Kamodo’s model-agnostic satellite flythrough: Lowering the utilization barrier for heliophysics model outputs

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Abstract

Heliophysics model outputs are increasingly accessible, but typically are not usable by the majority of the community unless directly collaborating with the relevant model developers. Prohibitive factors include complex file output formats, cryptic metadata, unspecified and often customized coordinate systems, and non-linear coordinate grids. Some pockets of progress exist, giving interfaces to various simulation outputs, but only for a small set of outputs and typically not with open-source, freely available packages. Additionally, the increasing array of tools built upon these sporadic interfaces are typically model-specific. We present Kamodo’s model-agnostic satellite flythrough capabilities as the solution to the utilization barrier for heliophysics model outputs. Developed at the Community Coordinated Modeling Center, these flythrough capabilities are built in Python upon a network of model-agnostic interfaces developed in collaboration with model developers, providing interpolation results the community can trust. Kamodo’s flythrough capabilities present the user with a growing variety of flythrough tools based upon a rapidly expanding library of heliophysics model outputs in several domains, currently including a variety of Ionosphere-Thermosphere-Mesosphere and global magnetosphere model outputs. Each capability is designed to be easily accessible via simplistic model-agnostic syntax, with the entire package freely available in the cloud on Github. Here, we describe the tools developed, include several sample applications for common science questions, demonstrate interoperability with selected packages, and summarize ongoing developments.

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APA

Ringuette, R., De Zeeuw, D., Rastaetter, L., Pembroke, A., Gerland, O., & Garcia-Sage, K. (2022). Kamodo’s model-agnostic satellite flythrough: Lowering the utilization barrier for heliophysics model outputs. Frontiers in Astronomy and Space Sciences, 9. https://doi.org/10.3389/fspas.2022.1005977

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