2026/06/15 by Cara L. Waters, Christopher H. K. Chen, Mathew J. Owens +1 · 1 voice
Physics and Astronomy · Engineering · #Ionosphere and magnetosphere dynamics #Solar and Space Plasma Dynamics #GNSS positioning and interference
paper · pdf · doi:10.1029/2026sw005094
Abstract The auroral electrojet (AE) index is a key indicator of high latitude geomagnetic activity and is widely used in operational space weather monitoring, yet forecasting AE from upstream solar wind conditions remains challenging due to nonlinear coupling, internal magnetospheric dynamics, and multiscale variability. We test whether incorporating solar wind turbulence improves short timescale AE forecasts beyond models based only on mean solar wind and interplanetary magnetic field parameters. Two gradient boosted decision tree (XGBoost) models are developed using near‐Earth solar wind observations: a baseline model using standard mean parameters and a turbulence‐aware model that additionally includes measures of fluctuation amplitude, intermittency, and Alfvénic structure. Both models achieve peak performance at short lead times, with correlations exceeding 0.8 at 60 min. However, while the baseline model exhibits a clear skill peak at 75 min, the turbulence‐aware model maintains comparable skill across 60–90 min horizons, indicating reduced degradation with lead time. The turbulence‐aware model also provides consistent improvements over both the baseline and persistence and, critically, improves forecast robustness for high‐impact events. Cost–loss analysis shows that, for the baseline model, economic value decreases systematically with increasing AE threshold and the range of cost–loss ratios yielding positive value narrows. In contrast, the turbulence‐aware model maintains an approximately constant zero‐value cost–loss threshold across all event levels, indicating stable economic usefulness even for extreme AE conditions. This demonstrates that turbulence provides complementary, scale‐dependent information beyond mean solar wind parameters, improving both forecast performance and decision‐relevant value for operational space weather applications.