MIST

By Sandra Chapman (University of Warwick), A. M. Bendito Nunes (undergraduate student, University of Warwick), and J. Gamper (undergraduate student, University of Warwick)

Space weather can have significant impact over a wide range of technological systems including power grids, aviation, satellites and communications. In common with studies across the geophysical sciences, space weather modelling and prediction requires long term space and ground-based parameters and indices that necessarily aggregate multiple observations, the details of which can change with time. The Newcomb-Benford law (NBL) specifies the relative occurrence rates of the leading digit in a sequence of numbers arising from multiple operations under certain conditions, the first non-zero digit in a number is more likely to be 1 than 2, 2 than 3, and so on. In this first application to space weather parameters and indices, we show that the NBL can detect changes in the instrumentation and calibration underlying long-term geophysical records, solely from the processed data records. In space weather, as in other fields such as climate change, it is critical to be able to verify that any observed secular change is not a result of changes in how the data record is constructed. As composite indices are becoming more widespread across the geosciences, the NBL may provide a generic data flag indicating changes in the constituent raw data, calibration or sampling method.

Figure 1: The plot shows the NBL goodness of fit parameter for magnetic field observed since 1981 by a series of satellites upstream of the earth. The NBL fit parameter shows a clear decrease when more sophisticated satellites, Wind. and later ACE, became available. 

The joint 1^st authors of this paper contributed to this research during their final year undergraduate Physics project at Warwick University

See paper for full details: 

A. M. Benedito Nunes, J. Gamper, S. C. Chapman, M. Friel, J. Gjerloev, Newcomb-Benford Law as a generic flag for changes in the derivation of long-term solar terrestrial physics timeseries, RAS Techniques and Instruments (2023) https://doi.org/10.1093/rasti/rzad041

By Sachin Reddy (UCL Mullard Space Science Laboratory)

In the nightside ionosphere, plumes of low-density plasma known as Equatorial Plasma Bubbles (EPBs) are prone to form. EPBs can disrupt GNSS signals which depend on quiet ionospheric conditions, but the day-to-day variability of bubbles has made predicting them a considerable challenge. In this study we present AI Prediction of EPBs (APE), a machine learning model that accurately predicts the Ionospheric Bubble Index (IBI) on Swarm. IBI identifies EPBs by correlating (R2) a simultaneous change in the current density and magnetic field.

APE is XGBoost regressor that is trained on data from 2014-2022. It performs well across all metrics, exhibiting a skill, association, and root mean squared error score of 0.96/1, 0.98/1 and 0.08/0 respectively. APE performs best post-sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods.

Shapley Value analysis reveals that F10.7 is the most important feature, whilst latitude is the least. Bespoke indices may be required to fully capture the effects of geomagnetic activity which is known to both enhance and suppress EPB formation. The Shapley analysis also reveals that low solar activity, active geomagnetic conditions, and the Earth-Sun perihelion all contribute to an increased EPB likelihood. To the best of our knowledge, this is the first time this exact combination of features has been linked to bubble detection. This showcases the ability of Shapley values to enable new insights into EPB climatology and predictability.

See full paper for details: Reddy, S. A., Forsyth, C., Aruliah, A., Smith, A., Bortnik, J., Aa, E., et al. (2023). Predicting swarm equatorial plasma bubbles via machine learning and Shapley values. Journal of Geophysical Research: Space Physics, 128, e2022JA031183. https://doi.org/10.1029/2022JA031183