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基于Holt-ARIMA-Lagrange Multiplier的CWC信息熵时序研究
作者:
作者单位:

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

中图分类号:

P457.6

基金项目:

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
Author:
Affiliation:

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.

    参考文献
    [1] References
    [2] [1] 段海霞, 李耀辉, 张强, 等. 西北区域几次暴雨过程中的自组织现象[J]. 高原气象, 2011, 30(04): 890-900.
    [3] DUAN Haixia, LI Yaohui, ZHANG Qiang, et al. Phenomenon of vortex self-organization of several rainstorm processes in northwest region of China[J]. Plateau Meteorology, 2011, 30(04): 890-900.
    [4] [2] 张小娟, 陶玥, 刘国强, 等. 一次冰雹天气过程的云系发展演变及云物理特征研究[J]. 气象, 2019, 45(03): 415-425.
    [5] ZHANG Xiaojuan, TAO Yue, LIU Guoqiang, et al. Study on the evolution of hailstorm and its cloud physical characteristics[J]. Meteorological Monthly, 2019, 45(03): 415-425.
    [6] [3] 陈逸伦. 基于多源卫星数据的云团和雨团识别及其特征研究[D]. 中国科学技术大学, 2019.
    [7] CHEN Yilun. Identification of cloud clusters and rain cell and their features from multi-satellite observations[D]. University of Science and Technology of China, 2019.
    [8] [4] 程敬雅, 游庆龙, 蔡淼. 全球云水量气候分布及变化趋势特征分析[J]. 气候与环境研究, 2021, 26(05): 541-555.
    [9] CHENG Jingya, YOU Qinglong, CAI Miao. Climatic distribution and trend characteristics of global cloud water content [J]. Climatic and Environmental Research, 2021, 26 (5): 541?555.
    [10] [5] 刘菊菊, 游庆龙, 周毓荃, 等. 基于ERA-Interim的中国云水量时空分布和变化趋势[J]. 高原气象, 2018, 37(06): 1590-1604.
    [11] LIU Juju, YOU Qinglong, ZHOU Yuquan, et al. Distribution and trend of cloud water content in China based on ERA-Interim reanalysis[J]. Plateau Meteorology, 2018, 37(6): 1590?1604．
    [12] [6] 张学文, 马力. 熵气象学简介[J]. 气象, 1995, (01): 52-56.
    [13] ZHANG Xuewen, MA Li. Entropy Meteorology[J]. Meteorological Monthly, 1995, (01): 52-56.
    [14] [7] 王惠娟. 基于信息熵的大气PM2.5浓度的不确定性研究[D]. 武汉理工大学, 2016.
    [15] WANG Huijuan. The research of PM2.5 atmospheric concentration uncertainty based on information entropy[D]. Wuhan University of Technology, 2016.
    [16] [8] Mario B, Step H W, Mark M. Entropic uncertainty and measurement reversibility[J]. New Journal of Physics, 2016, 18(7). DOI:10.1088/1367-2630/18/7/073004
    [17] [9] 李丽娟, 明飞, 宋学科, 等. 熵不确定度关系综述[J/OL]. 物理学报, 2022, 71(07): 25-41.
    [18] LI Lijuan, MING Fei, SONG Xueke, et al. Review on entropic uncertainty relations [J/OL]. Acta Physica Sinica, 2022, 71(07): 25-41.
    [19] [10] 车金星. 复杂数据的变量选择与预测方法研究[D]. 西安电子科技大学, 2019.
    [20] CHE Jinxing. Variable selection and forecasting method for complex data[D]. Xidian University, 2019.
    [21] [11] Geetha A, Nasira G. Time series modeling and forecasting: tropical cyclone prediction using ARIMA modeI[C].? International Conference on Computing for Sustainable Global Development, 16-18 March 2016, New Delhi, India. IEEE, 2016: 3080-3086.
    [22] [12] Tao X, Li C, Bao Y. Interval-valued time series forecasting using a novel hybrid holt and MSVR model[J]. Economic Modeling, 2017, 60: 11-23.
    [23] [13] 汪漂. 混合区间多尺度分解的区间时间序列组合预测[J]. 运筹与管理, 2021, 30(10): 159-164.
    [24] WANG Piao. An interval time series combination forecasting approach based on hybrid interval multi-scale decomposition[J]. Operations Research and Management Science, 2021, 30(10): 159-164.
    [25] [14] Wu Q, Lin H. A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors[J]. Science of the Total Environment, 2019, 683. DOI:10.1016/j.scitotenv.2019.05.288.
    [26] [15] Stéphanie M, Ma?na A, Rudy C, et al. Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach[J]. Energy, 2017, 119: 288-298.
    [27] [16] 李栋, 薛惠锋, 张燕. 基于经验模态分解的降水量组合预测模型[J]. 计算机仿真, 2019, 36(03): 458-463.
    [28] Li Dong, Xue Huifeng, ZHANG Yan. Combined forecasting model of precipitation based on empirical mode decomposition[J]. Computer Simulation, 2019, 36(03): 458-463.
    [29] [17] Yu X, Guo L, He L H, et al. A SVR–ANN combined model based on ensemble EMD for rainfall prediction[J]. Applied Soft Computing Journal, 2018, 73: 874-883.
    [30] [18] 庞金凤, 刘波, 张波, 等. 基于小波分解的沙尘天气发生日数预测组合模型研究——以2008—2016年策勒沙漠-绿洲过渡带沙尘天气发生时序为例[J]. 气象, 2019, 45(05): 651-658.
    [31] PANG Jinfeng, LIU Bo, ZHANG Bo, et al. Study on the combined model of forecasting the days of sand-dust weather based on wavelet decomposition—taking the series of dust weather in the transitional zone of Qira desert-oasis during 2008—2016 as an example[J]. Meteorological Monthly, 2019, 45(05): 651-658.
    [32] [19] Chen Q, Wen D, Li X, et al. Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow[J]. PloS one, 2020, 15(3). DOI:10.1371/journal. pone.02311.
    [33] [20] 杨国华, 郑豪丰, 张鸿皓, 等. 基于Holt-Winters指数平滑和时间卷积网络的短期负荷预测[J]. 电力系统自动化, 2022, 46(06): 73-82.
    [34] YANG Guohua, ZHENG Haofeng, ZHANG Honghao, et al. Short-term load forecasting based on Holt-Winters exponential smoothing and temporal convolutional network[J]. Automation of Electric Power Systems, 2022, 46(06): 73-82.
    [35] [21] 刘金培, 汪漂, 黄燕燕, 等.基于区间时间序列小波多尺度分解的组合预测方法[J]. 统计与决策, 2020, 36(19): 5-9.
    [36] LIU Jinpei, WANG Piao, HUANG Yanyan, et al. Combined forecasting method based on interval time series wavelet multi-scale decomposition[J]. Statistics and Decision, 2020, 36(19): 5-9.
    [37] [22] 李为东,李莉,徐岩.基于时间序列分析的北京地区PM2.5浓度研究[J]. 运筹学学报, 2018, 22(02): 115-126.
    [38] LI Weidong, LI Li, XU Yan. The concentration research of PM2.5 in Beijing with time series analysis[J].? Operations Research Transactions, 2018, 22(02): 115-126.
    [39] [23] 左秀霞. 带高次趋势项的ADF单位根检验[J]. 数量经济技术经济研究, 2019, 36(01): 152-169.
    [40] ZUO Xiuxia. ADF unit root test with high order trend term[J]. The Journal of Quantitative and Technical Economics, 2019, 36(01): 152-169.
    [41] [24] 丁严, 许德合, 曹连海, 等. 基于CEEMD的LSTM和ARIMA模型干旱预测适用性研究——以新疆为例[J]. 干旱区研究, 2022, 39(03): 734-744.
    [42] DING Yan, XU Dehe, CAO Lianhai, et al. Applicability of the LSTM and ARIMA model in drought prediction based on CEEMD: A case study of Xinjiang[J]. Arid Zone Research, 2022, 39(03): 734-744.
    [43] [25] LI X, XI C, WU W, et al. Forecasting of bioaerosol concentration by a Back Propagation neural network model[J]. Science of the Total Environment, 2020, 698(03), No.134315. DOI: 10.1016/j.scitotenv.2019.134315
    [44] Research on Information Entropy Time Series of Cloud Water Content Based on Mixed Multiscale Decomposition
    [45] ZHANG Xian1, WU Qiong 2, CHEN Yiqi 2, LI Yashao 2, WANG Weiwei 3
    [46] 1 CAAC North China Regional Adminstration, Beijing 100710
    [47] 2 School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
    [48] 3 Experimental teaching center for meteorology and environment, Nanjing University of Information Science and Technology, Nanjing 210044, China
    [49] 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 based on hybrid multi-scale decomposition is proposed.The 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.
    [50] Keywords: information entropy of cloud water content; wavelet decomposition; EMD decomposition; ARIMA; Holt two-parameter exponential smoothing
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张仙,吴琼,陈以祺,黎亚少,王巍巍.基于Holt-ARIMA-Lagrange Multiplier的CWC信息熵时序研究[J].南京信息工程大学学报,,():

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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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