The Geospace Environmental Modelling (GEM) community offers a framework for collaborations between modelers, observers, and theoreticians in the form of regular challenges. In many cases, these challenges involve model‐data comparisons to provide wider context to observations or validate model results. To perform meaningful comparisons, a statistical approach is often adopted, which requires the extraction of a large number of measurements from a specific region. However, in complex regions such as the magnetosheath, compiling these data can be difficult. Here, we provide the statistical context of compiling statistical data for the southward IMF GEM challenge initiated by the “Dayside Kinetic Processes in Global Solar Wind‐Magnetosphere Interaction” focus group. It is shown that matching very specific upstream conditions can severely impact the statistical data if limits are imposed on several solar wind parameters. We suggest that future studies that wish to compare simulations and/or single events to statistical data should carefully consider at an early stage the availability of data in context with the upstream criteria. We also demonstrate the importance of how specific IMF conditions are defined, the chosen spacecraft, the region of interest, and how regions are identified automatically. The lessons learnt in this study are of wide context to many future studies as well as GEM challenges. The results also highlight the issue where a global statistical perspective has to be balanced with its relevance to more‐extreme, less‐frequent individual events, which is typically the case in the field of space weather.

中文翻译：

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在特定太阳风条件下编译磁鞘统计数据集：从日侧动力学南向 IMF GEM 挑战中吸取的教训。


地球空间环境建模 (GEM) 社区以定期挑战的形式为建模者、观察者和理论家之间的合作提供了一个框架。在许多情况下，这些挑战涉及模型数据比较，以便为观察提供更广泛的背景或验证模型结果。为了进行有意义的比较，通常采用统计方法，这需要从特定区域提取大量测量值。然而，在磁鞘等复杂区域，编译这些数据可能很困难。在这里，我们提供了由“全球太阳风磁层相互作用的日间动力学过程”焦点小组发起的向南 IMF GEM 挑战编制统计数据的统计背景。结果表明，如果对几个太阳风参数施加限制，匹配非常具体的上游条件可能会严重影响统计数据。我们建议，希望将模拟和/或单个事件与统计数据进行比较的未来研究应在早期阶段仔细考虑与上游标准相关的数据的可用性。我们还展示了如何定义特定 IMF 条件、选择的航天器、感兴趣区域以及如何自动识别区域的重要性。本研究中汲取的经验教训对于未来的许多研究以及 GEM 挑战具有广泛的背景。结果还强调了一个问题，即全球统计视角必须与其与更极端、不太频繁的个别事件的相关性相平衡，这在空间天气领域是典型的情况。