前期土壤湿度、海表面温度对中国夏季极端气温的预测能力评估
投稿时间: 2016-12-20  最后修改时间: 2017-01-05  点此下载全文
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宋耀明 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心 songym@nuist.edu.cn 
邹永成 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心  
王志福 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心  
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
中文摘要:基于中国587站日最高、最低气温观测资料、月平均的ERA_interim土壤湿度(SM)再分析资料及扩展重建的海表面温度(ERSST)资料,对极端气温指数进行了定义,利用变形的典型相关分析(BP-CCA)和集合典型相关分析方法(ECC),分析了1979~2009年我国夏季极端气温与前期(春、前冬)土壤湿度、海表面温度间的线性联系,建立了中国夏季极端气温预测模型,并对独立样本检验的效果进行了评估。结果表明:同中国夏季极端气温联系密切的前期海表面温度异常的空间分布均为类PDO型,以及前期华南、青藏高原、东北、西北土壤湿度异常;交叉检验结果表明基于前冬预测因子的极端气温预测模型技巧高于春季,基于土壤湿度的极端气温预测模型技巧高于海表面温度;独立样本检验表明基于前期土壤湿度、海表面温度的ECC模型对中国东部夏季极端气温有一定的预测能力。因此,可以在夏季极端气温的预测业务中考虑前期土壤湿度及海表面温度的影响。
中文关键词:夏季极端气温  土壤湿度  海温  典型相关分析
 
Assessment of the summer extreme temperature predicting abilities in China for the previous soil moisture and sea surface temperature
Abstract:Based on the maximum and minimum surface air temperatures of 587 stations in China, ERA-interim reanalysis soil moisture(SM) data and the extended reconstructed sea surface temperature (ERSST) data, the possible linear connection between the previous (spring and winter) sea surface temperature (SST), soil moisture and summer extreme temperature in China during 1979-2009 is investigated. Statistical summer extreme temperature prediction models were developed by using Barnett-Presisendorfer Canonical Correlation Analysis(BP-CCA), Ensemble Canonical Correlation(ECC) method and newly defined summer extreme temperature indices. And the scores for the prediction models with predictive skill in the independent sample tests were also calculated. The results showed that the summer extreme temperature in China has close relationship with the previous SST and SM anomaly, the spatial distribution of the previous SST anomaly was similar to the PDO pattern, and the previous SM anomaly in South China, in the Tibetan plateau, in Northeast China and in the west of Northwest China. The cross validation tests showed that BP-CCA models based on the previous winter predictors had higher predictive skill than that based on the previous spring predictors. BP-CCA models based on SM had higher predictive skill than that based on SST. The independent sample tests showed that ECC models based on the previous SM and SST had high predictive skill for summer extreme temperature in China. The study shows that the previous soil moisture and sea surface temperature contain valuable signals for summer extreme temperature in eastern China, and can be considered for the summer extreme temperature prediction operations.
keywords:Summer Extreme Temperature  Soil Moisture  Sea Surface Temperature  Canonical Correlation Analysis
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