标题： 可见光卫星通道自动检测过冲云顶及其特性 显示英文标题

标题： Automatic detection of overshooting tops and their properties from visible satellite channels

摘要： 过冲云顶（OTs）是积雨云强度的关键指标，在气象分析中被广泛使用。 本研究提出了一种利用卷积神经网络自动检测OT和估算OT高度的算法，该算法应用于可见光卫星图像。 这些模型在包含大约10,000个欧洲手动检测案例的广泛OT数据集上进行了训练和验证。 OTs是从MSG地球同步卫星上的SEVIRI仪器的高分辨率可见光（HRV）通道中识别出来的，其高度由图像中它们阴影的长度确定。 虽然传统的OT检测方法主要依赖于热红外通道冷特征的识别，我们的方法从可见光通道提取信息，利用训练数据集提供的OT阴影长度的真实数据。 在早晨和下午时段，当阴影可见时，所提出的模型检测OT的概率达到95%，并以平均误差0.25公里估计其高度。 预计一旦将该模型应用于具有更高空间分辨率的极地和新一代地球同步卫星，其性能将进一步提高。 显示英文摘要

摘要： Overshooting tops (OTs) are critical indicators of convective storm intensity and are widely utilized in meteorological analyses. This study presents an automated algorithm for OT detection and OT height estimation using convolutional neural networks applied to visible satellite imagery. The models are trained and validated on an extensive OT dataset comprising approximately 10,000 manually detected cases over Europe. The OTs were identified from high-resolution visible (HRV) channel of the SEVIRI instrument on board the MSG geostationary satellite, with the heights determined from the length of their shadows in the imagery. While conventional OT detection methods primarily rely on the identification of cold features in thermal infrared channels, our approach extracts information from visible channels, leveraging the ground truth data on OT shadow length provided by the training dataset. In the morning and afternoon hours, when the shadows are visible, the proposed models detect OTs with a probability of detection reaching 95% and estimate their height with an average error of 0.25 km. The performance is expected to further improve once the model is applied to polar and new generation geostationary satellite with increased spatial resolution.