인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Frontal waves, characterized by sharp boundaries of airglow jump accompanied by following undulations, were detected using machine learning techniques, and their variability was examined. Frontal waves are thought to be manifestations of ducted waves called mesospheric bores or “wall” waves (large-amplitude gravity waves). The YOLOv3 machine learning model, short for “You Only Look Once version 3,” was trained to detect frontal wave events in Day/Night Band (DNB) data from the Visible/Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The YOLOv3 detector was trained with DNB images, including manually labeled objects of 756 unique frontal waves. The model achieved 83.19% of average precision (AP) for frontal wave event detection during the testing phase. Utilizing the trained model, 1,150 frontal wave events were identified out of all available 515,187 moonless images from Suomi NPP VIIRS/DNB from January 2012 to June 2023. Over the past eleven years, the monthly occurrence of frontal wave events has gradually decreased from approximately 15 in 2012 to around 5 in 2022. Frontal waves exhibit a high occurrence peak at equatorial latitudes and weaker occurrence peaks at winter mid-latitudes. In these regions, the migrating diurnal and semidiurnal tides exhibit large temperature amplitudes, which could create a favorable environment for ducted waves or mesospheric bores, such as a temperature inversion layer. Frontal waves detected in this study show higher occurrences in regions where conditions favor the formation of ducted waves or mesospheric bores. Graphical Abstract
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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