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DOI | 10.1007/s00382-018-4075-9 |
Cyclone-track based seasonal prediction for South Pacific tropical cyclone activity using APCC multi-model ensemble prediction | |
Kim, Ok-Yeon1; Chan, Johnny C. L.2 | |
2018-11-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2018 |
卷号 | 51页码:3209-3229 |
文章类型 | Article |
语种 | 英语 |
国家 | South Korea; Peoples R China |
英文摘要 | This study aims to predict the seasonal TC track density over the South Pacific by combining the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) dynamical prediction system with a statistical model. The hybrid dynamical-statistical model is developed for each of the three clusters that represent major groups of TC best tracks in the South Pacific. The cross validation result from the MME hybrid model demonstrates moderate but statistically significant skills to predict TC numbers across all TC clusters, with correlation coefficients of 0.4 to 0.6 between the hindcasts and observations for 1982/1983 to 2008/2009. The prediction skill in the area east of about 170 degrees E is significantly influenced by strong El Nino, whereas the skill in the southwest Pacific region mainly comes from the linear trend of TC number. The prediction skill of TC track density is particularly high in the region where there is climatological high TC track density around the area 160 degrees E-180 degrees and 20 degrees S. Since this area has a mixed response with respect to ENSO, the prediction skill of TC track density is higher in non-ENSO years compared to that in ENSO years. Even though the cross-validation prediction skill is higher in the area east of about 170 degrees E compared to other areas, this region shows less skill for track density based on the categorical verification due to huge influences by strong El Nino years. While prediction skill of the developed methodology varies across the region, it is important that the model demonstrates skill in the area where TC activity is high. Such a result has an important practical implication-improving the accuracy of seasonal forecast and providing communities at risk with advanced information which could assist with preparedness and disaster risk reduction. |
英文关键词 | Seasonal tropical cyclones Southern Pacific Multimodel ensemble Cyclone track clustering |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000447366100003 |
WOS关键词 | WESTERN NORTH PACIFIC ; CLUSTER-ANALYSIS ; SURFACE-TEMPERATURE ; AUSTRALIAN REGION ; TYPHOON TRACKS ; OSCILLATION ; ENSO ; MODELS ; OCEAN ; SEA |
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/35840 |
专题 | 气候变化 |
作者单位 | 1.APEC Climate Ctr, 12 Centum 7 Ro, Busan 612020, South Korea; 2.City Univ Hong Kong, Sch Energy & Environm, Guy Carpenter Asia Pacific Climate Impact Ctr, Hong Kong, Hong Kong, Peoples R China |
推荐引用方式 GB/T 7714 | Kim, Ok-Yeon,Chan, Johnny C. L.. Cyclone-track based seasonal prediction for South Pacific tropical cyclone activity using APCC multi-model ensemble prediction[J]. CLIMATE DYNAMICS,2018,51:3209-3229. |
APA | Kim, Ok-Yeon,&Chan, Johnny C. L..(2018).Cyclone-track based seasonal prediction for South Pacific tropical cyclone activity using APCC multi-model ensemble prediction.CLIMATE DYNAMICS,51,3209-3229. |
MLA | Kim, Ok-Yeon,et al."Cyclone-track based seasonal prediction for South Pacific tropical cyclone activity using APCC multi-model ensemble prediction".CLIMATE DYNAMICS 51(2018):3209-3229. |
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