Speaker
Description
Rapid fluctuations in GPS signal phase and amplitude—known as scintillation—degrade positioning accuracy in high-latitude regions. This work introduces the Canadian High Arctic Scintillation Model (CHASM), a Bayesian inference framework that predicts scintillation probability and intensity over the Canadian Arctic with high spatial and temporal accuracy under both quiet and disturbed geomagnetic conditions.
Using GPS observations from the Canadian High Arctic Ionospheric Network (CHAIN) spanning Solar Cycles 24 and 25 (2008–2024), we processed raw data to remove multipath effects, radio-frequency interference, and instrumental noise. Scintillation occurrence rates were quantified using σφ and S4 indices as functions of magnetic latitude and local time, revealing distinct spatiotemporal patterns. These climatological insights were statistically linked to key solar-terrestrial drivers such as F10.7 solar flux, solar wind parameters, interplanetary magnetic field orientation, and ground-based geomagnetic field variations.
The Bayesian approach first assesses the independence of predictor variables, then constructs prior probabilities for multinomial classification. Posterior probabilities are computed for arbitrary sets of predictors, enabling flexible forecasting. The model captures most variations seen in measured indices, whether associated with transient interplanetary events or background ionospheric conditions. Model accuracy is measured and demonstrated using cross-correlation analysis, yielding high-resolution forecasts of scintillation indices. This work advances the ability to forecast scintillation and mitigate its impact on navigation and timing systems in the Arctic region.
| Keyword-1 | Scintillation |
|---|---|
| Keyword-2 | Modelling |
| Keyword-3 | Ionosphere |