ABSTRACT

Overshooting tops (OTs) play a crucial role in carrying tropospheric water vapor to the lower stratosphere. They are closely related to climate change as well as local severe weather conditions, such as lightning, hail, and air turbulence, which implies the importance of their detection and monitoring. While many studies have proposed threshold-based detection models using the spatial characteristics of OTs, they have shown varied performance depending on the seasonality and study areas. In this study, we propose a pre-trained feature-aggregated convolutional neural network approach for OT detection and monitoring. The proposed approach was evaluated using multi-channel data from Geo-Kompsat-2A Advanced Meteorological Imager (GK2A AMI) over East Asia. The fusion of a visible channel and multi-infrared channels enabled the proposed model to consider both physical and spatial characteristics of OTs. Six schemes were evaluated according to two types of data pre-processing methods and three types of deep learning model architectures. The best-performed scheme yielded a probability of detection (POD) of 92.1%, a false alarm ratio (FAR) of 21.5%, and a critical success index (CSI) of 0.7. The results were significantly improved when compared to those of the existing CNN-based OT detection model (POD increase by 4.8% and FAR decrease by 29.4%).

摘要

顶峰 (OT) 在将对流层水汽输送到平流层下部方面起着至关重要的作用。它们与气候变化以及当地的恶劣天气状况密切相关，如闪电、冰雹和空气湍流，这意味着对其进行检测和监测的重要性。虽然许多研究提出了使用 OT 的空间特征的基于阈值的检测模型，但它们根据季节性和研究区域显示出不同的性能。在这项研究中，我们提出了一种用于 OT 检测和监控的预训练特征聚合卷积神经网络方法。使用来自东亚地区的 Geo-Kompsat-2A 高级气象成像仪 (GK2A AMI) 的多通道数据对所提出的方法进行了评估。可见通道和多红外通道的融合使所提出的模型能够同时考虑 OT 的物理和空间特性。根据两种数据预处理方法和三种深度学习模型架构对六种方案进行了评估。表现最佳的方案产生了 92.1% 的检测概率 (POD)、21.5% 的误报率 (FAR) 和 0.7 的关键成功指数 (CSI)。与现有的基于 CNN 的 OT 检测模型相比，结果有显着改善（POD 增加 4.8%，FAR 减少 29.4%）。误报率 (FAR) 为 21.5%，关键成功指数 (CSI) 为 0.7。与现有的基于 CNN 的 OT 检测模型相比，结果有显着改善（POD 增加 4.8%，FAR 减少 29.4%）。误报率 (FAR) 为 21.5%，关键成功指数 (CSI) 为 0.7。与现有的基于 CNN 的 OT 检测模型相比，结果有显着改善（POD 增加 4.8%，FAR 减少 29.4%）。