| 广西夏季降水的多时间尺度特征及影响因子 |
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| 引用本文： | 覃志年,胡娅敏,陈丽娟.广西夏季降水的多时间尺度特征及影响因子[J].应用气象学报,2013,24(5):565-575. |
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| 作者姓名： | 覃志年 胡娅敏 陈丽娟 |
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| 作者单位： | 1.广西壮族自治区气候中心，南宁 530022 |
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| 基金项目： | 资助项目：国家自然科学基金青年基金项目（40905043），广州市科技计划项目（2010Y1 C031），广西自然科学基金项目（2011GXNSFA018002），国家重点基础研究发展计划项目（2013CB430203），中国气象局气候预测创新团队项目 |
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| 摘 要： | 利用1951—2011年广西夏季降水站点资料和NCEP/NACR等多种再分析资料，通过相关分析、经验模态分解、统计检验分析了广西夏季降水的多时间尺度特征及其影响因子，利用多元线性回归方法对夏季降水进行拟合和预测试验。结果显示:广西夏季降水具有多时间尺度特征，不同时间尺度对应着环流因子不同时间尺度的分量；在准2年尺度上，主要影响因子为季风槽、低空急流、高空急流、贝加尔湖高度场、南印度洋东部海温。利用对广西夏季降水影响显著的环流因子本征模态函数分量和多元线性回归方法拟合夏季降水，相关系数为0.73，表明广西夏季降水是环流因子多时间尺度共同作用的结果。利用前期冬季南印度洋东部海温异常本征模态函数作为前兆因子预报广西夏季降水，6个独立样本检验显示预测与实况趋势一致，该工作可供利用多时间尺度信息进行区域气候预测参考。
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| 关 键 词： | 夏季降水 多时间尺度 经验模态分解 本征模态函数 |
| 收稿时间： | 2012-12-06 |
| 修稿时间： | 4/2/2013 12:00:00 AM |
The Multi timescale Features for Guangxi Summer Precipitation and the Related Predictors |
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| Qin Zhinian,Hu Yamin and Chen Lijuan.The Multi timescale Features for Guangxi Summer Precipitation and the Related Predictors[J].Quarterly Journal of Applied Meteorology,2013,24(5):565-575. |
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| Authors: | Qin Zhinian Hu Yamin and Chen Lijuan |
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| Institution: | 1.Guangxi Climate Center, Nanning 5300222.Guangdong Provincial Climate Center, Guangzhou 5100803.Guangzhou Climate and Agricultural Meteorology Center of Guangdong Province, Guangzhou 5100804.Laboratory for Climate Studies, National Climate Center, CMA, Beijing 100081 |
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| Abstract: | Based on NCEP/NACR reanalysis data and Guangxi summer precipitation (GSP) station data, using the correlation analysis, composite analysis, empirical orthogonal function (EOF), empirical mode decomposition (EMD), abrupt change test and the statistic significant test methods, GSP multi-timescale characteristics and their related circulation as well as the external forcing features are analyzed. According to the diagnostic analysis, the fitting and the prediction equation of GSP are proposed by the multivariate linear regression method.GSP is mainly influenced by the mid-latitude height field anomaly in Lake Baikal region, the subtropical high and monsoon trough (MonTr) in the subtropical region, the low level jet (LLJ) and upper level jet (ULJ) in the same season, as well as the sea surface temperature (SST) anomaly in the eastern of the South Indian Ocean in the pre-winter and pre-spring.The possible physical concept model for GSP is that, when MonTr, LLJ, and the easterly to the south of the subtropical high (ESTH) occur at 850 hPa wind field, the blocking high (BH) over Lake Baikal at 500 hPa potential height, as well as ULJ over South China at 200 hPa wind field are stronger (weaker) than normal, and the subtropical high ridge location is northward (southward) to its normal position, the rainfall is more. The influences of circulation may impact summer rainfall anomaly through the multi-timescale features.Using EMD method, there are 5 principle modes for the summer rainfall. The variance contributions from the first to the fourth intrinsic mode function (IMF1—IMF4) are 55%, 18%, 12% and 12%, respectively. The periods over the statistic significant test are quasi-2 years, 7.6 years, 12.7 years and 19 years. On the scale of quasi-2 years, the summer rainfall is affected by the corresponding IMF1 components of the MonTr, LLJ, ULJ, BH over Lake Baikal, SST anomaly in the east of the South Indian Ocean. The summer rainfall has high relationship with the other influenced indexes on the different time scales.Using IMF1—IMF4 components of circulation factors and the multivariate linear regression method, the summer precipitation equation is fitted. The results show that the multiple correlation coefficients reach 0.73 with the significant level over 0.05. The tests verify that the summer precipitation is really influenced by the multi-timescale components of different factors.Furthermore, based on the IMFs of SST anomaly in the east of southern Indian in winter, the prediction model of the summer precipitation is constructed by the multivariate linear regression method. The trends of the 6 independent sample tests are accord with that of the observation. This method provides an idea in the regional climate prediction based on the multi-timescale features of predictant and predictor. |
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| Keywords: | the summer precipitation multi timescale features empirical mode decomposition (EMD) intrinsic mode function (IMF) |
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