Abstract. An ability to accurately detect convective regions is essential for initializing models for short term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high temporal resolution data are mostly available over land and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and -17 provide high spatial and temporal resolution data, but only of cloud top properties. One-minute data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface, or cloud growth that occurs over periods of 30 minutes to an hour. This study represents a proof-of-concept that Artificial Intelligence (AI) is able, when given high spatial and temporal resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 μm) and 14 (11.2 μm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds, and resulted reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

中文翻译：

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应用机器学习方法使用GOES-16 ABI数据检测对流

摘要。准确检测对流区域的能力对于初始化短期降水预报模型至关重要。雷达数据通常用于检测对流，但是提供高时间分辨率数据的雷达通常在陆地上可用，并且数据质量在山区会下降。另一方面，对地静止卫星数据几乎可在任何地方实时获得。当前的对地静止卫星，对地静止环境卫星16（GOES-16）和-17提供高时空分辨率数据，但仅提供云顶特性。然而，即使没有对流系统的垂直信息，一分钟的数据也使我们能够从可见光和红外数据观察对流。现有的使用可见光和红外数据的检测算法会寻找对流云的静态特征，例如超顶或块状云顶表面，或者在30分钟到一个小时的时间内出现云增长。这项研究代表了一种概念证明，即当从GOES-16获得高时空分辨率数据时，人工智能（AI）能够了解对流云的物理特性并使检测过程自动化。提出了具有卷积层的神经网络模型，以从高温分辨率GOES-16数据中识别对流。该模型以通道2（0.65μm）和通道14（11.2μm）的五个时间图像作为输入，并生成对流区域图。为了提供可与雷达产品相媲美的产品，我们接受了多雷达多传感器（MRMS）的培训，这是一种基于雷达的产品，使用相当复杂的方法对降水类型进行分类。来自GOES-16的两个通道，分别与云的光学深度（通道2）和云顶高度（通道14）有关，预计将最能代表对流云的特征：高反射率，块状云顶表面和较低的云层顶温度。该模型正确地了解了对流云的那些特征，并导致相当低的虚警率（FAR）和高检测概率（POD）。但是，FAR和POD会根据阈值而变化，并且需要根据目的选择适当的阈值。预计最能代表对流云的特征：高反射率，块状云顶表面和较低的云层顶温度。该模型正确地了解了对流云的那些特征，并导致相当低的虚警率（FAR）和高检测概率（POD）。但是，FAR和POD会根据阈值而变化，并且需要根据目的选择适当的阈值。预计最能代表对流云的特征：高反射率，块状云顶表面和较低的云层顶温度。该模型正确地了解了对流云的那些特征，并导致相当低的虚警率（FAR）和高检测概率（POD）。但是，FAR和POD会根据阈值而变化，并且需要根据目的选择适当的阈值。