 to 10 6 members can be used to identify regions of parameter space for more detailed investigation by the MHD models. Similarly, the simplicity of the model means it can be rewritten as an adjoint model, enabling variational data assimilation with MHD models without the need to alter their code. The model code is available as an Open Source download in the Python language.

A significant challenge facing heliospheric models is the lack of full Sun observational coverage. The lack of information about the farside photospheric magnetic field necessitates the use of various techniques to approximate the structure and appearance of this field. However, a recently developed technique that uses He ii 304 Å emission observed by the Solar Terrestrial Relations Observatory (STEREO) enables developing a magnetic flux proxy by imaging of active regions on the far side of the Sun. Incorporating information about these active regions on the far side of the Sun may have the potential to drastically improve heliospheric models. In this work, we run multiple heliospheric models with and without farside information obtained from STEREO observations of He ii 304 Å emission and compare the predicted in situ measurements from the models with real in situ data from STEREO and Earth. We find that although there are noticeable quantitative differences between the in situ predictions from the two models, they are dwarfed by the overall disagreement between the heliospheric model and the actual in situ data. Nevertheless, our results indicate that active regions that significantly change the ratio of open-to-closed and open-to-total flux create the biggest change in the predicted in situ measurements.

The launches of Parker Solar Probe (Parker) and Solar Orbiter (SolO) are enabling a new era of solar wind studies that track the solar wind from its origin at the photosphere, through the corona, to multiple vantage points in the inner heliosphere. A key ingredient for these models is the input photospheric magnetic field map that provides the boundary condition for the coronal portion of many heliospheric models. In this paper, we perform steady-state, data-driven magnetohydrodynamic (MHD) simulations of the solar wind during Carrington rotation 2258 with the Grid GAMERA model. We use the ADAPT and AFT flux transport models and quantitatively assess how well each model matches in situ measurements from Parker, SolO, and Earth. We find that both models reproduce the magnetic field components at Parker quantitatively well. At SolO and Earth, the magnetic field is reproduced relatively well, though not as well as at Parker, and the density is reproduced extremely poorly. The velocity is overpredicted at Parker, but not at SolO or Earth, hinting that the Wang–Sheeley–Arge (WSA) relation, fine-tuned for Earth, misses the deceleration of the solar wind near the Sun. We conclude that AFT performs quantitatively similarly to ADAPT in all cases, and that both models are comparable to a purely WSA heliospheric treatment with no MHD component. Finally, we trace field lines from SolO back to an active region outflow that was observed by Hinode/EIS, and which shows evidence of elevated charge state ratios.

The large-scale dynamics of the heliosphere is driven by solar activity and variable solar wind (SW) conditions. In situ SW observations inform time-dependent heliosphere modeling efforts but are only available up until the present day, thus restricting informed predictions to the very near future. We developed and implemented a method to provide long-term forecasting of SW parameters at 1 au. Such forecasting supports modeling efforts of the time-dependent global heliosphere by providing realistic boundary conditions for heliospheric models at the upwind model-domain boundary ahead of time in order to understand the global dynamic processes in the heliosphere such as the evolution of the termination shock (TS), heliosheath, and heliopause. Such forecasting capabilities are needed to better constrain the timing of New Horizons’ TS encounter and predict energetic neutral atom flux variation for IMAP. We analyzed SW direct measurement time series for periodicities at various timescales using Lomb–Scargle periodogram analysis. Based on identified prominent periodicities, we construct quasiperiodic functions for the SW parameters. By extrapolating these functions forward in time, we obtain a prediction of the SW evolution over the next two solar cycles. Our results indicate that the next pulse in the SW dynamic pressure, which controls the global heliosphere, will occur around 2035.

Accurately determining solar wind parameters is crucial for Sun–Earth space research, as they significantly affect spacecraft safety and ground-based power systems. Traditionally, solar wind conditions are derived using coupled coronal and heliospheric models, with the latter initialized by the former’s output at 0.1 au, a computationally intensive and time-consuming process that limits real-time space weather forecasting. In this work, we propose a machine-learning-based method for generating solar wind parameters at 0.1 au. Specifically, we employ a U-Net neural network, trained using the output of the COolfluid COroNal UnsTructured (COCONUT) model as the learning target and Global Oscillation Network Group–Air Force Data Assimilative Photospheric Flux Transport magnetograms as input. The model achieves correlation coefficients of 0.992 for radial velocity, 0.987 for number density, and 0.991 for radial magnetic field on the test set, with derived Alfvén speed and dynamic pressure reaching 0.996 and 0.769, respectively, demonstrating strong capability in reconstructing key solar wind parameters. Moreover, the model effectively captures the temporal evolution of these parameters within a single Carrington rotation. Once trained, the model generates full-surface solar wind predictions at 0.1 au in 7.8 s on a CPU-only device and 0.065 s on a cluster with one GPU and 10 CPU cores, achieving 15× and 1800× speed-ups, respectively, over the COCONUT magnetohydrodynamic simulation, which requires at least 1 hr to obtain a converged steady-state solution and over 2 minutes on 288 CPU cores per prediction.

Magnetic bright points on the solar photosphere mark the footpoints of kilogauss magnetic flux tubes extending toward the corona. Convective buffeting of these tubes is believed to excite magnetohydrodynamic waves, which can propagate to the corona and deposit heat there. Measuring wave excitation via bright point motion can thus constrain coronal and heliospheric models, and this has been done extensively with centroid tracking, which can estimate kink-mode wave excitation. DKIST is the first telescope to provide well-resolved observations of bright points, allowing shape and size measurements to probe the excitation of other wave modes that have been difficult, if not impossible, to study to date. In this work, we demonstrate a method of automatic bright point tracking that robustly identifies the shapes of bright points, and we develop a technique for interpreting measured bright point shape changes as the driving of a range of thin-tube wave modes. We demonstrate these techniques on a MURaM simulation of DKIST-like resolution. These initial results suggest that modes other than the long-studied kink mode could increase the total available energy budget for wave heating by 50%. Pending observational verification as well as modeling of the propagation and dissipation of these additional wave modes, this could represent a significant increase in the potency of wave-turbulence heating models.

Global magnetohydrodynamic (MHD) models play an important role in the infrastructure of space weather forecasting. Validating such models commonly utilizes in situ solar wind measurements made near the Earth’s orbit. The purpose of this study is to test the performance of G3DMHD (a data driven, time-dependent, 3D MHD model of the solar wind) with Parker Solar Probe (PSP) measurements. Since its launch in 2018 August, PSP has traversed the inner heliosphere at different radial distances sunward of the Earth (the closest approach ∼13.3 R ⊙), thus providing a good opportunity to study evolution of the solar wind and to validate heliospheric models of the solar wind. The G3DMHD model simulation is driven by a sequence of maps of the photospheric field extrapolated to the assumed source surface (2.5 R ⊙) using the potential field model from 2018 to 2022, which covers the first 15 PSP orbits. The Pearson correlation coefficient (cc) and the mean absolute scaled error (MASE) are used as the metrics to evaluate the model performance. It is found that the model performs better for both magnetic intensity (cc = 0.75; MASE = 0.60) and the solar wind density (cc = 0.73; MASE = 0.50) than for the solar wind speed (cc = 0.15; MASE = 1.29) and temperature (cc = 0.28; MASE = 1.14). This is due primarily to lack of accurate boundary conditions. The well-known underestimate of the magnetic field in solar minimum years is also present. Assuming that the radial magnetic field becomes uniformly distributed with latitude at or below 18 R ⊙ (the inner boundary of the computation domain), the agreement in the magnetic intensity significantly improves (cc = 0.83; MASE = 0.49).

Accurately reconstructing the solar wind throughout the inner heliosphere is essential for understanding solar–terrestrial interactions and improving space‐weather forecasts. Conventional reconstruction methods rely on photospheric magnetic field observations and coronal models to estimate solar wind conditions near the Sun, typically at 0.1 AU. This introduces substantial uncertainty in the background flow used by heliospheric models through which coronal mass ejections (CMEs) propagate. Here we present a new approach that instead derives the inner‐boundary conditions directly from in situ solar wind observations, typically obtained near 1 AU. These observations are ballistically backmapped to 0.1 AU while accounting for both solar wind acceleration and solar rotation, and then corrected for stream‐interaction effects using a convolutional neural network trained on synthetic model data. The resulting 0.1 AU boundary conditions are used to drive the Heliospheric Upwind eXtropolation with time dependence (HUXt) model. Applied to the highly geoeffective May 2024 CME interval, this method reproduces solar wind conditions at Earth and at Solar Orbiter—on the far side of the Sun—with speed errors reduced by around 50% relative to traditional coronal‐model approaches. Although this represents a post‐event reconstruction rather than an operational forecast, the approach provides a fast, accurate, and magnetogram‐independent means of reconstructing the inner heliosphere, paving the way for improved CME analyses and future forecasting applications.

Solar‐wind forecasting is critical for predicting events which can affect Earth's technological systems. Typically, forecasts combine coronal model outputs with heliospheric models to predict near‐Earth conditions. Ensemble forecasting generates sets of outputs to create probabilistic forecasts which quantify forecast uncertainty, vital for reliable/actionable forecasts. We adapt meteorological methods to create a calibrated solar‐wind ensemble and probabilistic forecast for ambient solar wind, a prerequisite for accurate coronal mass ejection (CME) forecasting. Calibration is achieved by adjusting ensemble inputs/outputs to align the ensemble spread with observed event frequencies. We produce hindcasts in near‐Earth space using coronal‐model output over Solar Cycle 24, as input to Heliospheric Upwind eXtrapolation with time dependence (HUXt) solar‐wind model. Making spatial perturbations to the coronal model output at 0.1 AU, we produce ensembles of inner‐boundary conditions for HUXt, evaluating how forecast accuracy was impacted by the scales of perturbations applied. We found optimal spatial perturbations described by Gaussian distributions with variances of 20° latitude and 10° longitude; these might represent spatial uncertainty within the coronal model. This produced probabilistic forecasts better matching observed frequencies. Calibration improved forecast reliability, reducing the Brier score by 9% and forecast decisiveness increasing AUC ROC score by 2.5%. Improvements were subtle but systematic. Additionally, we explored statistical post‐processing to correct over‐confidence bias, improving forecast actionability. However, this method, applied post‐run, does not affect the solar‐wind state used to propagate CMEs. This work represents the first formal calibration of solar‐wind ensembles, laying groundwork for comprehensive forecasting systems like a calibrated multi‐model ensemble.

Severe space weather has the potential to cause significant socio‐economic impact and it is widely accepted that mitigating this risk requires more comprehensive observations of the Sun and heliosphere, enabling more accurate forecasting of significant events with longer lead‐times. In this context, it is now recognized that observations from the L5 Sun‐Earth Lagrange point (both remote and in situ) would offer considerable improvements in our ability to monitor and forecast space weather. Remote sensing from L5 allows for the observation of solar features earlier than at L1, providing early monitoring of active region development, as well as tracking of interplanetary coronal mass ejections through the inner heliosphere. In situ measurements at L5 characterize the solar wind's geoeffectiveness (particularly stream interaction regions), and can also be ingested into heliospheric models, improving their performance. The Vigil space weather mission is part of the ESA Space Safety Program and will provide a real‐time data stream for space weather services from L5 following its anticipated launch in the early 2030s. The interplanetary magnetic field is a key observational parameter, and here we describe the development of the Vigil magnetometer instrument for operational space weather monitoring at the L5 point. We summarize the baseline instrument capabilities, demonstrating how heritage from science missions has been leveraged to develop a low‐risk, high‐heritage instrument concept.