Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images
Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images
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DOI：
10.3390/rs11212555
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发表时间：
2019-11-01
期刊：
影响因子：
5
通讯作者：
Yang, Chaowei
中科院分区：
文献类型：
--
作者：
Liu, Qian;Li, Yun;Yang, Chaowei
降水，特别是对流降水，与水文灾害（如洪水和干旱）密切相关，对农业生产力、社会和环境产生负面影响。为了减轻这些负面影响，实时监测降水状况至关重要。GOES-16卫星上的新型高级基线成像仪（ABI）提供了这样的降水产品，具有更高的时空和光谱分辨率，特别是在白天。本研究提出了一种基于亮度温差（BTDs）和反射率（Ref）的深度神经网络（DNN）方法来对雨云和非雨云进行分类。对流云和层状雨云也可以用相似的光谱参数来表示云的性质特征。用于训练和验证的降水事件来自IMERG V05B数据，涵盖2018年雨季美国东南海岸。将该方法的性能与传统的机器学习方法，包括支持向量机（svm）和随机森林（RF）进行了比较。对于雨区检测，DNN方法的关键成功指数（CSI）为0.71，检测概率（POD）为0.86，优于其他方法。对于对流降水的描绘，DNN模式也表现出较好的效果，CSI为0.58，POD为0.72。该自动云分类系统可用于极端降雨事件检测、实时预报和与降雨有关的灾害决策支持。
Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters.