| 基于EEMD的黄河中上游夏季降水预报方法的研究 |
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| 引用本文： | 王文,任冉,李耀辉.基于EEMD的黄河中上游夏季降水预报方法的研究[J].气象科学,2014,34(3):261-266. |
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| 作者姓名： | 王文 任冉 李耀辉 |
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| 作者单位： | 南京信息工程大学 气象灾害教育部重点实验室, 南京 210044;南京信息工程大学 大气科学学院, 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 南京 210044;南京信息工程大学 大气科学学院, 南京 210044;中国气象局 兰州干旱气象研究所, 兰州 730020 |
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| 基金项目： | 公益性行业（气象）科研专项（GYHY201006038，GYHY201006023） |
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| 摘 要： | 传统的统计方法难以很好的对气候系统这一集非线性、非平稳性为一身的多层次系统进行处理。因此集层次化处理和平稳化处理的集合正交经验模态分解技术(EEMD)的提出,为解决上述问题提供了有效的途径。本文选取黄河中上游24个气象观测站的逐月降水资料,结合组合预报和集合预报思路,基于EEMD建立了统计预报模型。其中对降水序列中的高频部分进行了二次平稳化处理,实现对2008—2013年6—8月的降水预报,并用预报评分检测预报效果。结果表明:EEMD模型对黄河中上游夏季降水有着较强的预报能力,在该区域与气候模式和传统的统计方法相比具有更高的精度和更好的应用前景。
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| 关 键 词： | 降水 EEMD 组合预报 集合预报 二次平稳化 |
| 收稿时间： | 2013/9/11 0:00:00 |
| 修稿时间： | 1/5/2014 12:00:00 AM |
Summer rainfall prediction in the upper and middle reaches of the Yellow River with EEMD method |
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| WANG Wen,REN Ran and LI Yaohui.Summer rainfall prediction in the upper and middle reaches of the Yellow River with EEMD method[J].Scientia Meteorologica Sinica,2014,34(3):261-266. |
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| Authors: | WANG Wen REN Ran and LI Yaohui |
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| Institution: | Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China;College of Atmospheric Science, Nanjing University of Informetion Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China;College of Atmospheric Science, Nanjing University of Informetion Science & Technology, Nanjing 210044, China;Institute of Arid Meteorology, CMA, Lanzhou 730020, China |
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| Abstract: | As traditional statistical method is difficult to deal with nonlinear and non-stationary climate system, the Ensemble Empirical Mode Decomposition (EEMD) was found to be effective to stabilize the climatic time series. Based on the monthly precipitation data from 24 weather stations during 1951-2012,the statistical prediction model based on EEMD is built, composed of combined forecasting and ensemble forecasting. The high-frequent part in the precipitation series was twice stabilized and the precipitation forecasting from June to August during 2008-2013 was carried out, which was tested by forecast score. The prediction effect shows that the model is abile to forecast summer precipitation in this region better than climate patterns and traditional statistical method. |
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| Keywords: | Precipitation EEMD Combined forecasting Ensemble forecasting Second stationary process |
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