Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2018.08.018 |
A tropical cyclone similarity search algorithm based on deep learning method | |
Wang, Yu1; Han, Lei2; Lin, Yin-Jing1; Shen, Yue3; Zhang, Wei4 | |
2018-12-01 | |
发表期刊 | ATMOSPHERIC RESEARCH |
ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2018 |
卷号 | 214页码:386-398 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
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 fmd 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. |
英文关键词 | Tropical cyclone track forecasting Deep learning Weather circulation Feature extraction |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000445990400031 |
WOS关键词 | ARTIFICIAL NEURAL-NETWORKS ; CLOUD CLASSIFICATION ; ENSEMBLE APPROACH ; PART II ; FORECASTS ; INTENSIFICATION ; IDENTIFICATION ; PRECIPITATION ; TRACKING ; ATHENS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/15180 |
专题 | 地球科学 |
作者单位 | 1.China Meteorol Adm, Natl Meteorol Ctr, Beijing 100081, Peoples R China; 2.Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266101, Peoples R China; 3.China Meteorol Adm, Training Ctr, Beijing 100081, Peoples R China; 4.Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266101, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Yu,Han, Lei,Lin, Yin-Jing,et al. A tropical cyclone similarity search algorithm based on deep learning method[J]. ATMOSPHERIC RESEARCH,2018,214:386-398. |
APA | Wang, Yu,Han, Lei,Lin, Yin-Jing,Shen, Yue,&Zhang, Wei.(2018).A tropical cyclone similarity search algorithm based on deep learning method.ATMOSPHERIC RESEARCH,214,386-398. |
MLA | Wang, Yu,et al."A tropical cyclone similarity search algorithm based on deep learning method".ATMOSPHERIC RESEARCH 214(2018):386-398. |
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