| 摘要: |
| 利用相空间理论及方法对渤、黄、东海共4个站位近十几年的旬平均SST进行分析。结果表明：表层水温具有混饨特性，其吸引子关联维数平均约为1.23、嵌入相空间维数为6（渤、黄海）和7（东海178号站位）、二阶Renyi熵平均约为3.×10-4（1／d）及平均可预报时间尺度平均为27个点；基于以上分析结果运用相空间反演方法建立了旬平均SST的反演模型，并且在试预报的前5旬的最大相对误差约为4.2％。 |
| 关键词: 关联维数 嵌入维数 Lyapunov指数 二阶Renyi熵 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金资助项目，49476254号;国家“八五”攻关项目，85－903－08－01号 |
附件 |
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| APPLICATION OF INVERSE METHOD IN PHASE SPACE TO FORECAST SST |
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Wei Enbo, Tian Jiwei, Li Fengqi, Su Yusong
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Ocean Department of Ocean University of Qingdao,Qingdao 26003
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| Abstract: |
| Phase space theory and the inverse method were used to study the decadal average SST of the Bohai Sea, Yellow Sea and East China Sea. The results showed that the SST can be described as a chaotic phenomenon with phase space average correlation dimension, embedding dimension, two order Renyi entropy, and average predictable time scale of 1.23, 6 (Bohai Sea, Yellow Sea) or 7 (East China Sea), 3.7×10-4 (l/d) and 27 points, respectively.
Use of the phase space method and the above results yielded the inverse equation of SST below.
dX/dt=A+BX+XTCX (1)
where, X={x(t), x (t+τ), …, x[t+(m-1)τ]}T; τ=9Δt.
{x(t0+iΔt), i=1, 2, 3, …, n-1} is a SST time series, Δt is the time interval. Used the equation (1), the biggest prediction error of SST is about 4.2% within the first predictable five points. Main conclusions: (1) The analyzed stations' SST can be described by no more than six or seven and no less than two elements though we do not know clearly which corresponding elements affect SST. (2) Because of local chaotic traits and the whole predictable time scale controlled by Lyapunov exponent and two order Renyi entropy, the practical time scale is smaller than the whole predictable one. |
| Key words: Correlation dimension, Embedding dimension, Lyapunov exponent, Two order Renyi entropy |
