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Mapping Solutions for Geomagnetic Conjugate Observations in the Arctic and Antarctic

Year: 2024
Type: article
Abstract: Access the original poster here: https://agu24.ipostersessions.com/default.aspx?s=56-AC-77-47-70-EA-25-74-40-80-19-49-D4-CA-D6-A7 Link to conference program: https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1538608 This is an interactive poster, which was presented at AGU24 as a Lightning presentation. To view HTML files, download locally and open in browser. ___________________________________________________________________________________ AbstractThe polar regions are uniquely valuable in geospace science, in part because much of the solar wind's energy enters the system in polar regions and their magnetospheric, ionospheric, and atmospheric connections are markedly different from the lower latitudes. Geomagnetic conjugate points in the northern and southern hemispheres – i.e., points linked by Earth's magnetic field, including both points connected by closed magnetic field lines and points in open-field line regions that are in similar magnetic domains – have been shown to alter each other’s environment on the order of minutes. Space weather conditions in Antarctica, therefore, influence and are influenced by the conditions in the northern hemisphere. This has been observed in the formation of auroral structures. However, the magnetic conjugate relationship is not straightforward to visualize with many common mapping tools, which commonly focus on midlatitude-oriented map projections. Related visualization difficulties also arise from the counterintuitive vertical scale of the geospace environment. Here, we present Python-based tools for mapping multiple instrumentation networks, including ground-based instruments, radars and satellites, to observe geospace events such as the polar eclipses of 2021, and discuss approaches to make the data presentation more flexible and intuitive. In particular, we highlight regions of potential interest for future instrument deployments. DisclosuresWe thank Angeline Burrell for her invaluable help in creating and maintaining this Python package according to good coding practice. This work is supported by the NSF Office of Polar Programs (OPP PRF-2218996, OPP-PRF 2317994). Geospatial support for this work provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 2129685.
DOI: 10.5281/zenodo.14574876
Authors: Collins, Kristina, Hartinger, Michael, Salzano, Michelle, Zimmerman, Kelsey
Language: English
Open Access status: green
OpenAlex ID: W6949589761
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