Publications

Ejections of magnetized plasma from the Sun, known as coronal mass ejections, can drive major geomagnetic activity if Earth-directed, and are therefore monitored by space weather forecasters. The current focus being the forecast of the arrival time of a coronal mass ejection at Earth and the level of geomagnetic impact. Available instrumentation, data sets and modeling that support a timely assessment of Earth-directed coronal mass ejections are improving, but there remains a gap in the ability of forecasters to predict the occurrence of a coronal mass ejection ahead of time. We assess over 50 years of observational studies of coronal mass ejection source regions, and their evolution toward eruption, in order to provide guidance for those working in space weather forecasting. The aim being to provide an overview of the indicators that a region may have: (a) already produced a coronal mass ejection, that is, not identified in coronagraph images in a timely way, and (b) that a region may be likely to produce a CME in the near future. Checklists are provided as a practical tool for day-to-day forecasting.

A Specialist Discussion Meeting dedicated to establishing and sharing best practices for applied machine learning in space weather forecasting

Nonthermal velocities, derived from spectral line broadening, can provide crucial insights into plasma dynamics before and during solar flares. To systematically study the preflare phase, we constructed a Hinode/Extreme-ultraviolet Imaging Spectrometer flare catalog of 1449 flares from 2011 to 2024. This enabled a large-scale analysis of flare loop footpoint nonthermal velocity evolution across different flare magnitudes (C, M, X-classes). Analyzing Fe viii–Fe xxiv emission lines formed at log(T/K) ∼ 5.7–7.3 with piecewise linear fits in the preflare period, we find that nonthermal velocities consistently increase 4–25 minutes before Geostationary Operational Environmental Satellite (GOES) soft X-ray start in C and M-class flares. Onset timing patterns vary with flare magnitude: smaller flares show temperature-dependent progression, while larger flares exhibit more compressed, near-simultaneous onsets across temperatures. While our limited X-class sample (N = 18) also show onset before GOES, larger statistics are needed to confirm its behavior. M-class flares show a systematic precursor nonthermal velocity onset ∼30–60 minutes before GOES peak. In a subset of M-class flares (2011–2018), coronal-mass-ejection (CME)-associated events show earlier and more uniform precursor onsets (45–74 minutes before peak) than non-CME events, of which only some lines display a precursor, suggesting a strong link between extended preflare nonthermal broadening and successful eruptions. This large scale study establishes preflare nonthermal velocity increase at footpoints as a common precursor observable before any X-ray signature.

Forecasting coronal mass ejections (CMEs) remains a challenge, and no reliable, accurate forecasting method has yet been developed. Knowing that CMEs can occur in association with flares, we compare two approaches: (a) forecasting CMEs by coupling to flare forecasting, and (b) forecasting CMEs independently of flare occurrence. To do this, we train three transformer-based models that use sequences of HMI-SHARP keywords: (a) a flare model that predicts flares of class ≥ M1.0, (b) a flare-CME model that predicts CMEs associated with ≥ M1.0 flares, and (c) an independent CME model that predicts CMEs regardless of flare activity. When evaluated independently, the flare, flare-CME, and CME models achieve True Skill Score, Matthews Correlation Coefficient, and Average Precision (AP) of (0.79, 0.40, 0.46), (0.46, 0.27, 0.22), and (0.15, 0.16, 0.45), respectively. When the flare and flare-CME forecasts are chained and evaluated only during the 24 hr preceding a ≥ M1.0 flare, the coupled model outperforms the stand-alone CME model with ~ 100% Bayesian confidence and a mean ΔMCC ≈ 0.18 (95% Highest Density Interval (HDI) [0.08, 0.27]). However, in terms of AP, the independent CME model outperforms the coupled model with ~ 100% Bayesian confidence and a mean ΔAP ≈ 0.15 (95% HDI [0.09, 0.21]). All metrics vary with the fraction of active regions that produce events, irrespective of class imbalance, so this ratio should be reported alongside the scores. We conclude that the community needs to agree on what constitutes a “best” model, and that further progress may require knowledge of the state of the solar corona.

Identifying the source regions of coronal mass ejections (CMEs) is crucial for understanding their origins and improving space weather forecasting. We present an automated algorithm for matching CMEs detected by the Large Angle Spectrometric Coronagraph with their source active regions, specifically Space Weather HMI Active Region Patches (SHARPs), between 2010 May and 2019 January. Our method uses posteruptive signatures, including flares and coronal dimmings, to associate CMEs with potential source regions. Out of 4190 CMEs, we successfully match 1132, achieving a recall rate of ~57% for frontside events. We find that the algorithm performs better for complex SHARP regions containing multiple NOAA regions and for faster CMEs, consistent with expectations that more energetic events produce stronger eruption signatures. We find that CME–flare association rates increase with flare intensity, aligning with previous studies. While our approach has limitations, such as focusing exclusively on SHARP regions and relying on a limited set of posteruptive signatures, it significantly reduces the time required for CME source identification while providing transparent, reproducible results. We encourage the solar physics community to build upon this work, developing improved automated tools for CME source identification. The resulting catalog of CME–source region associations is made publicly available, offering a valuable resource for statistical studies and machine learning applications in solar physics and space weather forecasting.

We investigate the effect of observing cadence on the precision of radius ratio values obtained from transit light curves by performing uniform Markov chain Monte Carlo fits of 46 exoplanets observed by the Transiting Exoplanet Survey Satellite (TESS) in multiple cadences. We find median improvements of almost 50 per cent when comparing fits to 20 and 120 s cadence light curves to 1800 s cadence light curves, and of 37 per cent when comparing 600 s cadence to 1800 s cadence. Such improvements in radius precision are important, for example, to precisely constrain the properties of the radius valley or to characterize exoplanet atmospheres. We also implement a numerical information analysis to predict the precision of parameter estimates for different observing cadences. We tested this analysis on our sample and found that it reliably predicts the effect of shortening observing cadence with errors in the predicted percentage precision of ≲ 0.5 per cent for most cases. We apply this method to 157 TESS objects of interest that have only been observed with 1800 s cadence to predict the precision improvement that could be obtained by reobservations with shorter cadences and provide the full table of expected improvements. We report the 10 planet candidates that would benefit the most from reobservations at short cadence. Our implementation of the information analysis for the prediction of the precision of exoplanet parameters, Prediction of Exoplanet Precisions using Information in Transit Analysis, is made publicly available.