Study Of Regional Ionospheric Short-term Forecasting Based On CORS

The ionosphere is an important component of the upper atmosphere and its temporal and spatial changes have very large effects on the reliability of the radio communications system, over-the-horizontal radar system and other systems. Ionospheric total electron content (TEC) is one of the most important ionospheric characteristic parameters, whose ionospheric short-term forecast has become an issue in the research of the ionosphere. The long-term forecasting models which are suitable to China and surrounding areas have been established in China; however, the ionospheric short-term forecast is still at the initial stage. In this paper, the Chinese domestic observations of the International GNSS Service (IGS) stations and national GNSS Continuously Operating Reference Stations (CORS) were used to calculate TEC and do the research about ionospheric short-term forecast in this region. The main contents include the following aspects:1. Temporal and spatial changes of ionosphere. In this paper, NeQuick model, IRI model and IGS data were used to calculate TEC and analyze its diurnal variation, annual discipline, and space distribution, etc.2. Ionospheric TEC calculation. The precisions of Kalman filtering method which is used to estimate the satellite differential code bias and receiver differential code bias are 1ns and 1.5ns respectively. Pseudo-range, carrier phase and carrier phase smoothed pseudo range were used to calculate TEC respectively with dual-frequency GNSS observations, then compared the results of different methods. It is indicated that the precision of carrier phase smoothed pseudo range is better than others, while the precision of VTEC is about 1-5TECU after differential code bias correction.3. Ionospheric TEC short-term forecast research. This paper applies the statistics method innovatively, superposition analysis of periodical wave variance, to short-term forecasting of the ionospheric TEC and improves it after preliminary study and analysis. Then the forecast previsions of both original and improved method were compared based on the ionospheric TEC as original data provided by IGS, which shows that the forecast prevision of the improved method is better than the original one and the forecast previsions about single station and region are both below 3.5TECU. Through comparative analysis with the current methods, this method can be well applied to the ionosphere short-term prediction with the advantage of higher precision, fewer parameters and more simple calculation. Then different sampling rate TEC data were used to short-term forecast, which demonstrate that high sampling rate has little effect to forecast prevision, so 2-hour TEC data could be used in forecast in application, which could reduce the computational complexity, shortened the computing time and improve the forecast efficiency of this method.