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Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015) , we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big Data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big Data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the plethora of methods is typically far from perfect, with physical reasons to blame, besides practical shortcomings: imperfect data, data gaps and a lack of multiple, and meaningful, vantage points of solar observations. We briefly discuss these issues, too, that shape our desired short- and long-term objectives for an efficient future predictive capability. A central aim of this article is to trigger meaningful, targeted discussions that will compel the community to adopt standards for performance verification and validation, which could be maintained and enriched by institutions such as NASA’s Community Coordinated Modeling Center (CCMC) and the community-driven COSPAR/ISWAT initiative. 

Abstract We perform ensemble simulations of radiation belt electron acceleration using the quasi‐linear approach during the storm on 9 October 2012, where chorus waves dominated electron acceleration atL = 5.2. Based on a superposed epoch analysis of 11 similar storms when both multi‐MeV electron flux enhancements and chorus wave activities were observed by Van Allen Probes, we use percentiles to sample the normalized input distributions for the four key inputs to estimate their relative perturbations. Using 11 points in each input parameter including chorus wave amplitudeB_w, chorus wave peak frequencyf_m, background magnetic fieldB₀, and electron densityN_e, we ran 11⁴simulations to quantify the impact of uncertainties in the input parameters on the resulting simulated electron acceleration by chorus. By comparing the simulations to observations, our ensemble simulations reveal that inaccuracies in all four input parameters significantly affect the simulated electron acceleration, with the largest simulation errors attributed to the uncertainties inB_w,N_e, andf_m. The simulation can deviate from the observations by four orders of magnitude, while members with largest probability density (smallest perturbations in the input) provide reasonable estimations of output fluxes with log accuracy errors concentrated between ∼−2.0 and 0.5. Quantifying the uncertainties in our study is a prerequisite for the validation of our radiation belt electron model and improvements of accurate electron flux predictions.