Wei Tian, Yuanyuan Chen, Ping Song, Haifeng Xu, Liguang Wu, K. T. C. Lim Kam Sian, Yonghong Zhang, Chunyi Xiang
{"title":"利用双分支时空注意力卷积网络预测北太平洋西部热带气旋的强度","authors":"Wei Tian, Yuanyuan Chen, Ping Song, Haifeng Xu, Liguang Wu, K. T. C. Lim Kam Sian, Yonghong Zhang, Chunyi Xiang","doi":"10.1175/waf-d-23-0191.1","DOIUrl":null,"url":null,"abstract":"\nThis paper proposes a Spatio-temporal Attention Convolutional Network (STACPred) that leverages deep learning techniques to model the spatio-temporal features of tropical cyclones (TC) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a Residual Attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC’s longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a Rolling Mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieve satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3 and 6-hour intervals, respectively.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting the Intensity of Tropical Cyclones over the Western North Pacific Using Dual-Branch Spatio-Temporal Attention Convolutional Network\",\"authors\":\"Wei Tian, Yuanyuan Chen, Ping Song, Haifeng Xu, Liguang Wu, K. T. C. Lim Kam Sian, Yonghong Zhang, Chunyi Xiang\",\"doi\":\"10.1175/waf-d-23-0191.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nThis paper proposes a Spatio-temporal Attention Convolutional Network (STACPred) that leverages deep learning techniques to model the spatio-temporal features of tropical cyclones (TC) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a Residual Attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC’s longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a Rolling Mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieve satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3 and 6-hour intervals, respectively.\",\"PeriodicalId\":49369,\"journal\":{\"name\":\"Weather and Forecasting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Weather and Forecasting\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1175/waf-d-23-0191.1\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Weather and Forecasting","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/waf-d-23-0191.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Predicting the Intensity of Tropical Cyclones over the Western North Pacific Using Dual-Branch Spatio-Temporal Attention Convolutional Network
This paper proposes a Spatio-temporal Attention Convolutional Network (STACPred) that leverages deep learning techniques to model the spatio-temporal features of tropical cyclones (TC) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a Residual Attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC’s longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a Rolling Mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieve satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3 and 6-hour intervals, respectively.
期刊介绍:
Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.