Prediction Experiment for the South China Sea Summer Monsoon Strength by Physical-statistic Integrated Model
The South China Sea summer monsoon(SCSSM) is a tropical system that plays a key role during the flood season of South China. However, the prediction of the SCSSM strength is difficult by no matter dynamic or statistic methods. Statistic methods are used in practice rather than dynamic model, but emp...
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| description | The South China Sea summer monsoon(SCSSM) is a tropical system that plays a key role during the flood season of South China. However, the prediction of the SCSSM strength is difficult by no matter dynamic or statistic methods. Statistic methods are used in practice rather than dynamic model, but empirical-statistic models always have good hindcasting results during the period of building model, while the forecasting skills decrease evidently in practice. Physical-statistic methods have relatively stable predictive skill when the persistence of physical processes is taken into account. Therefore, an integrated technique is introduced based on associated physical processes to establish a predictive model for SCSSM. It is well known that the rainfall of SCSSM has multi-scale climate variability, for example, quasi-biennial and quasi-quadrennial time scale, which are mainly related to TBO(Tropospheric Biennial Oscillation) and ENSO(El Nino-Southern Oscillation), respectively. Based on the corresponding climatic f |
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However, the prediction of the SCSSM strength is difficult by no matter dynamic or statistic methods. Statistic methods are used in practice rather than dynamic model, but empirical-statistic models always have good hindcasting results during the period of building model, while the forecasting skills decrease evidently in practice. Physical-statistic methods have relatively stable predictive skill when the persistence of physical processes is taken into account. Therefore, an integrated technique is introduced based on associated physical processes to establish a predictive model for SCSSM. It is well known that the rainfall of SCSSM has multi-scale climate variability, for example, quasi-biennial and quasi-quadrennial time scale, which are mainly related to TBO(Tropospheric Biennial Oscillation) and ENSO(El Nino-Southern Oscillation), respectively. Based on the corresponding climatic f</description><identifier>ISSN: 1001-7313</identifier><language>chi</language><publisher>Beijing: China Meteorological Press</publisher><subject>Annual variations ; Anomalies ; Atmospheric precipitations ; Biennial ; Climate variability ; Correlation coefficient ; Correlation coefficients ; Data ; Dynamic models ; El Nino ; El Nino phenomena ; El Nino-Southern Oscillation event ; Flood predictions ; Hindcasting ; Interannual variations ; Monsoons ; Precipitation ; Precipitation anomalies ; Quasi-biennial oscillation ; Rain ; Rainfall ; Rainfall distribution ; Rainfall forecasting ; Sea surface ; Sea surface temperature ; Sea surface temperature anomalies ; Southern Oscillation ; Statistical analysis ; Statistical methods ; Summer ; Summer monsoon ; Surface temperature ; Temperature anomalies ; Tropical climate ; Wind</subject><ispartof>Ying yong qi xiang xue bao = Quarterly journal of applied meteorology, 2017-01 (5)</ispartof><rights>Copyright China Meteorological Press 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>Zheng, Bin</creatorcontrib><creatorcontrib>Li, Chunhui</creatorcontrib><creatorcontrib>Lin, Ailan</creatorcontrib><creatorcontrib>Gu, Dejun</creatorcontrib><creatorcontrib>He, Chao</creatorcontrib><title>Prediction Experiment for the South China Sea Summer Monsoon Strength by Physical-statistic Integrated Model</title><title>Ying yong qi xiang xue bao = Quarterly journal of applied meteorology</title><description>The South China Sea summer monsoon(SCSSM) is a tropical system that plays a key role during the flood season of South China. However, the prediction of the SCSSM strength is difficult by no matter dynamic or statistic methods. Statistic methods are used in practice rather than dynamic model, but empirical-statistic models always have good hindcasting results during the period of building model, while the forecasting skills decrease evidently in practice. Physical-statistic methods have relatively stable predictive skill when the persistence of physical processes is taken into account. Therefore, an integrated technique is introduced based on associated physical processes to establish a predictive model for SCSSM. It is well known that the rainfall of SCSSM has multi-scale climate variability, for example, quasi-biennial and quasi-quadrennial time scale, which are mainly related to TBO(Tropospheric Biennial Oscillation) and ENSO(El Nino-Southern Oscillation), respectively. Based on the corresponding climatic f</description><subject>Annual variations</subject><subject>Anomalies</subject><subject>Atmospheric precipitations</subject><subject>Biennial</subject><subject>Climate variability</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Data</subject><subject>Dynamic models</subject><subject>El Nino</subject><subject>El Nino phenomena</subject><subject>El Nino-Southern Oscillation event</subject><subject>Flood predictions</subject><subject>Hindcasting</subject><subject>Interannual variations</subject><subject>Monsoons</subject><subject>Precipitation</subject><subject>Precipitation anomalies</subject><subject>Quasi-biennial oscillation</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Rainfall distribution</subject><subject>Rainfall forecasting</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>Sea surface temperature anomalies</subject><subject>Southern Oscillation</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Summer</subject><subject>Summer monsoon</subject><subject>Surface temperature</subject><subject>Temperature anomalies</subject><subject>Tropical climate</subject><subject>Wind</subject><issn>1001-7313</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqNjsEKgkAURWdRkJT_8KC1MJOU7sWoRSDYXiZ96oTO2MwT8u-boA9ocbmLew7cFQsE5yJKYhFvWOicevCjiJOD4GnAhsJio2pSRkP-ntCqETVBayxQj1CamXrIeqUllOgzjyNauBntjDdKsqg7TzwWKPrFqVoOkSNJypGq4aoJOysJG280OOzYupWDw_DXW7Y_5_fsEk3WvGZ0VD3NbLWfqu-79JTwNIn_oz7Bl0ml</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Zheng, Bin</creator><creator>Li, Chunhui</creator><creator>Lin, Ailan</creator><creator>Gu, Dejun</creator><creator>He, Chao</creator><general>China Meteorological Press</general><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>H97</scope><scope>KL.</scope><scope>L.G</scope></search><sort><creationdate>20170101</creationdate><title>Prediction Experiment for the South China Sea Summer Monsoon Strength by Physical-statistic Integrated Model</title><author>Zheng, Bin ; Li, Chunhui ; Lin, Ailan ; Gu, Dejun ; He, Chao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_21088670873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>chi</language><creationdate>2017</creationdate><topic>Annual variations</topic><topic>Anomalies</topic><topic>Atmospheric precipitations</topic><topic>Biennial</topic><topic>Climate variability</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Data</topic><topic>Dynamic models</topic><topic>El Nino</topic><topic>El Nino phenomena</topic><topic>El Nino-Southern Oscillation event</topic><topic>Flood predictions</topic><topic>Hindcasting</topic><topic>Interannual variations</topic><topic>Monsoons</topic><topic>Precipitation</topic><topic>Precipitation anomalies</topic><topic>Quasi-biennial oscillation</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Rainfall distribution</topic><topic>Rainfall forecasting</topic><topic>Sea surface</topic><topic>Sea surface temperature</topic><topic>Sea surface temperature anomalies</topic><topic>Southern Oscillation</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Summer</topic><topic>Summer monsoon</topic><topic>Surface temperature</topic><topic>Temperature anomalies</topic><topic>Tropical climate</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zheng, Bin</creatorcontrib><creatorcontrib>Li, Chunhui</creatorcontrib><creatorcontrib>Lin, Ailan</creatorcontrib><creatorcontrib>Gu, Dejun</creatorcontrib><creatorcontrib>He, Chao</creatorcontrib><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Ying yong qi xiang xue bao = Quarterly journal of applied meteorology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zheng, Bin</au><au>Li, Chunhui</au><au>Lin, Ailan</au><au>Gu, Dejun</au><au>He, Chao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction Experiment for the South China Sea Summer Monsoon Strength by Physical-statistic Integrated Model</atitle><jtitle>Ying yong qi xiang xue bao = Quarterly journal of applied meteorology</jtitle><date>2017-01-01</date><risdate>2017</risdate><issue>5</issue><issn>1001-7313</issn><abstract>The South China Sea summer monsoon(SCSSM) is a tropical system that plays a key role during the flood season of South China. However, the prediction of the SCSSM strength is difficult by no matter dynamic or statistic methods. Statistic methods are used in practice rather than dynamic model, but empirical-statistic models always have good hindcasting results during the period of building model, while the forecasting skills decrease evidently in practice. Physical-statistic methods have relatively stable predictive skill when the persistence of physical processes is taken into account. Therefore, an integrated technique is introduced based on associated physical processes to establish a predictive model for SCSSM. It is well known that the rainfall of SCSSM has multi-scale climate variability, for example, quasi-biennial and quasi-quadrennial time scale, which are mainly related to TBO(Tropospheric Biennial Oscillation) and ENSO(El Nino-Southern Oscillation), respectively. Based on the corresponding climatic f</abstract><cop>Beijing</cop><pub>China Meteorological Press</pub></addata></record> |
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| subjects | Annual variations Anomalies Atmospheric precipitations Biennial Climate variability Correlation coefficient Correlation coefficients Data Dynamic models El Nino El Nino phenomena El Nino-Southern Oscillation event Flood predictions Hindcasting Interannual variations Monsoons Precipitation Precipitation anomalies Quasi-biennial oscillation Rain Rainfall Rainfall distribution Rainfall forecasting Sea surface Sea surface temperature Sea surface temperature anomalies Southern Oscillation Statistical analysis Statistical methods Summer Summer monsoon Surface temperature Temperature anomalies Tropical climate Wind |
| title | Prediction Experiment for the South China Sea Summer Monsoon Strength by Physical-statistic Integrated Model |
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