时间序列法在临近空间大气风场预报中的应用
Application of Time Series Method in Forecasting Near-space Atmospheric Wind
查看参考文献22篇
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文摘
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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88 km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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其他语种文摘
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Due to many factors,near-space environment is complex and variable. Atmospheric environmental elements are hard to be forecasted. In this paper time series method is applied to the near-space wind forecasting. Autoregressive Moving Average (ARMA) model is adopted. The zonal wind data at 88 km altitude of Langfang (39.4°N,116.7°W) MF radar form September 24 to October 24,2015 is used for the forecasting test. In this test the data of past 7 days was used to forecast the data of the 8th day. Results suggest that ARMA model has certain applicability in forecasting the near-space atmospheric wind. The forecast effect is better when the winds have stronger regularity of change,i.e.,when the sample data show a significant 24-hour cycle,the forecast effect is better,and is worse when the winds have a mutation. Compared with the observed data,results show that the forecast error of ARMA model is 9~27m·s~(-1),and the forecast result of ARMA model is better than that of AR model with the same order,and is slightly better than that of higher-order AR model. |
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来源
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空间科学学报
,2018,38(2):211-220 【核心库】
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DOI
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10.11728/cjss2018.02.211
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关键词
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临近空间
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大气风场
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时间序列法
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ARMA模型
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预报
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地址
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1.
中国科学院国家空间科学中心, 北京, 100190
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中国科学院大学, 北京, 100049
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0254-6124 |
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学科
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地球物理学 |
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基金
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国家重点研发计划项目资助
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文献收藏号
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CSCD:6209691
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