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Qian Zou, Ruiqiang Ding, Jianping Li, Yu-heng Tseng, Zhaolu Hou, Tao Wen, and Kai Ji


This study investigates the connection between the North Pacific Victoria mode (VM) during the boreal spring [February–April (FMA)] and the following boreal winter [January–March (JFM)] rainfall over South China (SC). The VM is defined as the second empirical orthogonal function mode (EOF2) of sea surface temperature (SST) anomalies (SSTAs) in the North Pacific poleward of 20°N. It is found that the boreal spring VM has a significant positive correlation with the following winter rainfall over SC. Analyses indicate that a strong positive VM during spring can induce El Niño during the following winter via an air–sea interaction, resulting in the generation of an anomalous anticyclone over the western North Pacific (WNPAC). The anomalous southwesterlies along the southeast coast of East Asia associated with the WNPAC favor an abundant supply of water vapor and anomalous ascending motion over SC. As a result, winter rainfall over SC increases. A linear regression model based on the VM shows that the VM can act as an effective predictor of winter rainfall over SC about 1 year in advance. It also has a higher prediction skill than ENSO in predicting winter rainfall over SC.

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Zongsheng Zheng, Chenyu Hu, Zhaorong Liu, Jianbo Hao, Qian Hou, and Xiaoyi Jiang


Tropical cyclone, also known as typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damages to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to the disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant pre-processing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on convolutional neural network (CNN). Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud band (SCB) more effectively and reduces over-fitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10,000 multiple sensor satellite cloud images of National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3 and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas and SCBs, which facilitates classify and forecast typhoon intensity.

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Yun Qian, Charles Jackson, Filippo Giorgi, Ben Booth, Qingyun Duan, Chris Forest, Dave Higdon, Z. Jason Hou, and Gabriel Huerta
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