Pete Riley

Reza Mansouri, Dustin Kempton, Pete Riley et al.

The solar wind, a continuous stream of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Variations such as high-speed streams and coronal mass ejections can disrupt satellites, power grids, and communications, making accurate modeling essential for space weather forecasting. While 3D magnetohydrodynamic (MHD) models are used to simulate and investigate these variations in the solar wind, they tend to be computationally expensive, limiting their usefulness in investigating the impacts of boundary condition uncertainty. In this work, we develop a surrogate for steady state solar wind modeling, using a Spherical Fourier Neural Operator (SFNO). We compare our model to a previously developed numerical surrogate for this task called HUX, and we show that the SFNO achieves comparable or better performance across several metrics. Though HUX retains advantages in physical smoothness, this underscores the need for improved evaluation criteria rather than a flaw in SFNO. As a flexible and trainable approach, SFNO enables efficient real-time forecasting and can improve with more data. The source code and more visual results are available at https://github.com/rezmansouri/solarwind-sfno-velocity.

Reza Mansouri, Dustin Kempton, Pete Riley et al.

The solar wind, a continuous outflow of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is critical for space weather forecasting, but traditional three-dimensional magnetohydrodynamic (MHD) models are computationally expensive, limiting rapid exploration of boundary condition uncertainties. We introduce the first autoregressive machine learning surrogate for steady-state solar wind radial velocity using the Spherical Fourier Neural Operator (SFNO). By predicting a limited radial range and iteratively propagating the solution outward, the model improves accuracy in distant regions compared to a single-step approach. Compared with the numerical HUX surrogate, SFNO demonstrates superior or comparable performance while providing a flexible, trainable, and data-driven alternative, establishing a novel methodology for high-fidelity solar wind modeling. The source code and additional visual results are available at https://github.com/rezmansouri/solarwind-sfno-velocity-autoregressive.

Matthew Lang, Jake Witherington, Harriet Turner et al.

Data Assimilation (DA) has enabled huge improvements in the skill of terrestrial operational weather forecasting. In this study, we use a variational DA scheme with a computationally efficient solar wind model and in situ observations from STEREO-A, STEREO-B and ACE. This scheme enables solar-wind observations far from the Sun, such as at 1 AU, to update and improve the inner boundary conditions of the solar wind model (at 30 solar radii). In this way, observational information can be used to improve estimates of the near-Earth solar wind, even when the observations are not directly downstream of the Earth. This allows improved initial conditions of the solar wind to be passed into forecasting models. To this effect, we employ the HUXt solar wind model to produce 27-day forecasts of the solar wind during the operational lifetime of STEREO-B (01 November 2007 - 30 September 2014). In near-Earth space, we compare the accuracy of these DA forecasts with both non-DA forecasts and simple corotation of STEREO-B observations. We find that 27-day root mean-square error (RMSE) for STEREO-B corotation and DA forecasts are comparable and both are significantly lower than non-DA forecasts. However, the DA forecast is shown to improve solar wind forecasts when STEREO-B's latitude is offset from Earth, which is an issue for corotation forecasts. And the DA scheme enables the representation of the solar wind in the whole model domain between the Sun and the Earth to be improved, which will enable improved forecasting of CME arrival time and speed.