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Preliminary investigation of real‐time mapping of f o F 2 in northern China based on oblique ionosonde data
Author(s) -
Zhou Chen,
Wang Ruopeng,
Lou Wenyu,
Liu Jing,
Ni Binbin,
Deng Zhongxin,
Zhao Zhengyu
Publication year - 2013
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1002/jgra.50262
Subject(s) - ionosonde , beijing , transmitter , correlation coefficient , ionosphere , oblique case , geodesy , artificial neural network , meteorology , remote sensing , geology , geography , computer science , china , mathematics , statistics , physics , telecommunications , artificial intelligence , geophysics , channel (broadcasting) , linguistics , philosophy , archaeology , electron density , quantum mechanics , electron
A real‐time mapping model of f o F 2 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 f o F 2 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 f o F 2 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 f o F 2 from the vertical ionosondes at Beijing, Changchun, Qingdao, and Suzhou stations are compared with the model prediction of f o F 2 . The results indicate that the developed real‐time f o F 2 mapping model based upon genetic algorithm‐based NN is very promising for ionospheric studies.