机器学习检测和VIIRS昼夜频段观察到的中大层正面波的变化
Yuta Hozumi1,2,3, Jia Yue2,3, Seraj Al Mahmud Mostafa4
1Department of Physics, Utah State University, Logan, UT US.
概括
机器学习在卫星空气发光数据中识别了额头波,被认为是中层层孔. 它们的发生在过去11年中一直在减少,在赤道和中度附近的峰值.
科学领域:
- 大气物理学 大气物理学
- 空间物理 空间物理
- 机器学习应用 机器学习应用
背景情况:
- 额头波是大气现象,其特点是有尖的空气发光边界和波浪.
- 假设它们是中层层孔或导管引力波的表现.
- 了解它们的发生和变异性对于大气动力学研究至关重要.
研究的目的:
- 使用机器学习检测和分析正面波的变化.
- 调查正面波事件的空间和时间分布.
- 探索正面波发生与大气条件之间的潜在相关性.
主要方法:
- 使用YOLOv3 (你只看一次版本3) 机器学习模型进行对象检测.
- 在可见/红外成像辐射仪套件 (VIIRS) 昼夜带 (DNB) 卫星图像上训练了YOLOv3模型.
- 分析了从2012年1月到2023年6月的515,187张无月DNB图像,以确定正面波事件.
主要成果:
- 成功检测到1,150个正面波事件,平均精度为83.19%.
- 从2012年到2022年,观察到每月的正面波事件的下降趋势.
- 确定了赤道度的峰值和冬季中度的次要峰值.
结论:
- 这项研究证明了机器学习在识别大气现象,如正面波等方面的有效性.
- 前线波活动显示长期下降和特定的度偏好.
- 发生的模式表明与有利于管道波的大气条件有联系,例如潮驱动的温度逆转.
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