Speaker
Description
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 the application of Generalized Linear Models (GLMs) to forecast ionospheric conditions. The method is illustrated using 11-year solar cycle data from a GNSS station located in Brasília (geographic coordinates - 15°S and 47°W, 1106.02 m for altitude). Designed for simplicity and interpretability, these models use at most four independent variables to predict the ionosphere behaviour, all of them related to local time, seasonal variability, solar cycle and magnetosphere state. These selected variables were: Time Left to Sunrise (TLS), Maximum Elevation Angle of the Sun (EAS), F$_{10.7}$ and K$_p$, and combinations among themselves, during the 24th solar cycle. The third quartile of Rate of TEC index (ROTI) was used to classify the state of the ionosphere, defined as a binary response variable in the model: values below 0.5 indicated regular condition, while high values indicated irregularity. The models were trained (with 70% of random data) and tested (with 30% of random data) using ROTI data from GNSS receivers in Brasília (2010–2022). After training and calibration, the optimal model, featuring a probit link function, achieved near-perfect classification of the ionosphere. A logit link function also yielded good classification scores, having the advantage of providing easier interpretation of the model optimal parameter. In conclusion, the GLM model represents a practical and promising alternative for predicting ionospheric behavior, also providing valuable insights for space weather applications.