Forecasting the arrival time of Earth-directed coronal mass ejections (CMEs) via physics-based simulations is an essential but challenging task in space weather research due to the complexity of the underlying physics and limited remote and in-situ observations of these events. Data assimilation (DA) techniques can assist in constraining free model parameters and reduce the uncertainty in subsequent model predictions. In this study, we show that CME simulations conducted with the Space Weather Modeling Framework (SWMF) can be assimilated with SOHO LASCO white-light (WL) observations and solar wind observations at L1 prior to the CME eruption to improve the prediction of CME arrival time. The L1 observations are used to constrain the model of the solar wind background into which the CME is launched. Average speed of CME shock front over propagation angles are extracted from both synthetic WL images from the Alfv\’en Wave Solar atmosphere Model (AWSoM) and the WL observations. We observe a strong rank correlation between the average WL speed and CME arrival time, with the Spearman’s rank correlation coefficients larger than 0.90 for three events occurring during different phases of the solar cycle. This enables us to develop a Bayesian framework to filter ensemble simulations using WL observations, which is found to reduce the mean absolute error of CME arrival time prediction from about 13.4$\pm$3.8 hours to 5.1$\pm$3.0 hours. The results show the potential of assimilating readily available L1 and WL observations within hours of the CME eruption to construct optimal ensembles of Sun-to-Earth CME simulations.

Accurately predicting the horizontal component of ground magnetic field perturbation (\text{d}$B_{\text{H}}$), a key quantity for calculating the geomagnetically induced currents (GICs), is crucial for assessing the space weather impact of geomagnetic disturbances. The current operational first-principles Michigan Geospace model can predict \text{d}$B_{\text{H}}$ with positive Heidke Skill Scores, but requires significant computational resources to achieve real-time speeds. Existing data-driven methods tend to underpredict \text{d}$B_{\text{H}}$ and lack uncertainty quantification, which is either overlooked or treated as secondary. In this work, we introduce GeoDGP, a novel and efficient data-driven model based on the deep Gaussian process (DGP). GeoDGP provides global probabilistic forecasts of \text{d}$B_{\text{H}}$ with a lead time of at least 1 hour, and at 1-minute time cadence and with arbitrary spatial resolution. The model takes solar wind measurements, the Dst index, and the prediction location in solar magnetic coordinate system as inputs, and is trained on 28 years of data from SuperMAG global magnetometer stations. Additionally, GeoDGP is also trained to predict the north (\text{d}$B_{\text{N}}$) and east (\text{d}$B_{\text{E}}$) components of perturbations. We evaluate GeoDGP’s performance at over 200 stations worldwide during 24 geomagnetic storms, including the Gannon extreme storm of May 2024. Comparisons with the first-principles Michigan Geospace model and the data-driven DAGGER model revealed that GeoDGP significantly outperforms both across multiple performance metrics.

Modeling the impact of space weather events such as coronal mass ejections (CMEs) is crucial to protecting critical infrastructure. The Space Weather Modeling Framework (SWMF) is a state-of-the-art framework that offers full Sun-to-Earth simulations by computing the background solar wind, CME propagation and magnetospheric impact. However, reliable long-term predictions of CME events require uncertainty quantification (UQ) and data assimilation (DA). We take the first steps by performing global sensitivity analysis (GSA) and UQ for background solar wind simulations produced by the Alfvén Wave Solar atmosphere Model (AWSoM) for two Carrington rotations: CR2152 (solar maximum) and CR2208 (solar minimum). We conduct GSA by computing Sobol indices that quantify contributions from model parameter uncertainty to the variance of solar wind speed and density at 1 au, both crucial quantities for CME propagation and strength. Sobol indices also allow us to rank and retain only the most important parameters, which aids in the construction of smaller ensembles for the reduced-dimension parameter space. We present an efficient procedure for computing the Sobol indices using polynomial chaos expansion (PCE) surrogates and space-filling designs. The PCEs further enable inexpensive forward UQ. Overall, we identify three important model parameters: the multiplicative factor applied to the magnetogram, Poynting flux per magnetic field strength constant used at the inner boundary, and the coefficient of the perpendicular correlation length in the turbulent cascade model in AWSoM.