基于Holt-ARIMA-Lagrange Multiplier的CWC信息熵时序研究
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1.中国民用航空华北地区空中交通管理局气象中心;2.南京信息工程大学 数学与统计学院;3.南京信息工程大学 大气与环境实验教学中心

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P457.6

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国家自然科学基金(42075068,41975176,41975087);国家重点研发计划重点专项(2018YFC1507905);


Research on Information Entropy Time Series of CWC Based on Holt-ARIMA-Lagrange Multiplier
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1.Meteorological Center of Air Traffic Regulation of Civil Aviation in North China;2.School of Mathematics and Statistics,Nanjing University of Information Science and Technology;3.Experimental teaching center for meteorology and environment,Nanjing University of Information Science and Technology

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    摘要:

    降水云系的发展过程及其特征分析,是云降水物理学中的一个重要问题.本文选取一次云发展过程中的700 hPa云水含量(Cloud Water Content, CWC)和大气垂直方向上气流速度(Omega, OMG)的1h值,以信息熵来度量CWC空间分布的混沌程度,辅以OMG的时间变化来判断云的发展,并提出了一种基于多尺度分解、Holt模型、自回归滑动平均模型 (Autoregressive Integrated Moving Average Model, ARIMA)和Lagrange Multiplier的组合预测方法.结果表明:1)CWC熵具有非线性和非平稳性;2)在云的不同发展阶段,北方CWC熵序列的均值都小于南方,方差普遍大于南方;3)OMG区域均值与CWC熵的小波低频重构的极值点在时间上有很好的对应关系,相近的极值点在南方中占50%,在北方中占83.3%,说明CWC熵可以在一定程度上反映云系的发展;4)CWC熵序列往往具有多种时间尺度特征,故进行多尺度分解之后再组合建模的Holt-ARIMA-Lagrange Multiplier模型比单一预测方法、单层分解的预测模型更优,准确率提高了3%以上.

    Abstract:

    The development process and characteristic analysis of precipitation cloud system is an important problem in the field of cloud precipitation physics. In this paper, we select the 700 hPa cloud water content (CWC) and the 1h value of airflow velocity (OMG) in the vertical direction of the atmosphere, measure the chaos degree of CWC distribution with the information entropy as a tool, and OMG to judge the development of cloud, a combined prediction model is also proposed based on hybrid multi-scale decomposition, Holt model, autoregressive integrated moving average model (ARIMA) and Lagrange Multiplier. Results show that: 1) The CWC entropy has non-linear and non-stationary characteristics; 2) At the different development stages of the cloud, The means of the CWC entropy sequence of northern clouds are all smaller than those of southern clouds, The variance is generally greater than that of the southern cloud; 3) The OMG mean and the extreme point of the wavelet low-frequency reconstruction of the CWC entropy correspond well in time, Close extreme value points account for 50% of the southern cloud, In 83.3% of the northern cloud, It shows that CWC entropy can reflect the development of cloud system to a certain extent; 4) CWC entropy sequences often have multiple timescale features, Therefore, the accuracy of the Holt-ARIMA-Lagrange Multiplier model after multi-scale decomposition is improved by more than 3%, compared with the single prediction method and the prediction model of single-layer decomposition.

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  • 收稿日期:2022-06-22
  • 最后修改日期:2022-10-07
  • 录用日期:2022-10-11
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