Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact
The classification of the Northeast China Cold Vortex (NCCV) activity paths is an important way to analyze its characteristics in detail. Based on the daily precipitation data of the northeastern China (NEC) region, and the atmospheric circulation field and temperature field data of ERA-Interim for...
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description | The classification of the Northeast China Cold Vortex (NCCV) activity paths is an important way to analyze its characteristics in detail. Based on the daily precipitation data of the northeastern China (NEC) region, and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours, the NCCV processes during the early summer (June) seasons from 1979 to 2018 were objectively identified. Then, the NCCV processes were classified using a machine learning method (
k
-means) according to the characteristic parameters of the activity path information. The rationality of the classification results was verified from two aspects, as follows: (1) the atmospheric circulation configuration of the NCCV on various paths; and (2) its influences on the climate conditions in the NEC. The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin, movement direction, and movement velocity of the NCCV. These included the generation-eastward movement type in the east of the Mongolia Plateau (eastward movement type or type A); generation-southeast longdistance movement type in the upstream of the Lena River (southeast long-distance movement type or type B); generation-eastward less-movement type near Lake Baikal (eastward less-movement type or type C); and the generation-southward less-movement type in eastern Siberia (southward less-movement type or type D). There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths, which indicated that the classification results were reasonable. |
doi_str_mv | 10.1007/s00376-020-0118-3 |
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k
-means) according to the characteristic parameters of the activity path information. The rationality of the classification results was verified from two aspects, as follows: (1) the atmospheric circulation configuration of the NCCV on various paths; and (2) its influences on the climate conditions in the NEC. The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin, movement direction, and movement velocity of the NCCV. These included the generation-eastward movement type in the east of the Mongolia Plateau (eastward movement type or type A); generation-southeast longdistance movement type in the upstream of the Lena River (southeast long-distance movement type or type B); generation-eastward less-movement type near Lake Baikal (eastward less-movement type or type C); and the generation-southward less-movement type in eastern Siberia (southward less-movement type or type D). There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths, which indicated that the classification results were reasonable.</description><identifier>ISSN: 0256-1530</identifier><identifier>EISSN: 1861-9533</identifier><identifier>DOI: 10.1007/s00376-020-0118-3</identifier><language>eng</language><publisher>Heidelberg: Science Press</publisher><subject>Atmospheric circulation ; Atmospheric Sciences ; Classification ; Climate ; Climatic classifications ; Climatic conditions ; Cluster analysis ; Clustering ; Configurations ; Daily precipitation ; Earth and Environmental Science ; Earth Sciences ; Geophysics/Geodesy ; Hydrologic data ; Lakes ; Landslides & mudslides ; Learning algorithms ; Machine learning ; Meteorology ; Original Paper ; Precipitation data ; Summer ; Temperature distribution ; Temperature fields ; Vector quantization ; Vortices</subject><ispartof>Advances in atmospheric sciences, 2021-03, Vol.38 (3), p.400-412</ispartof><rights>Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c350t-de8f1342e60b80a83b600562ad742219b48c2fcc91982d02ff29100dff5194323</citedby><cites>FETCH-LOGICAL-c350t-de8f1342e60b80a83b600562ad742219b48c2fcc91982d02ff29100dff5194323</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/dqkxjz-e/dqkxjz-e.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00376-020-0118-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00376-020-0118-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Fang, Yihe</creatorcontrib><creatorcontrib>Chen, Haishan</creatorcontrib><creatorcontrib>Lin, Yi</creatorcontrib><creatorcontrib>Zhao, Chunyu</creatorcontrib><creatorcontrib>Lin, Yitong</creatorcontrib><creatorcontrib>Zhou, Fang</creatorcontrib><title>Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact</title><title>Advances in atmospheric sciences</title><addtitle>Adv. Atmos. Sci</addtitle><description>The classification of the Northeast China Cold Vortex (NCCV) activity paths is an important way to analyze its characteristics in detail. Based on the daily precipitation data of the northeastern China (NEC) region, and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours, the NCCV processes during the early summer (June) seasons from 1979 to 2018 were objectively identified. Then, the NCCV processes were classified using a machine learning method (
k
-means) according to the characteristic parameters of the activity path information. The rationality of the classification results was verified from two aspects, as follows: (1) the atmospheric circulation configuration of the NCCV on various paths; and (2) its influences on the climate conditions in the NEC. The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin, movement direction, and movement velocity of the NCCV. These included the generation-eastward movement type in the east of the Mongolia Plateau (eastward movement type or type A); generation-southeast longdistance movement type in the upstream of the Lena River (southeast long-distance movement type or type B); generation-eastward less-movement type near Lake Baikal (eastward less-movement type or type C); and the generation-southward less-movement type in eastern Siberia (southward less-movement type or type D). There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths, which indicated that the classification results were reasonable.</description><subject>Atmospheric circulation</subject><subject>Atmospheric Sciences</subject><subject>Classification</subject><subject>Climate</subject><subject>Climatic classifications</subject><subject>Climatic conditions</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Configurations</subject><subject>Daily precipitation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Geophysics/Geodesy</subject><subject>Hydrologic data</subject><subject>Lakes</subject><subject>Landslides & mudslides</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Meteorology</subject><subject>Original Paper</subject><subject>Precipitation data</subject><subject>Summer</subject><subject>Temperature distribution</subject><subject>Temperature fields</subject><subject>Vector quantization</subject><subject>Vortices</subject><issn>0256-1530</issn><issn>1861-9533</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kUFPGzEQhS0EUkPoD-jNEicObsf2ruM9wipQ1KggAb1azq5NnO56E9uhpDf-eR1tJU6cRhp9743mPYS-UPhKAWbfIgCfCQIMCFAqCT9CEyoFJVXJ-TGaACsFoSWHT-g0xnWmKy7pBL3VnY7RWdfo5AaPB4t_DiGtjI4J1yvnNa6HrsW_8tK84ssmuReX9vhep1XEzuO5Dt0eP-z63gR8paNpcbb5QXqjfcR1t4vJBOefsfYtflwZF_LS9ToZfNtvdJPO0InVXTSf_88perqeP9bfyeLu5ra-XJCGl5BIa6SlvGBGwFKClnwpAErBdDsrGKPVspANs01T0UqyFpi1rMrJtNaWtCo441N0Mfr-0d5q_6zWwy74fFG129-v67_KMGAUOIDI7PnIbsKw3ZmY3mFWSFECrYTMFB2pJgwxBmPVJuTPwl5RUIdW1NiKyq2oQyuKZw0bNXFzSMWEd-ePRf8AGkeOdQ</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Fang, Yihe</creator><creator>Chen, Haishan</creator><creator>Lin, Yi</creator><creator>Zhao, Chunyu</creator><creator>Lin, Yitong</creator><creator>Zhou, Fang</creator><general>Science Press</general><general>Springer Nature B.V</general><general>Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, China</general><general>Key Opening Laboratory for Northeast China Cold Vortex Research, China Meteorological Administration, Shenyang 110016, China%Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China%Liaoning Provincial Meteorological Service Center, Shenyang 110016, China%Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, China%Climate Change Research Center, Institute of Atmospheric Physics, and Nansen-Zhu International Research Centre, Chinese Academy of Sciences, Beijing 100029, China</general><general>Regional Climate Center of Shenyang, Liaoning Province Meteorological Administration, Shenyang 110016, China</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20210301</creationdate><title>Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact</title><author>Fang, Yihe ; Chen, Haishan ; Lin, Yi ; Zhao, Chunyu ; Lin, Yitong ; Zhou, Fang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c350t-de8f1342e60b80a83b600562ad742219b48c2fcc91982d02ff29100dff5194323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Atmospheric circulation</topic><topic>Atmospheric Sciences</topic><topic>Classification</topic><topic>Climate</topic><topic>Climatic classifications</topic><topic>Climatic conditions</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Configurations</topic><topic>Daily precipitation</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Geophysics/Geodesy</topic><topic>Hydrologic data</topic><topic>Lakes</topic><topic>Landslides & mudslides</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Meteorology</topic><topic>Original Paper</topic><topic>Precipitation data</topic><topic>Summer</topic><topic>Temperature distribution</topic><topic>Temperature fields</topic><topic>Vector quantization</topic><topic>Vortices</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fang, Yihe</creatorcontrib><creatorcontrib>Chen, Haishan</creatorcontrib><creatorcontrib>Lin, Yi</creatorcontrib><creatorcontrib>Zhao, Chunyu</creatorcontrib><creatorcontrib>Lin, Yitong</creatorcontrib><creatorcontrib>Zhou, Fang</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Advances in atmospheric sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fang, Yihe</au><au>Chen, Haishan</au><au>Lin, Yi</au><au>Zhao, Chunyu</au><au>Lin, Yitong</au><au>Zhou, Fang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact</atitle><jtitle>Advances in atmospheric sciences</jtitle><stitle>Adv. Atmos. Sci</stitle><date>2021-03-01</date><risdate>2021</risdate><volume>38</volume><issue>3</issue><spage>400</spage><epage>412</epage><pages>400-412</pages><issn>0256-1530</issn><eissn>1861-9533</eissn><abstract>The classification of the Northeast China Cold Vortex (NCCV) activity paths is an important way to analyze its characteristics in detail. Based on the daily precipitation data of the northeastern China (NEC) region, and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours, the NCCV processes during the early summer (June) seasons from 1979 to 2018 were objectively identified. Then, the NCCV processes were classified using a machine learning method (
k
-means) according to the characteristic parameters of the activity path information. The rationality of the classification results was verified from two aspects, as follows: (1) the atmospheric circulation configuration of the NCCV on various paths; and (2) its influences on the climate conditions in the NEC. The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin, movement direction, and movement velocity of the NCCV. These included the generation-eastward movement type in the east of the Mongolia Plateau (eastward movement type or type A); generation-southeast longdistance movement type in the upstream of the Lena River (southeast long-distance movement type or type B); generation-eastward less-movement type near Lake Baikal (eastward less-movement type or type C); and the generation-southward less-movement type in eastern Siberia (southward less-movement type or type D). There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths, which indicated that the classification results were reasonable.</abstract><cop>Heidelberg</cop><pub>Science Press</pub><doi>10.1007/s00376-020-0118-3</doi><tpages>13</tpages></addata></record> |
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subjects | Atmospheric circulation Atmospheric Sciences Classification Climate Climatic classifications Climatic conditions Cluster analysis Clustering Configurations Daily precipitation Earth and Environmental Science Earth Sciences Geophysics/Geodesy Hydrologic data Lakes Landslides & mudslides Learning algorithms Machine learning Meteorology Original Paper Precipitation data Summer Temperature distribution Temperature fields Vector quantization Vortices |
title | Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact |
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