The formation of the ionosphere is mainly the interaction of solar radiation and the earth's atmosphere, in different
temporal-spatial environment, the characteristics of the ionosphere is more complex, and the Total Electron Content
(TEC) is one of the important parameters of the ionospheric morphology and structure. Therefore, in this paper, using the
high-precision TEC time series provided by the International GNSS Service (IGS) as experimental data, by Fast Fourier
Transform (FFT) to detect its periodic changes, and then focus on analysis the characteristics of diurnal variation,
seasonal variation and annual variation and winter anomaly, simultaneous analysis of the ionospheric characteristics vary
with latitude and longitude. The result show that: (1) TEC changes more intense during the day, but the night is quiet,
and in different latitudes, the TEC reached peak value at different moment; (2) Winter anomaly exists only during the day,
night does not exist; (3) In the same time domain, TEC value decreases gradually with the increase of latitude, and it has
different spatial variation features in different hemispheres.
Aiming at the characteristic of nonlinear and non-stationary in ionospheric total electron content(TEC), this article bring Wavelet Analysis into the autoregressive integrated moving average model to forecast the next four days’ TEC values by using six days’ ionospheric grid observation data of Chinese area in 2010 provided by IGS station. Taking IGS station’s observation data as true value, compare the forecast value with it then count the forecast accuracies which are to prove that it has a quite good result by using WARIMA model to forecast Chinese area’s Ionospheric grid data. But near the geomagnetic latitude of about ±20°grid, the model’s forecast results are a little worse than others’ because Geomagnetic activity is irregular which lead to the TEC values there change greatly.
Using the eleven Radiosonde Stations’ data in southwestern of China from 2010 to 2013 to calculate the conversion coefficient K which is a reference value of Precipitable Water Vapor (PWV). Then build the EMARDSON model and the EMARDSON K model which introduced with elevation parameter and altitude. And to analysis the accuracy of the two models in the southwest China by radiosonde data in 2014. The results show: 1) The K value calculated by EMARDSON model has good adaptability in southwest region. 2) The method of spatial interpolation prediction by choosing 7 Radiosonde Stations’ K value uniformity is more adaptable than using 11 Radiosonde Stations’ K value to build basic model in the case of predicting 11 Radiosonde Stations’ K value, and it has a certain accuracy when predicting by using spatial interpolation in some areas where lacking data. 3) The accuracy by using the A-EMARDSON model to predict K value was improved obviously. at the same time, when predicting K value by the method of spatial interpolation, both the precision of inner and the precision of outer are better than EMARDSON model. So it can be concluded that the altitude factor is an important factor to influence the K value prediction.