Simultaneous observation of auroral substorm onset in Polar satellite global images and ground-based all-sky images.

Substorm onset has originally been defined as a longitudinally extended sudden auroral brightening (Akasofu initial brightening: AIB) followed a few minutes later by an auroral poleward expansion in ground-based all-sky images (ASIs). In contrast, such clearly marked two-stage development has not been evident in satellite-based global images (GIs). Instead, substorm onsets have been identified as localized sudden brightenings that expand immediately poleward. To resolve these differences, optical substorm onset signatures in GIs and ASIs are compared in this study for a substorm that occurred on December 7, 1999. For this substorm, the Polar satellite ultraviolet global imager was operated with a fixed-filter (170 nm) mode, enabling a higher time resolution (37 s) than usual to resolve the possible two-stage development. These data were compared with 20-s resolution green-line (557.7 nm) ASIs at Muonio in Finland. The ASIs revealed the AIB at 2124:50 UT and the subsequent poleward expansion at 2127:50 UT, whereas the GIs revealed only an onset brightening that started at 2127:49 UT. Thus, the onset in the GIs was delayed relative to the AIB and in fact agreed with the poleward expansion in the ASIs. The fact that the AIB was not evident in the GIs may be attributed to the limited spatial resolution of GIs for thin auroral arc brightenings. The implications of these results for the definition of substorm onset are discussed herein.

Aurora retrieval in all-sky images based on hash vision transformer.

Auroras are bright occurrences when high-energy particles from the magnetosphere and solar wind enter Earth's atmosphere through the magnetic field and collide with atoms in the upper atmosphere. The morphological and temporal characteristics of auroras are essential for studying large-scale magnetospheric processes. While auroras are visible to the naked eye from the ground, scientists use deep learning algorithms to analyze all-sky images to understand this phenomenon better. However, the current algorithms face challenges due to inefficient utilization of global features and neglect the excellent fusion of local and global feature representations extracted from aurora images. Hence, this paper introduces a Hash-Transformer model based on Vision Transformer for aurora retrieval from all-sky images. Experimental results based on real-world data demonstrate that the proposed method effectively improves aurora image retrieval performance. It provides a new avenue to study aurora phenomena and facilitates the development of related fields.