时间序列法在临近空间大气风场预报中的应用

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24 h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.%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 9～27 m.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.