| Total Electron Content (TEC) is one of the most important ionospheric parameters. As ground-based GPS-TEC network provides a powerful tool to monitor ionospherie continuously in a wide range, GPS-TEC is playing an important role in the research of ionospheric physics and the theory of radio wave propagation. It is valuable in the fields of both theory and engineering. Based on the previous investigation, TEC is influenced by sun activities, magnetic fields, atmospheric winds, and so on. As the same with others ionospheric parameters, TEC's change is also complex in both space and time. The study of TEC reveals the rules of ionospheric-physics processing, and helps us to know the relations between ionosphere and magnetic field, ionosphere and atmosphere. In engineering application, the values of TEC influence the radio wave propagation directly, and influence the human activities just like radio-communication, navigation, measurement and so on. Through the data-analysis, modeling and predicting of TEC, we can obtain space-resource more efficiently. For example, we can increase precision of satellite navigation.It has been many years since TEC was studied, especially in data-analysis, modeling and predicting, and large numbers of fruits are accumulated. It must be pointed out that most of works about the analysis, modeling and predicting of TEC are dealed with in real physics-space, and this method is directly and convenient in use, but not able to open out the complex process in the changes of TEC parameter deeply, for example, the nonlinear reciprocity between varied processes. According to different research purposes, the varied analysis methods in phase-space are adopted and these will be effective methods to open out complex phenomena just like the nonlinear processes and so on.The phase-space analysis like chaos was applied to study TEC analysis, TEC modeling and TEC predicting in this paper. The results of our work are followed:(1) Analysesing the property of TEC time series in phase space to open out the property of planetary wave-type oscillations (PWTO) and chaos existed in ionospheric TEC.Using the two methods of wavelet-analysis and chaos-analysis, we analyses the TEC data, during year 1996-2004,at longitude 120oE, and latitude from -40o to 60o to attain their phase-space properties.At the first, using the method of wavelet-analysis, we separate the spectrum of varied-periodic PWTO from the original temporal signal existed in TEC time series. It is found that the appeared rates and amplitudes of varied-periodic PWTO are distributed quite follow some certain law. At the same time, the amplitudes of varied-periodic PWTO are changed by seasons and latitude. It must be pointed out that these laws are in accord with people's knowledge to PWTO today. This says that planetary waves (PW) are coupled with ionospheric F2 by some mechanism.Secondly, using the method of chaos-analysis, we get the values of the correlation dimension and the Lyapunov exponent of TEC. By fact calculating, it says that the chaotic properties exist in some certain TEC time series and its correlation dimensions are between 2.6592 ~ 5.0334 , its Lyapunov exponents are between 0.0007 ~ 0.7554. The typical correlation dimension and the typical Lyapunov exponent, appeared over magnet equator, are 3.6092 and 0.3369 separately on. These make sure that TEC time series have chaotic property.Follow these results, It says that, using wavelet-analysis and chaos-analysis to analyses TEC data, we can get the characteristic of some important processes of ionospheric physics which are difficult to be got in real physics-space..(2) This paper discusses the modeling problem based on chaotic phase-space theory. Phase-space geometry predicting model is introduced and phase-space analytic predicting model is created.At the first, we introduce one-rank local-region method (ORLRM) to be the model of TEC parameter as geometry predicting model in phase-space. There are many geometry predicting models in phase-space, such as globe-region method, local-region method, zero-rank local-region method, one-rank local-region method, Lyapunov exponent method, NN method and so on. Here, the method, whose precise is the most high, is one-rank local-region method.Secondly, we set up an phase-space analytic predicting model. This model, based on a perfect group of partial differential equations, taking phase vectors as variables, is educed as a discrete one and, at last, is changed into real physics-space. It can express the TEC value in some future time point using the TEC values of N time points before the time we predict at. It can predict not only one step, but also multiple steps.(3) This paper used two models, geometry one and analysis one, to predict real TEC time series detected.At the first, we use weighted one-rank local-region forecasting model, a kind of geometry ones, to predict the TEC time series. The place we predict is chosen at 120oE over magnet equator, and the time we predict is the time points, 500 hours and 2500 hours, or 1000 time points and 5000 time points, that before the bottom of 2004 year. According to the Lyapunov exponent in this area, we reckon the efficient time points are 144 in theory. In this score, our prediction is successful. To 1000 time points, its standard deviation is 8.3123 TECU and the correlation coefficient is about 0.8664. To 5000 points, its standard deviation is 7.6438 TECU and the correlation coefficient is about 0.9172.Secondly, we use chaos phase-space analysis forecasting model to predict the TEC time series. The place we predict is chosen at 120oE over magnet equator, and the time we predict is in January 1997. Its standard deviation is 6.1063~7.4431 TECU and Its correlation coefficient is 0.81594~0.73501 when the prediction step is 48 and 144 point (equal 1~3 day).It says that, we can take the models, geometry model and analysis model, to predict ionospheric TEC parameter preferably. Error analysis shows that, if predicted point is between 1- 144, the error is less. Its standard deviation is between 7.4431 and 8.3123 TECU and the correlation coefficient is between 0.73501 and 0.9172.In conclusion, this investigation provides us with a new method to study TEC of ionosphere. We can not only obtain the characters of TEC, but also enrich our understanding on the ionospheric physics by analyzing in phase-space. Two methods to forecast TEC including phase-space geometry predicting model and phase-space analytic predicting model are put forward. Based on these models, the practical TEC can be predicted with high precision and good stability by our test. We suggest that these models can be used in ionospheric weather nowcast and forecast.
|
PDF Full Text Request