Updating Measures of CME Arrival Time Errors

Space Weather Pub Date : 2024-07-01 DOI:10.1029/2024sw003951
C. Kay, E. Palmerio, P. Riley, M. L. Mays, T. Nieves-Chinchilla, M. Romano, Y. Collado-Vega, C. Wiegand, A. Chulaki
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Abstract

Coronal mass ejections (CMEs) drive space weather effects at Earth and the heliosphere. Predicting their arrival is a major part of space weather forecasting. In 2013, the Community Coordinated Modeling Center started collecting predictions from the community, developing an Arrival Time Scoreboard (ATSB). Riley et al. (2018, https://doi.org/10.1029/2018sw001962) analyzed the first 5 years of the ATSB, finding a bias of a few hours and uncertainty of order 15 hr. These metrics have been routinely quoted since 2018, but have not been updated despite continued predictions. We revise analysis of the ATSB using a sample 3.5 times the size of that in the original study. We find generally the same overall metrics, a bias of −2.5 hr, mean absolute error of 13.2 hr, and standard deviation of 17.4 hr, with only a slight improvement comparing between the previously‐used and new sets. The most well‐established, frequently‐submitted model results tend to outperform those from seldomly‐contributed models. These “best” models show a slight improvement over the 11 year span, with more scatter between the models during early times and a convergence toward the same error metrics in recent years. We find little evidence of any correlations between the arrival time errors and any other properties. The one noticeable exception is a tendency for late predictions for short transit times and vice versa. We propose that any model‐driven systematic errors may be washed out by the uncertainties in CME reconstructions in characterization of the background solar wind, and suggest that improving these may be the key to better predictions.
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更新测量集合放射粒子到达时间误差的方法
日冕物质抛射(CMEs)会对地球和日光层产生空间天气影响。预测它们的到来是空间天气预报的一个重要部分。2013 年,社区协调建模中心开始收集来自社区的预测,并开发了 "到达时间记分牌"(ATSB)。Riley 等人(2018 年,https://doi.org/10.1029/2018sw001962)分析了 ATSB 的前 5 年,发现偏差为几小时,不确定性为 15 小时。自 2018 年以来,这些指标一直被例行引用,但尽管预测不断,却一直没有更新。我们修订了对 ATSB 的分析,使用的样本量是原始研究的 3.5 倍。我们发现总体指标大致相同,偏差为-2.5 小时,平均绝对误差为 13.2 小时,标准偏差为 17.4 小时,以前使用的数据集与新数据集相比仅略有改善。最成熟的、经常提交的模型结果往往优于很少提交的模型结果。这些 "最佳 "模型在 11 年的时间跨度中略有改进,早期模型之间的差异较大,近几年则趋向于相同的误差指标。我们几乎没有发现到达时间误差与其他属性之间有任何相关性。一个明显的例外是,短过境时间的预测往往较晚,反之亦然。我们认为,任何由模型驱动的系统误差都可能被 CME 重建中描述背景太阳风的不确定性所冲淡,并建议改进这些不确定性可能是更好预测的关键。
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