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Abstract The temporal variability of magnetopause reconnection is an important aspect of solar wind magnetosphere coupling. Even under stable solar wind driving, reconnection can be triggered, modulated, or suppressed because of magnetic field and plasma conditions near the magnetopause boundary. We analyze a unique event in which a THEMIS satellite crosses the subsolar magnetopause three times within a 5 min interval in the presence of a cold‐ion population on the magnetospheric side of the boundary. During the first crossing, the satellite detects reconnection outflow and a D‐ shaped ion velocity distribution earthward from the boundary, indicating an active reconnection. The signatures disappear during the second crossing when the magnetospheric cold‐ion density increases significantly and reappear during the third crossing when the magnetospheric density drops to a level comparable to that of the first crossing. The solar wind and magnetosheath conditions do not change much during the interval. The magnetospheric population is evidently associated with a plasmaspheric plume with considerable variation in density. According to the theory of mass loading, the presence of such a plume population results in the local Alfvén speed at the second crossing being 40% smaller compared to the first and third crossings. However, the theory itself does not suggest suppression. We discuss possible suppression mechanisms considering the additional effects of the prevailing solar wind and local magnetopause conditions. 

Abstract Magnetopause reconnection is the dominant mechanism for transporting solar wind energy and momentum into the magnetosphere‐ionosphere system. Magnetopause reconnection can occur along X‐lines of variable extent in the direction perpendicular to the reconnection plane. Identifying the spatial extent of X‐lines using satellite observations has critical limitations. However, we can infer the azimuthal extent of the X‐lines by probing the ionospheric signature of reconnection, the antisunward flow channels across the ionospheric Open‐Closed Field Line Boundary (OCB). We study 39 dayside magnetopause reconnection events using conjugate in situ and ionospheric observations to investigate the variability and controlling factors of the spatial extent of reconnection. We use spacecraft data from Time History of Events and Macroscale Interactions during Substorms (THEMIS) to identify in situ reconnection events. The width of the antisunward flow channels across the OCB is measured using the concurrent measurements from Super Dual Auroral Radar Network (SuperDARN). Also, the X‐line lengths are estimated by tracing the magnetic field lines from the ionospheric flow boundaries to the magnetopause. The solar wind driving conditions upstream of the bow shock are studied using solar wind monitors located at the L1 point. Results show that the magnetopause reconnection X‐lines can extend from a few Earth Radii (RE) to at least 22 RE in the GSM‐Y direction. Furthermore, the magnetopause reconnection tends to be spatially limited during high solar wind speed conditions. 

The Super Dual Auroral Radar Network (SuperDARN) is an international network of high frequency coherent scatter radars that are used for monitoring the electrodynamics of the Earth’s upper atmosphere at middle, high, and polar latitudes in both hemispheres. pyDARN is an open-source Python-based library developed specifically for visualizing SuperDARN radar data products. It provides various plotting functions of different types of SuperDARN data, including time series plot, range-time parameter plot, fields of view, full scan, and global convection map plots. In this paper, we review the different types of SuperDARN data products, pyDARN’s development history and goals, the current implementation of pyDARN, and various plotting and analysis functionalities. We also discuss applications of pyDARN, how it can be combined with other existing Python software for scientific analysis, challenges for pyDARN development and future plans. Examples showing how to read, visualize, and interpret different SuperDARN data products using pyDARN are provided as a Jupyter notebook.