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The ionosphere stands in the path of signals emitted by Global Navigation Satellite System (GNSS) satellites to receivers located on the Earth's surface. Many factors affect the accuracy of satellite positioning, but error due to ionospheric refraction is the largest among them. For this reason, it is important to minimize the impact of ionospheric refraction, and ionospheric models are one of the methods used. As the intensity of the processes taking place in the ionosphere is variable because of solar activity, the influence on satellite observations is also not constant; it varies by location and time of day and year. Therefore, models focusing on the region of interest to users are especially useful in precise GNSS applications. In this research, the H2PT model covering the region of Europe was examined at a temporal resolution of 15 min and two spatial resolutions (latitude × longitude) of 1° × 1° and 5° × 5°. This study aimed to compare the H2PT model with the solution obtained from the International GNSS Service (IGS) in the context of vertical total electron content (VTEC). The H2PT values in high-latitude regions turned out to be overestimated compared to IGS VTEC maps, while, in low-latitude regions, the situation was the opposite. Although the differences between the analyzed maps were usually a few TECUs, it was observed that, during the course of a day, they could increase to several dozen TECUs. Furthermore, the data from selected days characterized by high or low activity of the ionosphere were subjected to a detailed analysis (in relation to quiet days, as well as to the median). The data available with a 15-min interval allowed the identification of short-term disturbances appearing in the ionosphere. The analyzed model, which is of a regional nature and has a relatively high resolution, allows improvement to be made to the quality of the determined ionospheric correction in GNSS positioning. [ABSTRACT FROM AUTHOR]

In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning can both benefit from the accurate modeling and forecasting of ionospheric TEC. The majority of current ionospheric TEC forecasting research only considers the temporal or spatial dimensions, ignoring the ionospheric TEC's spatial and temporal autocorrelation. Therefore, we constructed a spatiotemporal network model with two modules: (i) global spatiotemporal characteristics extraction via forwarding spatiotemporal characteristics transfer and (ii) regional spatiotemporal characteristics correction via reverse spatiotemporal characteristics transfer. This model can realize the complementarity of TEC global spatiotemporal characteristics and regional spatiotemporal characteristics. It also ensures that the global spatiotemporal characteristics of the global ionospheric TEC are transferred to each other in both temporal and spatial domains at the same time. The spatiotemporal network model thus achieves a spatiotemporal prediction of global ionospheric TEC. The Huber loss function is also used to suppress the gross error and noise in the ionospheric TEC data to improve the forecasting accuracy of global ionospheric TEC. We compare the results of the spatiotemporal network model with the Center for Orbit Determination in Europe (CODE), the convolutional Long Short-Term Memory (convLSTM) model and the Predictive Recurrent Neural Network (PredRNN) model for one-day forecasts of global ionospheric TEC under different conditions of time and solar activity, respectively. With internal data validation, the average root mean square error (RMSE) of our proposed algorithm increased by 21.19, 15.75, and 9.67%, respectively, during the maximum solar activity period. During the minimum solar activity period, the RMSE improved by 38.69, 38.02, and 13.54%, respectively. This algorithm can effectively be applied to ionospheric delay error correction and can improve the accuracy of satellite navigation and positioning. [ABSTRACT FROM AUTHOR]