Forecast Ionospheric Electron Content

The ionosphere is the atmosphere which is in the height of60～2000kilometers. It is a ionization area containing a large number of electrons and positive ions, and these electrons and positive ions are formed by high-energy rays such as the sun’s ultraviolet rays, X rays et al. ionizing a part of gas molecule in the ionosphere. The ionosphere is an important part of solar-terrestrial space environment which is the survival of humans,and it occupies a very important position in research of modern space weather. Additionally, total electron content (TEC) of ionosphere is an important parameter in describing the morphology and structure of ionosphere. Therefore, it is very important to study the TEC of ionosphere. This paper mainly discusses the following aspects:(1) This paper briefly introduces the significance and the researching progress of forecasting the TEC, and the key point of the paper. Besides it introduces the theory of IGS global ionosphere, and it mainly includes the single model of ionosphere、the data structure of IGS global ionosphere and the distribution of GPS/TEC observation station. We conclude that the current data of TEC is gained by hundreds of global GPS tracking stations, It releases the grid data of2.5°×5°in every two hours.(2) We mainly analyze the characters of the change in TEC. It includes the study of diurnal change、seasonal change、half an anniversary and annual change characters in typical stations, and comparing the distributions of global TEC in different seasons. Finally, we conclude that the values of TEC associate with solar activity, and the stronger the solar activity is, the larger the TEC values are.(3) This part mainly introduces the common predicting principle and the error statistics methods of TEC. It is the autoregressive moving average (ARMA) model, and the method of determining the order of the model as well as the parameter estimation of the model.(4) Using AR model to forecast the total electron content of ionosphere. Firstly, It separates regular and irregular items of TEC to preprocess the data of TEC which uses the least squares fitting. Secondly, we make a detail introduction of AR iterative model. Thirdly, we select the data sequences of Shanghai and Beijing station in2010as basic sequences to forecast regular item、 irregular item and TEC sequences by using AR iterative model, and statistic the root mean square error, relative error and mean absolute error. Finally, we compare and analyze the forecasting accuracy of different parts.(5) We sum up the current research which have been done, and further expand the current achievements as well as look forward the work in future. Future research will include the further exploration and improvement of TEC forecasting methods、the combination of various methods to predict TEC, and comparing the forecasting results with the international results.