Detecting of Overshooting Cloud Tops via Himawari-8 Imagery Using Dual Channel Multiscale Deep Network

Abstract

The occurrence of overshooting cloud tops can cause extremely severe weather such as strong winds and heavy rainfalls. The traditional overshooting cloud top (OT) detection methods not only need to choose a reasonable threshold, it is also very hard to make full advantage of the multispectral information of cloud images. These make small-scale OT detection very difficult with poor accuracy of OT boundary determination. In order to utilize the multispectral information of Himawari-8 satellite cloud images, in this article, we propose a method for detecting OT based on the dual channel multiscale deep network (DCMSDN). The brightness temperature of infrared window and the difference of brightness temperature between the infrared window and water vapor window are used as dual channel inputs, respectively. Then, DCMSDN introduces a multiscale prediction module to improve the accuracy of small target detection, which makes the network more suitable for the detection of the OT with small spatial scale. Experimental results indicate that the proposed method provides competitive performance with acceptable computational efficiency. Specifically, for the quantitative indicators of OTs detection, our approach achieves the accuracy of 89.36%, the precision of 95.63%, the recall of 88.90%, and the F1-measure of 91.61% for the test cloud images, which outperforms that of comparative methods.

Keywords

• Deep learning
• dual channel multiscale deep network (DCMSDN)
• Himawari-8
• multiscale prediction fusion
• overshooting cloud tops (OT)