Abstract The ionosphere poses challenges for accurate forecasting due to its complexity and variability. Irregularities in the lower ionosphere are influenced by local time, season, geographic location, solar activity and space weather, complicating precise predictions. However, understanding this region is crucial for radio communication, navigation and Global Navigation Satellite System (GNSS) accuracy. This study presents Generalized Linear Models (GLMs) to forecast ionospheric conditions at a specific location. Designed for simplicity and interpretability, these models use at most four independent variables related to local time, seasonal variability, solar cycle and magnetosphere state. The third quartile of Rate of TEC index (ROTI) was used to classify the state of the ionosphere: values below 0.5 indicated regular condition, while high values indicated irregularity. The models were trained and tested using ROTI data from GNSS receivers in Brasília (2010–2022). After training and calibration, the optimal GLM, featuring a probit link function, achieved near‐perfect classification of irregular conditions. This enhanced model represents an alternative in predicting ionospheric behavior, providing valuable insights for space weather applications.
Brhian et al. (Sun,) studied this question.