A1 Refereed original research article in a scientific journal
Compiling Magnetosheath Statistical Data Sets Under Specific Solar Wind Conditions: Lessons Learnt From the Dayside Kinetic Southward IMF GEM Challenge
Authors: Dimmock AP, Hietala H, Zou Y
Publisher: AMER GEOPHYSICAL UNION
Publication year: 2020
Journal: Earth and Space Science
Journal name in source: EARTH AND SPACE SCIENCE
Journal acronym: EARTH SPACE SCI
Article number: UNSP e2020EA001095
Volume: 7
Issue: 6
First page : 1
Last page: 13
Number of pages: 13
eISSN: 2333-5084
DOI: https://doi.org/10.1029/2020EA001095
Web address : https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EA001095
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/48798341
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.
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