Characteristics Of Ionospheric FoF2 Variation And Its Prediction In Sanya Area

The ionosphere is a partially ionized region of the earth’s upper atmosphere,which exists a large number of charged particles.When radio waves propagate through the anisotropic ionosphere,refraction,scattering,absorption and other phenomena will occur,which will affect the communication equipments and navigation system relying on radio.With the development of science and technology,it is urgent for human beings to know the process of deeper changes in the ionosphere.This paper analyzes the diurnal and seasonal variation characteristics of ionospheric foF2 in Sanya,China,as well as its relationship with solar and geomagnetic activities based on the observation data of ionospheric digital altimeter.The changes of ionospheric foF2 are predicted within a certain range by using observed data,combining with the chaos theory and the RBF neural network model.The predicted results are compared with the measured data and the data from the IRI model.First of all,we introduces the basic knowledge of the ionosphere and the physical and chemical processes of ionospheric changes.It also describes the methods used in ionospheric research and makes a introduction of the development process of the ionosphere and the research status.Next it describes the various detection techniques and means used in ionospheric research.Then,the ionograms will be calibrated to obtain the corresponding ionospheric foF2 data according to the observation data of the ionospheric digital altimeter.In this paper,the variation characteristics of ionospheric foF2 were studied based on the observation data of Sanya station(18.34°N,109.62°E),Hainan in 2013.The results show that foF2 has obvious diurnal variation characteristics,which reaches a maximum after noon,then gradually decreases to a minimum value at around dawn.The maximum and minimum occurrence times correspond to local time around 15:00LT and 06:00LT,respectively.The diurnal variation of ionospheric foF2 is also strongly related to the seasons.The variation amplitudes of ionospheric foF2 in two seasons(spring and autumn)is larger than that in other two seasons(winter and summer).The maximum value is higher in spring and autumn than that in other seasons,while the minimum value is lower in summer than that in other seasons,which exhibits obvious semiannual anomaly.In winter,solar radiation is weak,but the change level of ionospheric foF2 during the daytime is higher than that in summer,showing the winter abnormal.Solar activities also have an influence on the change of ionospheric foF2.The statistical results showed that foF2 increased with the increase of solar activity and the value with moderate solar activity was larger than that with low solar activity.The change of ionospheric foF2 is obviously affected by geomagnetic disturbance.In the magnetostatic period,the diurnal variation of ionospheric foF2 is relatively stable.During the magnetic disturbance period,the foF2 has a significant deviation from the average level.The values from IRI-2016 model values can show the variation characteristics of ionospheric foF2 in Sanya,but there are differences with measured values in day and night,season,solar and geomagnetic activities.Finally,we discusses chaotic features of ionospheric foF2 and its prediction based on the observation data of foF2 in Sanya,2013.The ionospheric foF2 time series in Sanya has a certain time delay and embedding dimension,and its maximum Lyapunov index is 0.1264,which indicates that foF2 time series has chaotic characteristics.The foF2 time series was reconstructed and the predicted results were obtained through the RBF neural network model and Volterra model.The predicted results were compared with the measured data and the data derived from the international reference ionosphere(IRI)model.It shows that the results predicted by the RBF model and Volterra model are significantly improved in Root Mean Square Error(RMSE)and Relative Error(RE)compared with the IRI model.The prediction effect of RBF model was slightly better than that of Volterra model.There is a time scale when using RBF neural network to predict foF2 time series.Within this scale range,the predicted results are more accurate.The accuracy of prediction will decrease with the extension of prediction time.We studies the ionospheric foF2 parameters in Sanya in 2013 and obtains the variation characteristics.FoF2 was predicted by different models and good results are obtained.The method can be used to study the other characteristic parameters in the ionosphere and has certain scientific significance and application value.