违反了 PRIMARY KEY 约束 'PK_t_counter'。不能在对象 'dbo.t_counter' 中插入重复键。 语句已终止。 时间尺度分离在华南夏季极端高温预测中的应用-Application of Time-Scale Decomposition Statistical Method in Climatic Prediction of Summer Extreme High-Temperature Events in South China
doi:  10.3878/j.issn.1006-9585.2017.16220
时间尺度分离在华南夏季极端高温预测中的应用

Application of Time-Scale Decomposition Statistical Method in Climatic Prediction of Summer Extreme High-Temperature Events in South China
摘要点击 364  全文点击 808  投稿时间:2016-12-15  
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基金:  国家重点基础研究发展计划项目2012CB955604,国家自然科学基金项目41275089、41305071、41425019、41661144016,海洋公益性行业科研专项201505013
中文关键词:  华南  时间尺度分离  夏季高温日数  预测
英文关键词:  South China  Time-scale decomposition  Summer hot days  Prediction
        
作者中文名作者英文名单位
陈思思CHEN Sisi中国科学院大气物理研究所季风系统研究中心, 北京 100190;中国科学院大学, 北京 100049
张井勇ZHANG Jingyong中国科学院大气物理研究所季风系统研究中心, 北京 100190;中国科学院大学, 北京 100049
黄刚HUANG Gang中国科学院大学, 北京 100049;中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京 100029;全球变化与中国绿色发展协同创新中心, 北京 100875
引用:陈思思,张井勇,黄刚.2018.时间尺度分离在华南夏季极端高温预测中的应用[J].气候与环境研究,23(2):185-198,doi:10.3878/j.issn.1006-9585.2017.16220.
Citation:CHEN Sisi,ZHANG Jingyong,HUANG Gang.2018.Application of Time-Scale Decomposition Statistical Method in Climatic Prediction of Summer Extreme High-Temperature Events in South China[J].Climatic and Environmental Research(in Chinese),23(2):185-198,doi:10.3878/j.issn.1006-9585.2017.16220.
中文摘要:
      基于高温日数存在受不同物理因子影响不同时间尺度变率的特征,应用滤波对华南夏季高温日数进行时间尺度分离,得到高温日数的年代际分量和年际分量。统计分析高温日数总量、年代际分量和年际分量在各自对应时间尺度上的影响因子,采用"向前"交叉检验逐步回归法,分别建立高温日数总量、年代际分量和年际分量的回归模型。高温日数总量的回归模型即为高温日数不区分时间尺度的直接回归模型,而两个分量回归模型拟合结果的叠加,即为高温日数时间尺度分离统计模型对总量的拟合。利用十折交叉检验法,对高温日数直接回归模型和时间尺度分离统计模型的拟合结果进行比较:相比高温日数直接回归模型,时间尺度分离统计模型的年代际分量均方根误差由2.6降低到2.3,与观测数据的相关系数由0.69提高到0.73(显著性水平α=0.01);年际分量均方根误差由3.2降低到2.9,与观测数据的相关系数由0.4(α=0.1)提高到0.48(α=0.01);高温日数总量均方根误差由4.1降低到3.7,与观测数据的相关系数由0.48提高到0.62(α=0.01)。1979~2010年拟合时段华南夏季高温日数的回报结果表明:两模型回报结果与观测数据均存在明显相关(α=0.01),直接回归模型的相关系数为0.57,时间尺度分离统计模型提高到0.72。2011~2013年独立检验时段的预测结果表明:直接回归模型预测结果的平均均方根误差为26.4%,时间尺度分离统计模型降低到12.3%。初步结果表明,两模型对华南夏季高温日数均有一定的预测能力,而时间尺度分离统计模型的预测结果有所改进。
Abstract:
      A time-scale decomposition (TSD) method to statistically downscale the predictand and predictors is used for seasonal forecast of summer extreme high temperature events (hot days) in South China. The hot days present a significant variability that is associated with distinct possible predictors. Both the hot days and the possible predictors are decomposed into inter-decadal and inter-annual components by fast flourier transformation filtering. Three downscaling regression models are then separately set up for the total hot days and the inter-decadal and inter-annual components of hot days. The downscaling regression model of the total hot days is named as direct regression model, while the downscaled inter-decadal and inter-annual regression models are combined together and named as TSD statistical regression model to obtain the total hot days. The fitting results of the direct regression model and TSD statistical regression model are tested by 10-fold cross-validation. The results show that compared to the direct regression model, the TSD statistical regression model decreases the root-mean-square error (RMSE) from 2.6 d to 2.3 d and increases the correlation coefficient with observations from 0.69 to 0.73 for the inter-decadal component; the TSD statistical regression model also decreases the RMSE from 3.2 d to 2.9 d and increases the correlation coefficient from 0.4 to 0.48 for the inter-annual component; for total hot days, the TSD statistical regression model decreases the RMSE from 4.1 d to 3.7 d and increases the correlation coefficient from 0.48 to 0.68. The hindcast results of hot days during 1979-2010 show that the correlation coefficient between observations and outputs of the direct regression model is 0.57, while the value is improved to 0.72 by the TSD statistical regression model. The forecast results of hot days during the independent validation period (2011-2013) show that the relative RMSE is 26.4% by the direct regression model, and it is 12.3% by the TSD statistical regression model. Compared with observations, both of the direct regression model and the TSD statistical regression model can predict the hot days to some extent in South China, and the TSD statistical regression model performs better for forecasts during 1979-2013.
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