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Severe convection is a disastrous mesoscale weather system. The early detection of such systems is very important for saving peoples' lives and properties. Previous studies address the issue mainly based on thresholding methods, which are not robust and accurate enough. In this paper, we propose a novel semantic segmentation method (Dugs-UNet) to solve the problem. Our method is based on the well-known U-Net framework. As convective clouds mimic fluids, its detection faces two important challenges. First, the shape and boundary features of clouds need to be carefully exploited. Second, the positive and negative samples for convection detection are very imbalanced. To address the two challenges, our method was carefully developed. Regarding the importance of the shape and boundary features for convective target detection, we introduce a shape stream module to extract these features. Also, a data-dependent upsample operation is adopted in the decoder of U-Net to effectively utilize the features. This is one of our contributions. To address the imbalance issue for convective target detection, the a focal loss function is employed to train our method, which is another contribution. Experimental results of 2018 Fengyun-4A satellite observations in China demonstrate the effectiveness of the proposed method. Compared to conventional thresholding-based methods and deep semantic segmentation algorithms such as SegNet, PSPNet, DeepLav-v3+ and U-Net, the proposed approach performs the best. [ABSTRACT FROM AUTHOR]

Accurate rapidly developing convection (RDC) detection is an essential part of a severe weather warning. A novel algorithm called object track and identification (OTI) is proposed for detecting RDC using infrared image sequences from geostationary meteorology satellite. Convective cells are computed using extended maxima transform-based region growing algorithm. Firstly, a novel area overlap-based object tracking method is proposed to track convective cells in successive images. Secondly, the lowest 25% of overall brightness temperature of the same convective cloud is averaged in order to preserve the extremum information of evolution of cloud. Thirdly, a new identification criterion, which contains three subcriteria, is developed to detect RDC. Contingency table approach applied to various case studies over China shows that the OTI algorithm is efficient and accurate. [ABSTRACT FROM AUTHOR]