Volume 38 Issue 2
Mar.  2018
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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

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

doi: 10.11728/cjss2018.02.211
  • Received Date: 2017-02-20
  • Rev Recd Date: 2017-08-13
  • Publish Date: 2018-03-15
  • 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 88km 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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