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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. Abstract: The 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. 

Audification has an established history in the field of space science, with events such as “lion roars” and “whistlers” drawing their names from auditory observations. As of 2019, NASA’s CDAWeb repository provides audified versions of observations from spacecraft and ground-based instruments as a standard data product. This approach can be extended further through spatialized audio (auralization) of data from multiple sensors. However, there are not currently standardized tools available for spatially rendering audified multispacecraft observations. Here, we demonstrate an auralization of magnetometer data from NASA’s  Magnetospheric Multiscale (MMS) Mission, produced using open-source tools in python. Each spacecraft’s audified data is played by a virtual sound source with a location matching the physical arrangement of that spacecraft. This is used to generate a binaural rendering optimized for playback over headphones. This approach eliminates the need for specialized tools, improving access for citizen scientists. It lays a foundation for standardized auralizations of distributed instrumentation systems, both for use in space science research and for systematically evaluating the effectiveness of auralization methods, and supports ongoing work with ground-based magnetometers in polar regions. 

This is an audio demo; listen with headphones. The audio begins around the 0:55 mark. In Collins et al 2024, we demonstrated a spatial audification of data from NASA's Magnetospheric Multiscale (MMS) mission produced with open-source tools in Python. In that demo, however, the sound sources for each satellite are placed in a static and representative position. Here, we use OpenSpace to associate each audio stream with its respective spacecraft, so that the audification may be experienced with spatial fidelity on a flexible timescale. This proof-of-concept uses the Open Sound Control protocol to send positional data of the sound sources from OpenSpace to SuperCollider, a method also used in Elmquist et al 2024. 

This dataset adds satellite parameters and dynamic pressure calculations to the event list at https://osf.io/7rjs4/ You can reference this data set as follows: Plaschke, F., Hietala, H., & LaMoury, A. T. (2020, October 27). THEMIS magnetosheath jet data set 2012-2018. Retrieved from osf.io/7rjs4. The description of the identification processes of the THEMIS data sets is published in: Plaschke, F., Hietala, H., and Angelopoulos, V.: Anti-sunward high-speed jets in the subsolar magnetosheath, Ann. Geophys., 31, 1877–1889, https://doi.org/10.5194/angeo-31-1877-2013, 2013. The original THEMIS list should be downloaded locally from the link above before running the python code in the Jupyter notebook. The columns contain:    column 1: jet number    column 2: observing spacecraft (A: THEMIS-A, ..., E: THEMIS-E)    column 3: start of identified jet interval in UT    column 4: time of maximum dynamic pressure ratio in UT    column 5: end of identified jet interval in UT In the final version of the list, the column is indexed by "Max" (col 4 in original), and the other columns are named 'Jet Number', 'Ref Spacecraft', 'Start', and 'End'. From CDAWeb, we add the following columns:     SM_LAT, SM_LON: Latitude and longitude in GSM coordinates    SM_X, SM_Y, SM_Z: Cartesian position in GSM coordinates    GEO_X_1, GEO_Y_1, GEO_Z_1: Cartesian position in geographic coordinates    DIST_FROM_P93_BOW_SHOCK: Distance from the P93 Bow Shock    DIST_FROM_MAGNETOPAUSE: Distance from the RS93 Magnetopause    DIST_FROM_T95_NS: Distance to the Tsyganenko 1995 model Neutral Sheet    L_VALUE: Dipole L value    INVAR_LAT: Dipole Invariant Latitude    MAGNETIC_STRENGTH: Magnetic Field Strength    Dynamic Pressure (nPa): Peak dynamic pressure of jet     Electron density:     Vx, Vy, Vz: Cartesian coordinates of ion velocity. Used in computing dynamic pressure These are pulled from orbit parameters and on-board moment data, using ai.cdas and the second using pyspedas respectively. 

The amateur radio community is a global, highly engaged, and technical community with an intense interest in space weather, its underlying physics, and how it impacts radio communications. The large-scale observational capabilities of distributed instrumentation fielded by amateur radio operators and radio science enthusiasts offers a tremendous opportunity to advance the fields of heliophysics, radio science, and space weather. Well-established amateur radio networks like the RBN, WSPRNet, and PSKReporter already provide rich, ever-growing, long-term data of bottomside ionospheric observations. Up-and-coming purpose-built citizen science networks, and their associated novel instruments, offer opportunities for citizen scientists, professional researchers, and industry to field networks for specific science questions and operational needs. Here, we discuss the scientific and technical capabilities of the global amateur radio community, review methods of collaboration between the amateur radio and professional scientific community, and review recent peer-reviewed studies that have made use of amateur radio data and methods. Finally, we present recommendations submitted to the U.S. National Academy of Science Decadal Survey for Solar and Space Physics (Heliophysics) 2024–2033 for using amateur radio to further advance heliophysics and for fostering deeper collaborations between the professional science and amateur radio communities. Technical recommendations include increasing support for distributed instrumentation fielded by amateur radio operators and citizen scientists, developing novel transmissions of RF signals that can be used in citizen science experiments, developing new amateur radio modes that simultaneously allow for communications and ionospheric sounding, and formally incorporating the amateur radio community and its observational assets into the Space Weather R2O2R framework. Collaborative recommendations include allocating resources for amateur radio citizen science research projects and activities, developing amateur radio research and educational activities in collaboration with leading organizations within the amateur radio community, facilitating communication and collegiality between professional researchers and amateurs, ensuring that proposed projects are of a mutual benefit to both the professional research and amateur radio communities, and working towards diverse, equitable, and inclusive communities. 

Abstract. Ionospheric variability produces measurable effects in Doppler shift of HF (high-frequency, 3–30 MHz) skywave signals. These effects are straightforward to measure with low-cost equipment and are conducive to citizen science campaigns. The low-cost Personal Space Weather Station (PSWS) network is a modular network of community-maintained, open-source receivers, which measure Doppler shift in the precise carrier signals of time standard stations. The primary goal of this paper is to explain the types of measurements this instrument can make and some of its use cases, demonstrating its role as the building block for a large-scale ionospheric and HF propagation measurement network which complements existing professional networks. Here, data from the PSWS network are presented for a period of time spanning late 2019 to early 2022. Software tools for the visualization and analysis of this living dataset are also discussed and provided. These tools are robust to data interruptions and to the addition, removal or modification of stations, allowing both short- and long-term visualization at higher density and faster cadence than other methods. These data may be used to supplement observations made with other geospace instruments in event-based analyses, e.g., traveling ionospheric disturbances and solar flares, and to assess the accuracy of the bottomside estimates of ionospheric models by comparing the oblique paths obtained by ionospheric ray tracers with those obtained by these receivers. The data are archived at https://doi.org/10.5281/zenodo.6622111 (Collins, 2022).