Enable DTW-based alignment for AE index time series with high temporal variability (September 4–6, 2017 event)

Develop a sequence alignment approach capable of handling the high temporal variability of AE index time series for the 4–6 September 2017 geomagnetic storm so that dynamic time warping (DTW) or an adapted method can be applied to quantitatively assess model performance against observations.

Background

The paper uses dynamic time warping (DTW) to assess the similarity between modeled and observed Dst time series and introduces a skill metric (SSF). While DTW was effective for Dst, the authors report that the AE index exhibits strong temporal variability, preventing sequence alignment for the September 2017 event.

A robust alignment approach for AE would enable quantitative evaluation of ionospheric substorm dynamics in global simulations, complementing Dst-based assessments and improving the validation of coupled EUHFORIA–OpenGGCM predictions.