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时间序列法在临近空间大气风场预报中的应用

刘涛 肖存英 胡雄 涂翠 杨钧烽 徐轻尘

刘涛, 肖存英, 胡雄, 涂翠, 杨钧烽, 徐轻尘. 时间序列法在临近空间大气风场预报中的应用[J]. 空间科学学报, 2018, 38(2): 211-220. doi: 10.11728/cjss2018.02.211
引用本文: 刘涛, 肖存英, 胡雄, 涂翠, 杨钧烽, 徐轻尘. 时间序列法在临近空间大气风场预报中的应用[J]. 空间科学学报, 2018, 38(2): 211-220. doi: 10.11728/cjss2018.02.211
LIU Tao, XIAO Cunying, HU Xiong, TU Cui, YANG Junfeng, XU Qingchen. Application of Time Series Method in Forecasting Near-space Atmospheric Windormalsize[J]. Journal of Space Science, 2018, 38(2): 211-220. doi: 10.11728/cjss2018.02.211
Citation: LIU Tao, XIAO Cunying, HU Xiong, TU Cui, YANG Junfeng, XU Qingchen. Application of Time Series Method in Forecasting Near-space Atmospheric Windormalsize[J]. Journal of Space Science, 2018, 38(2): 211-220. doi: 10.11728/cjss2018.02.211

时间序列法在临近空间大气风场预报中的应用

doi: 10.11728/cjss2018.02.211
基金项目: 

国家重点研发计划项目资助(2016YFB0501503)

详细信息
    作者简介:

    刘涛,E-mail:xiaocy@nssc.ac.cn

  • 中图分类号: P351

Application of Time Series Method in Forecasting Near-space Atmospheric Windormalsize

  • 摘要: 受多种因素影响,临近空间大气环境要素复杂多变,预报难度很大.本文采用时间序列法中的自回归滑动平均(ARMA)模型对临近空间大气风场开展统计预报方法研究,基于廊坊(39.4°N,116.7°W)中频雷达在88km高度的大气纬向风数据开展预报试验.本次预报试验的样本数据为2015年9月24日至10月24日风场数据,利用过去7天数据对未来第8天风场数据进行预报.试验结果显示,ARMA模型对临近空间大气风场预报有一定的适用性.当风场变化规律性较强,即样本数据风场呈现出比较显著的24h周期性变化时,ARMA模型预报效果较好;当风场发生突变时,预报效果变差.与实测数据的对比结果表明,ARMA模型预报结果的误差在9~27m·s-1,预报效果优于同阶自回归(AR)模型,略优于高阶AR模型.

     

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出版历程
  • 收稿日期:  2017-02-20
  • 修回日期:  2017-08-13
  • 刊出日期:  2018-03-15

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