DOI: 10.1016/j.atmosres.2018.08.018
Scopus记录号: 2-s2.0-85052430962
论文题名: A tropical cyclone similarity search algorithm based on deep learning method
作者: Wang Y. ; Han L. ; Lin Y.-J. ; Shen Y. ; Zhang W.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 214 起始页码: 386
结束页码: 398
语种: 英语
英文关键词: Deep learning
; Feature extraction
; Tropical cyclone track forecasting
; Weather circulation
Scopus关键词: Feature extraction
; Hurricanes
; Learning algorithms
; Storms
; Tropics
; Weather forecasting
; Chebyshev distances
; Confidence levels
; Deep belief network (DBN)
; Learning approach
; Search Algorithms
; Similarity search
; Tropical cyclone
; Weather patterns
; Deep learning
; algorithm
; atmospheric circulation
; confidence interval
; learning
; storm track
; tropical cyclone
; weather forecasting
英文摘要: The tropical cyclone (TC) track forecast is still a challenging problem. For operational TC forecasts, it is useful for forecasters to find the similar TC in history and reference its data to improve TC forecasting. Considering the vast number of historical TC cases, it is necessary to design a suitable search algorithm to help forecasters find similar TC cases. A historical TC similarity search algorithm (named as SA_DBN) used deep learning approaches based on 500-hPa weather patterns was proposed in this study. Various weather features were automatically extracted by a deep belief network (DBN) without subjective influences. The Chebyshev distance was used to measure the similarity between two TCs. In order to show that similar-TCs retrieved by SA_DBN are helpful for forecasting, a modified WPCLPR method based on the standard WPCLPR and similar-TC track is designed. The modified WPCLPR improved the forecast result (at 85% confidence level) when the lead time was 54H, 60H or 66H. These results showed that the proposed algorithm could effectively retrieve similar TCs and be helpful to forecasters. © 2018 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108744
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: National Meteorological Center of China Meteorological Administration, Beijing, 100081, China; College of Information Science and Engineering, Ocean University of China, Qingdao, 266101, China; China Meteorological Administration Training Center, Beijing, 100081, China; Department of Computer Science and Technology, Ocean University of China, Qingdao, 266101, China
Recommended Citation:
Wang Y.,Han L.,Lin Y.-J.,et al. A tropical cyclone similarity search algorithm based on deep learning method[J]. Atmospheric Research,2018-01-01,214