In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft. IBI is a correlation (R2) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day‐to‐day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014 to 2022 at a resolution of 1s, and transform it from a time‐series into a 6‐dimensional space with a corresponding EPB R2 (0–1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model 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 values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset.
Spacecraft charging affects the accuracy of in-situ plasma measurements in space. We investigate the impact of spacecraft charging on upper thermospheric plasma measurements captured by a 2U CubeSat called Phoenix. Using the Spacecraft Plasma Interactions Software (SPIS), we simulate dayside surface potentials of − 0.6 V, and nightside potentials of − 0.2 V. We also observe this charging mechanism in the distribution function captured by the Ion and Neutral Mass Spectrometer (INMS) on-board Phoenix. Whilst negative charging in the dense ionosphere is known, the diurnal variation in density and temperature has resulted in dayside potentials that are smaller than at night. We apply charging corrections in accordance with Liouville’s theorem and employ a least-squares fitting routine to extract the plasma density, bulk speed, and temperature. Our routine returns densities that are within an order of magnitude of the benchmarks above, but they carry errors of at least 20%. All bulk speeds are greater than the expected range of 60–120 m/s and this could be due to insufficient charging corrections. Our parameterised ion temperatures are lower than our empirical benchmark but are in-line with other in-situ measurements. Temperatures are always improved when spacecraft charging corrections are applied. We mostly attribute the shortcomings of the findings to the ram-only capture mode of the INMS. Future work will improve the fitting routine and continue to cross-check with other in-flight data.
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