Development on Short-term prediction of Iononspheric Parameters
Fan Jun-mei · Progress in geophysics · 2010
We review the ionospheric parameter short-term prediction methods and present several methods developed by the authors,including quasi-real time prediction (15-minute ahead) based on chaotic time series analysis, nowcasting (one-hour ahead) using artificial neural networks and short-term prediction (1 to 3 day ahead),which is realized by several algorithms like neural network,similar-day and integrated model.By using years of data form ionospheric observation stations in China,the prediction accuracy of all the methods is examined,compared with other existent methods qualitatively or quantitatively.All the prediction methods developed by authors can reach high accuracy and are better than that of predecessors'.The average relative error for 15 min ahead prediction is less than 4%,and the absolute one is less than 0.2 MHz.Such precision is high enough for those short wave systems which need high accuracy and real time.The method to forecast f_oF_2 one-hour ahead can also reach high accuracy.During high solar activity,the average relative error is less than 6% and RMS less than 0.6 MHz,while during solar minimum,the average relative error less than 10% and RMS less than 0.5 MHz and the average relative error for all the conditions is 3% lower than that of the autocorrelation one.As to forecasting one-to-three day ahead,the integrated model has the highest precision because it takes full advantage of the neural network method,similar-day method and the autocorrelation one.The method is tested with data of nine inland vertical stations (Haikou, Guangzhou,Chongqing,Lhasa,Lanchow,Beijing,Urumchi,Changchun,Manchuria),covering one solar periods (1976 ～ 1986) and the average relative errors for one day and three day ahead are less than 10% and 15% respectively,keeping ahead in China.Besides,the integrated model can also integrate other methods,having potential to improve the precision.All the methods mentioned in the article are all easy to be implemented,with high accuracy, which could be valuable reference for those who work on short-term prediction or correlated profession.