On the Assessment of Daily Equatorial Plasma Bubble Occurrence Modeling and Forecasting

On the Assessment of Daily Equatorial Plasma Bubble Occurrence Modeling and Forecasting
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日常赤道等离子体气泡发生建模与预报的评估

DOI:
10.1029/2020sw002555
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发表时间:
2020
期刊:
影响因子:
3.7
通讯作者:
Caton, R.
Caton, R.
中科院分区:
地球科学1区
文献类型:
--
作者:
Carter, B. A.;Currie, J. L.;Dao, T.;Yizengaw, E.;Retterer, J.;Terkildsen, M.;Groves, K.;Caton, R.

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预测赤道等离子体气泡(EPB)的日变化是一项持续的科学挑战。已经开发了各种预测EPBS的方法,然而,研究界还没有仔细检查用于评估和比较这些预测模型/技术的方法。在这项研究中,利用12个 月的全球定位系统和超高频闪烁观测,覆盖南美洲、大西洋/西非、东南亚和太平洋地区,以评估根据热层电离层电动力学大气环流模式计算的广义瑞利-泰勒(R-T)增长率。探索了各种评估指标,包括使用对技能分数的显著性测试来选择阈值。还研究了这些技能分数对数据集类型(即全球定位系统与超高频)和数据集大小(30、50、60和90 天/事件)的敏感性。结果表明,需要50到90 天才能获得统计上显著的技能分数。还探讨了进行模型-模型比较的方法,包括使用模型“充分性”。然而,研究表明,模型-模型比较的结果必须仔细解释,并且可能在很大程度上依赖于所使用的数据集。它还证明,观测数据集必须显示适当水平的每日EPB变异性,以便评估给定模型/技术的真实强度。此外,还讨论了EPB预测研究在评估指标和未来挑战方面的其他限制和考虑。
Predicting the daily variability of Equatorial Plasma Bubbles (EPBs) is an ongoing scientific challenge. Various methods for predicting EPBs have been developed, however, the research community is yet to scrutinize the methods for evaluating and comparing these prediction models/techniques. In this study, 12 months of co‐located GPS and UHF scintillation observations spanning South America, Atlantic/Western Africa, Southeast Asia, and Pacific sectors are used to evaluate the Generalized Rayleigh‐Taylor (R‐T) growth rates calculated from the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). Various assessment metrics are explored, including the use of significance testing on skill scores for threshold selection. The sensitivity of these skill scores to data set type (i.e., GPS versus UHF) and data set size (30, 50, 60, and 90 days/events) is also investigated. It is shown that between 50 and 90 days is required to achieve a statistically significant skill score. Methods for conducting model‐model comparisons are also explored, including the use of model “sufficiency.” However, it is shown that the results of model‐model comparisons must be carefully interpreted and can be heavily dependent on the data set used. It is also demonstrated that the observation data set must exhibit an appropriate level of daily EPB variability in order to assess the true strength of a given model/technique. Other limitations and considerations on assessment metrics and future challenges for EPB prediction studies are also discussed.
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