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黄良珂,李 琛,王浩宇,等. 联合经验正交分解和ARIMA模型的中国地区电离层短期预报[J]. 科学技术与工程, 2020, 20(30): 12304-12312.
huang liang ke,et al.Research on Short-term Ionospheric Prediction Combining with EOF and ARIMA Model Over China Area[J].Science Technology and Engineering,2020,20(30):12304-12312.
联合经验正交分解和ARIMA模型的中国地区电离层短期预报
Research on Short-term Ionospheric Prediction Combining with EOF and ARIMA Model Over China Area
投稿时间:2019-11-18  修订日期:2020-08-03
DOI:
中文关键词:  电离层预报  经验正交分解  自回归移动平均模型  中国地区
英文关键词:Ionosphere prediction  Empirical orthogonal function  ARIMA  China region
基金项目:国家自然科学基金(41704027, 41664002),广西 “八桂学者”岗位专项经费项目,(2017GXNSFBA198139, 2017GXNSFDA198016)。
                 
作者单位
黄良珂 桂林理工大学测绘地理信息学院
李 琛 桂林理工大学测绘地理信息学院
王浩宇 桂林理工大学测绘地理信息学院
彭 华 桂林理工大学测绘地理信息学院
陈 军 武汉大学测绘学院
刘立龙 桂林理工大学测绘地理信息学院
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中文摘要:
      针对电离层垂直总电子含量(Vertical Total Electron Content,VTEC)具有非平稳和季节性变化的特性,结合经验正交分解(EOF)能够对非平稳时间序列进行简化和剔除冗余信息的优势,该文探索联合EOF和自回归移动平均模型(ARIMA)作为FOE-ARIMA,对中国地区电离层VTEC进行短期预报。采用IGS(International GNSS Service)中心提供的中国地区电离层格网数据(Global Ionospheric Maps,GIM),对不同季节前10天GIM数据进行EOF分解,使用ARIMA模型对主分量进行预报,通过重构获取未来5天VTEC值,并将EOF-ARIMA模型预报结果与ARIMA模型进行对比、分析。结果表明:EOF-ARIMA模型平均相对精度为83.3%,平均标准差为3.51 TECu,较ARIMA模型其平均相对精度提高了3.3%,平均标准差降低了0.16 TECu;EOF-ARIMA模型预测结果无明显季节差异,ARIMA模型秋季预报精度明显低于其它季节;EOF-ARIMA模型在赤道异常处预报精度未受影响。由此表明EOF-ARIMA模型在中国地区进行电离层短期预报具有较高的精度和稳定性。
英文摘要:
      The ionospheric Vertical Total Electron Content (VTEC) with the characteristics of non-stability and seasonal variation, and the Empirical Orthogonal Function (EOF) also has advantages of simplifying and eliminating redundant information of unstable time series. In this work, the EOF and ARIMA models are combined, named as EOF- ARIMA model, to predict the Vertical TEC (VTEC) in China. The EOF decomposition was performed on the GIM data (the Global Ionospheric Maps data provided by the IGS Center) in the first 10 days of different seasons, and then the ARIMA model was used to predict the principal components; finally, the VTEC values could be obtained after reconstructed for the next 5 days. The prediction results of the EOF-ARIMA model were compared with the commonly used ARIMA model. The results show that the average relative accuracy and the average standard deviation are 83.3% and 3.51TECu for EOF-ARIMA model, respectively, which has a 3.3% and 0.16 TECu improvement for those of the ARIMA model, respectively. The predicted results of EOF-ARIMA model has no obvious seasonal variations, while the ARIMA model shows smaller values in autumn than other seasons. Besides, the predicted performance did not affect by the equator abnormal for EOF-ARIMA model. Thus, the EOF-ARIMA model shows higher accuracy and stability when used to predict VTEC at short-term for China area.
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