Monitoramento e modelagem de atividades ionosféricas na região oeste do Paraná: uma arquitetura serverless para análise e predição de Roti

Abstract

GNSS (Global Navigation Satellite Systems) are essential for applications that rely on precise positioning, such as navigation, geodesy, and environmental monitoring. In Brazil, the RBMC (Brazilian Network for Continuous Monitoring of GNSS Systems) provides crucial data for studying the ionosphere, a layer of the Earth’s atmosphere that can significantly affect the accuracy of GNSS signals due to irregularities and ionospheric scintillation. This study presents the development of an online platform for monitoring ionospheric activities in western Paraná, using GNSS data collected from RBMC stations located in the municipalities of Foz do Iguaçu, Guaíra, and Santa Helena. The methodology adopted includes the automation of the data collection and processing workflow, as well as its availability through an API, which integrates the data panels of the developed web platform. Additionally, ionospheric metrics such as ROT (Rate of TEC), ROTI (Rate of TEC Index), and ionospheric gradient were employed to characterise ionospheric variations and their impacts on GNSS signals. The developed platform enables interactive data analysis, allowing users to visualise time series and export information for advanced studies. The results obtained demonstrate the feasibility of the proposed solution, highlighting the efficiency of the XGBoost model in forecasting variations in the ROTI metric, surpassing traditional statistical approaches. The platform represents a significant advancement in ionospheric monitoring, providing a practical tool for real-time analysis of ionospheric irregularities. As a continuation of this work, it is proposed to enhance the platform’s user interface and experience, in addition to incorporating new functionalities as required by ITAIPU Binacional. Future extensions may include data from additional RBMC stations and explore advanced approaches to further improve the accuracy of ionospheric forecasting.