A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites

Yuhang Jiang, Wei Xin Cheng, Feng Gao, Shaoqing Zhang, Shudong Wang, Chang Liu, Juanjuan Liu

Remote Sensing · 11 May 2022 · Institute of Atmospheric Physics, Chinese Academy of Sciences, China Meteorological Administration (CMA)

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In one sentence. U-Net based CLP-CNN classifies cloud types from FY-4A L1 data over China in 0.9 s per 800x800 scene, matching Himawari-8 products 76.8% of the time.

Abstract

The study of cloud types is critical for understanding atmospheric motions and climate predictions; for example, accurately classified cloud products help improve meteorological predicting accuracies. However, the current satellite cloud classification methods generally analyze the threshold change in a single pixel and do not consider the relationship between the surrounding pixels. The classification development relies heavily on human recourses and does not fully utilize the data-driven advantages of computer models. Here, a new intelligent cloud classification method based on the U-Net network (CLP-CNN) is developed to obtain more accurate, higher frequency, and larger coverage cloud classification products. The experimental results show that the CLP-CNN network can complete a cloud classification task of 800 × 800 pixels in 0.9 s. The classification area covers most of China, and the classification task only needs to use the original L1-level data, which can meet the requirements of a real-time operation. With the Himawari-8 CLTYPE product and the CloudSat 2B-CLDCLASS product as the test comparison target, the CLP-CNN network results matched the Himawari-8 product highly by 76.8%. The probability of detection (POD) was greater than 0.709 for clear skies, deep-convection, and Cirrus–Stratus-type clouds. The probability of detection (POD) and accuracy are improved compared with other deep learning methods.

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