Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting
Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting
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DOI：
10.3389/fspas.2020.571286
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
D. Millas;M. Innocenti;B. Laperre;J. Raeder;S. Poedts;G. Lapenta
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文献类型：
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作者：
D. Millas;M. Innocenti;B. Laperre;J. Raeder;S. Poedts;G. Lapenta
太阳活动，从背景太阳风到高能日冕物质抛射，是行星际空间和地球空间环境条件的主要驱动因素，称为空间天气。更好地了解太阳与地球之间的联系具有巨大的潜力，可以减轻空间气象的负面影响，带来经济和社会效益。有效的空间天气预报依赖于数据和模型。在本文中，我们讨论了一些最常用的空间天气模型，并提出了合适的空间天气目的的数据收集的位置。我们报告的代表分析（RA）和影响域（DOI）分析的应用程序模拟不同阶段的太阳-地球连接的三个模型：OpenGGCM和Tsyganenko模型，专注于太阳风磁层的相互作用，和PLUTO模型，用于模拟CME在行星际空间的传播。我们的分析是有希望的空间天气的目的有几个原因。首先，我们获得有关观测点（如太阳风监测器）最有用位置的定量信息。例如，我们发现在磁层等离子体片中DOI的绝对值非常低。由于了解这一特定分系统对空间气象至关重要，因此加强对该区域的监测将是最有益的。其次，我们能够更好地描述模型。虽然目前的分析集中在空间，而不是时间的相关性，我们发现，时间无关的模型是数据同化活动比时间相关的模型更有用。第三，我们采取的雄心勃勃的目标，确定最相关的日光层参数建模CME在日光层中的传播，它们的到达时间，以及它们在地球上的地球效应的第一步。
Solar activity, ranging from the background solar wind to energetic coronal mass ejections (CMEs), is the main driver of the conditions in the interplanetary space and in the terrestrial space environment, known as space weather. A better understanding of the Sun-Earth connection carries enormous potential to mitigate negative space weather effects with economic and social benefits. Effective space weather forecasting relies on data and models. In this paper, we discuss some of the most used space weather models, and propose suitable locations for data gathering with space weather purposes. We report on the application of Representer analysis (RA) and Domain of Influence (DOI) analysis to three models simulating different stages of the Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar wind—magnetosphere interaction, and the PLUTO model, used to simulate CME propagation in interplanetary space. Our analysis is promising for space weather purposes for several reasons. First, we obtain quantitative information about the most useful locations of observation points, such as solar wind monitors. For example, we find that the absolute values of the DOI are extremely low in the magnetospheric plasma sheet. Since knowledge of that particular sub-system is crucial for space weather, enhanced monitoring of the region would be most beneficial. Second, we are able to better characterize the models. Although the current analysis focuses on spatial rather than temporal correlations, we find that time-independent models are less useful for Data Assimilation activities than time-dependent models. Third, we take the first steps toward the ambitious goal of identifying the most relevant heliospheric parameters for modeling CME propagation in the heliosphere, their arrival time, and their geoeffectiveness at Earth.