Geospace Environment Modeling 2008–2009 Challenge: Ground magnetic field perturbations
Geospace Environment Modeling 2008–2009 Challenge: Ground magnetic field perturbations
复制标题
地球空间环境建模 2008–2009 挑战：地面磁场扰动
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
A. Chulaki
中科院分区：
文献类型：
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作者：
A. Pulkkinen;M. Kuznetsova;A. Ridley;J. Raeder;A. Vapirev;D. Weimer;R. Weigel;M. Wiltberger;G. Millward;L. Rastätter;M. Hesse;H. Singer;A. Chulaki
获取关于各种空间物理建模方法性能的定量度量知识是空间气象界的核心。性能的量化有助于建模产品的用户更好地理解模型的功能，并选择最适合其特定需求的方法。此外，基于度量的分析对于解决各种建模方法之间的差异以及测量和指导该领域的进展非常重要。在本文中，基于度量的结果地球空间环境建模2008-2009挑战的地面磁场扰动的一部分。由14个不同的模型，包括合奏模型，预测进行了比较，地磁观测记录从12个不同的北方半球的位置。五个不同的指标被用来量化四个风暴事件的模型性能。它示出的模型的排名是强烈依赖于用于评估模型性能的度量的类型。对于所有使用的指标，没有一个模型系统地排名接近或位于顶部。因此，人们不能挑选绝对的“赢家”：最佳模型的选择取决于人们感兴趣的信号的特征。模特的表演也因活动而异。这对于均方根差和基于效用度量的分析尤其明显。此外，分析表明，对于一些模型，增加全球磁流体动力学模型的空间分辨率和包括环电流动态提高模型的能力，以产生更现实的地面磁场波动。
Acquiring quantitative metrics‐based knowledge about the performance of various space physics modeling approaches is central for the space weather community. Quantification of the performance helps the users of the modeling products to better understand the capabilities of the models and to choose the approach that best suits their specific needs. Further, metrics‐based analyses are important for addressing the differences between various modeling approaches and for measuring and guiding the progress in the field. In this paper, the metrics‐based results of the ground magnetic field perturbation part of the Geospace Environment Modeling 2008–2009 Challenge are reported. Predictions made by 14 different models, including an ensemble model, are compared to geomagnetic observatory recordings from 12 different northern hemispheric locations. Five different metrics are used to quantify the model performances for four storm events. It is shown that the ranking of the models is strongly dependent on the type of metric used to evaluate the model performance. None of the models rank near or at the top systematically for all used metrics. Consequently, one cannot pick the absolute “winner”: the choice for the best model depends on the characteristics of the signal one is interested in. Model performances vary also from event to event. This is particularly clear for root‐mean‐square difference and utility metric‐based analyses. Further, analyses indicate that for some of the models, increasing the global magnetohydrodynamic model spatial resolution and the inclusion of the ring current dynamics improve the models' capability to generate more realistic ground magnetic field fluctuations.