Prediction of solar energetic events impacting space weather conditions

Manolis K. Georgoulis, Stephanie L. Yardley, Jordan A. Guerra, Sophie A. Murray, Azim Ahmadzadeh, Anastasios Anastasiadis, Rafal Angryk, Berkay Aydin, Dipankar Banerjee, Graham Barnes, Alessandro Bemporad, Federico Benvenuto, D. Shaun Bloomfield, Monica Bobra, Cristina Campi, Enrico Camporeale, Craig E. DeForest, A. Gordon Emslie, David Falconer, Li FengWeiqun Gan, Lucie M. Green, Sabrina Guastavino, Mike Hapgood, Dustin Kempton, Irina Kitiashvili, Ioannis Kontogiannis, Marianna B. Korsos, K. D. Leka, Paolo Massa, Anna Maria Massone, Dibyendu Nandy, Alexander Nindos, Athanasios Papaioannou, Sung Hong Park, Spiros Patsourakos, Michele Piana, Nour E. Rawafi, Viacheslav M. Sadykov, Shin Toriumi, Angelos Vourlidas, Haimin Wang, Jason T. Jason, Kathryn Whitman, Yihua Yan, Andrei N. Zhukov

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalAdvances in Space Research
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Astronomy and Astrophysics
  • Geophysics
  • Atmospheric Science
  • Space and Planetary Science
  • General Earth and Planetary Sciences

Keywords

  • Forecasting
  • Future missions
  • International space weather action teams
  • Methods – machine learning
  • Methods – statistical
  • Solar flares and eruptions

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