<p>In May 2024, multiple X-class solar flares, full-halo coronal mass ejections (CMEs), severe geomagnetic disturbances, and severe ionospheric negative storms were observed, and these space weather events caused some social impacts. In this study, we analyzed the performance of space weather forecasts for these extreme space weather events, focusing on maximum forecast levels; X-class solar flares, geomagnetic disturbances with K ≥ 7, and ionospheric storms with I-scale = I3, using multi verification indices such as proportion correct (accuracy), probability of detection (discrimination), false-alarm ratio (reliability), frequency bias (bias), and equitable threat score (skill). As a result, NICT’s forecasts were evaluated as having a strong discrimination ability with moderate reliability for X-class solar flare, and high accuracy with subject to discrimination ability for I3 ionospheric storms, while for K ≥ 7 geomagnetic disturbances forecast, nearly perfect performance had been achieved by using solar wind simulation (SUSANOO-CME) incorporating multiple earth-directing CMEs. Comparative analyses with forecasts issued by other regional warning centers (RWCs) of the International Space Environment Service (ISES) indicated that RWC Japan showed the highest discrimination capability for X-class solar flares, while RWC USA showed the highest skill with zero false alarms. Perfect skill for K ≥ 7 geomagnetic disturbances were archived by RWC Japan, together with RWC Australia. Although continued efforts to improve forecast performance are still significant subject, we anticipate that the evaluation presented here will support appropriate implementation of effective measures to mitigate space weather risks to modern social infrastructure.</p> Graphical abstract <p></p>

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Evaluation of NICT space weather forecast for extreme events in May 2024

  • Kaori Sakaguchi,
  • Sanae Akiyama,
  • Satoshi Andoh,
  • Yumi Bamba,
  • Kaisei Enoki,
  • Park Inchun,
  • Mamoru Ishii,
  • Hiromitsu Ishibashi,
  • Hidekatsu Jin,
  • Yuki Kubo,
  • Satoshi Morita,
  • Tsutomu Nagatsuma,
  • Aoi Nakamizo,
  • Taku Namekawa,
  • Michi Nishioka,
  • Naoto Nishizuka,
  • Kenichi Otsuji,
  • Septi Perwitasari,
  • Shinji Saito,
  • Daikou Shiota,
  • Tomohiko Sumi,
  • Naoko Takahashi,
  • Chihiro Tao,
  • Takuya Tsugawa

摘要

In May 2024, multiple X-class solar flares, full-halo coronal mass ejections (CMEs), severe geomagnetic disturbances, and severe ionospheric negative storms were observed, and these space weather events caused some social impacts. In this study, we analyzed the performance of space weather forecasts for these extreme space weather events, focusing on maximum forecast levels; X-class solar flares, geomagnetic disturbances with K ≥ 7, and ionospheric storms with I-scale = I3, using multi verification indices such as proportion correct (accuracy), probability of detection (discrimination), false-alarm ratio (reliability), frequency bias (bias), and equitable threat score (skill). As a result, NICT’s forecasts were evaluated as having a strong discrimination ability with moderate reliability for X-class solar flare, and high accuracy with subject to discrimination ability for I3 ionospheric storms, while for K ≥ 7 geomagnetic disturbances forecast, nearly perfect performance had been achieved by using solar wind simulation (SUSANOO-CME) incorporating multiple earth-directing CMEs. Comparative analyses with forecasts issued by other regional warning centers (RWCs) of the International Space Environment Service (ISES) indicated that RWC Japan showed the highest discrimination capability for X-class solar flares, while RWC USA showed the highest skill with zero false alarms. Perfect skill for K ≥ 7 geomagnetic disturbances were archived by RWC Japan, together with RWC Australia. Although continued efforts to improve forecast performance are still significant subject, we anticipate that the evaluation presented here will support appropriate implementation of effective measures to mitigate space weather risks to modern social infrastructure.

Graphical abstract