A mask R-CNN model for reidentifying extratropical cyclones based on quasi-supervised thought.

The applications of machine learning/deep learning (ML/DL) methods in meteorology have developed considerably in recent years. Massive amounts of meteorological data are conducive to improving the training effect and model performance of ML/DL, but the establishment of training datasets is often time consuming, especially in the context of supervised learning. In this paper, to identify the two-dimensional (2D) structures of extratropical cyclones in the Northern Hemisphere, a quasi-supervised reidentification method for extratropical cyclones is proposed. This method first uses a traditional automatic cyclone identification method to construct a trainable labeled dataset and then reidentifies extratropical cyclones in a quasi-supervised fashion by using a (pre-trained) Mask region-based convolutional neural network (Mask R-CNN) model. In comparison, the new method increases the number of identified cyclones by 8.29%, effectively supplementing the traditional method. The newly recognized cyclones are mainly shallow or moderately deep subsynoptic-scale cyclones. However, a considerable portion of the new cyclones along the coastlines of the oceans are accompanied by strong winds. In addition, the Mask R-CNN model also shows good performance in identifying the horizontal structures of tropical cyclones. The quasi-supervised concept proposed in this paper may shed some light on accurate target identification in other research fields.