使用深度学习进行热带和热带气旋检测
Tropical and Extratropical Cyclone Detection Using Deep Learning
摘要：
从大量不同的气象数据中提取有价值的信息是一个耗时的过程。机器学习方法可以帮助提高这一过程的速度和准确性。具体来说，使用U-Net结构的深度学习图像分割模型执行速度更快，并且可以识别限制性更强的方法（例如专家手工标记和先验启发式方法）所遗漏的区域。本文讨论了四种不同的国家的最先进的U-Net模型设计用于检测热带和热带气旋感兴趣的区域（ROI）从两个独立的输入源：总可降水量输出的全球预报系统（GFS）模型和水汽辐射图像从地球同步业务环境卫星（GOES）。这些模型被称为IBTrACS-GFS、Heuristic-GFS、IBTrACS-GOES和Heuristic-GOES。所有四个U-Net都是快速信息提取工具，ROI检测准确率从80%到99%不等。此外，还使用Dice和Tversky交集对并集（IoU）度量进行了评估，Dice系数得分范围为0.51至0.76，Tversky系数范围为0.56至0.74。与用于检测相同ROI的可比启发式模型相比，extratrophilic cyclone U-Net模型的执行速度快3倍。U-Nets是专门选择的，因为它们能够检测超出培训标签范围的气旋ROI。这些机器学习模型识别出了启发式模型和手动标记方法在生成实时天气警报时遗漏的更模糊和活跃的ROI，这对公共安全有潜在的直接影响。
英文摘要：
Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine learning methods can help improve both speed and accuracy of this process. Specifically, deep learning image segmentation models using the U-Net structure perform faster and can identify areas missed by more restrictive approaches, such as expert hand-labeling and a priori heuristic methods. This paper discusses four different state-of-the-art U-Net models designed for detection of tropical and extratropical cyclone Regions Of Interest (ROI) from two separate input sources: total precipitable water output from the Global Forecasting System (GFS) model and water vapor radiance images from the Geostationary Operational Environmental Satellite (GOES). These models are referred to as IBTrACS-GFS, Heuristic-GFS, IBTrACS-GOES, and Heuristic-GOES. All four U-Nets are fast information extraction tools and perform with a ROI detection accuracy ranging from 80% to 99%. These are additionally evaluated with the Dice and Tversky Intersection over Union (IoU) metrics, having Dice coefficient scores ranging from 0.51 to 0.76 and Tversky coefficients ranging from 0.56 to 0.74. The extratropical cyclone U-Net model performed 3 times faster than the comparable heuristic model used to detect the same ROI. The U-Nets were specifically selected for their capabilities in detecting cyclone ROI beyond the scope of the training labels. These machine learning models identified more ambiguous and active ROI missed by the heuristic model and hand-labeling methods commonly used in generating real-time weather alerts, having a potentially direct impact on public safety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
