Preliminary investigation of real-time mapping offoF2in northern China based on oblique ionosonde data

[1] A real-time mapping model of foF2 in northern China was established using neural networks (NNs). To avoid the local minimum problem associated with traditional NNs, a newly improved genetic algorithm-based NN (GA-NN) was developed using the input parameters of solar activities, geomagnetic activities, neutral winds, seasonal information, and geographical coordinates. The foF2 data were extracted by inversing the oblique ionograms obtained from the oblique ionosondes of the China Ground-based Seismo-ionospheric Monitoring Network every 30 min for the period from August 2009 to December 2011. The data associated with five transmitter stations (Beijing, Changchun, Qingdao, Xinxiang, and Suzhou) and one receiver station in Binzhou were considered the input parameters for the real-time foF2 mapping model, and the data from the Dalian and Jinyang transmitter stations were used to verify the results. The Jining transmitter station data were used to test the capability of the model. The root-mean-square error and percent deviation were calculated to estimate the performance of the model. The correlation coefficient was used to evaluate the correlation of observed and predicted values. In addition, observations of foF2 from the vertical ionosondes at Beijing, Changchun, Qingdao, and Suzhou stations are compared with the model prediction of foF2. The results indicate that the developed real-time foF2 mapping model based upon genetic algorithm-based NN is very promising for ionospheric studies.