万仕全 ,封国林 ,周国华 ,王冰梅,秦铭荣,许遐桢 . 2005. 基于EMD方法的观测数据信息提取与预测研究[J]. 气象学报, 63(4):516-525, doi:10.11676/qxxb2005.051
基于EMD方法的观测数据信息提取与预测研究
EXTRACTING USEFUL INFORMATION FROM THE OBSERVATIONS FOR THE PREDICTION BASED ON EMD METHOD
投稿时间:2005-01-08  修订日期:2005-04-06
DOI:10.11676/qxxb2005.051
中文关键词:  经验模态分解,非线性/非平稳时间序列,层次理论,气候预测
英文关键词:Complex quality control, Control threshold limit,Extremely abnormal meteorological data, Extreme weather or climate event.
基金项目:国家自然科学基金(90411008和40325015)及国家重点发展基础研究项目(2006CB4 00503)
作者单位
万仕全 江苏省扬州市气象局扬州225000 国家气候中心气候研究开放实验室北京100081 
封国林 国家气候中心气候研究开放实验室北京100081 
周国华 江苏省气象局南京210009 
王冰梅 江苏省气象局南京210009 
秦铭荣 江苏省气象局南京210009 
许遐桢 江苏省气象局南京210009 
摘要点击次数: 2441
全文下载次数: 2424
中文摘要:
      用统计方法作月、季尺度的短期气候乃至年际尺度的长期气候预测是当前气候预测业务的主要依据,在短时间内这种情况仍然不可能彻底改变。虽然数值预报模式的预测能力达到了7 d的时效,不过要积分到月、季尺度并实现短期气候预测还面临着重重困难。其根本原因是气候系统的混沌分量和非线性/非平稳性等因素在起作用。而现有气候预测的统计方法(主要包括经验统计、数理统计和物理统计等方法)的数学基础却忽略了这些特点,这是因为以现 有的科学水平人们不得不假设时间序列是线性和平稳的。实际气候观测序列普遍具有层次性 、非线性和非平稳性,这给建立预测方法带来了极大困难。文中构建了一个新的预测模型, 即首先利用经验模态分解(empirical mode decomposition, EMD)方法将气候序列作平稳化处理,得到一系列平稳分量本征模函数(intrinsic mode function,IMF);其次,利用均生函数(mean generate function,MGF)模型获得各分量的初次预测值;最后,在最优子集回归(optimal subset regression,OSR)模型的基础上,通过直接或逐步拟合一部分预测值,构建两种预测方案达到提高预测能力的目的。典型气候序列的预测试验结果表明,具有平 稳化的IMF分量,尤其是特征IMF分量有较高的可预测性,它对原序列趋势的预测有重要指示意义。大力开展气候系统机理和气候层次的研究,并建立相应的气候模式是未来发展趋势。该文是这方面的一个初步尝试,相信该模型能为气候预测(评估)开辟一条新的有效途径。
英文摘要:
      At present, the main proach to the climate prediction in practice such as the short-term and long-term climate prediction is using the statistical method to predict the climate in month scale, season scale and annual scale, respectively. Now the numerical model is capable to forecast weather up to 7 days, but there a re numerous difficulties in realizing the short-climate prediction by numerical integration due to the nonlinear/non-stationary effects of the climate system. In fact, most of the present statistical climate prediction methods (mainly includes empirical, mathematical and physical statistics methods) are based on the hypothesis that the system is stationary. However, the observations, in particul ar for the climate data, are often nonlinear/non-stationary and multihierarchical, which makes the prediction very difficult. Aiming at this problem, a new p rediction model is introduced, in which, firstly, using the empirical mode decom position the observation sequence are stationarized and a variety of intrinsic mode functions (IMF) are obtained; secondly the IMFs are predicted by the mean ge nerating function model separately; finally with the optimal subset regression model the part of predictions are used as new samples to fit the original series directly or step by step and a system of prediction equations are set up. The climate sequences prediction research shows that the individual IMF, especially the eigen-IMF, has more stable predictability than that of its sources. The trend of development in climate prediction lies in researching the mechanism and hier archy of the climate system, constructing the corresponding climate prediction model. An attempt has been accomplished in this paper. It is believed that the mo del proposed can open up a new effective way for the climate prediction or evaluation.
HTML   查看全文   查看/发表评论  下载PDF阅读器
分享按钮