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 (
This study develops a new Bubble Index to quantify the intensity of 2‐D postsunset equatorial plasma bubbles (EPBs) in the American/Atlantic sector, using Global‐scale Observations of the Limb and Disk (GOLD) nighttime data. A climatology and day‐to‐day variability analysis of EPBs is conducted based on the newly‐derived Bubble Index with the following results: (a) EPBs show considerable seasonal and solar activity dependence, with stronger (weaker) intensity around December (June) solstice and high (low) solar activity years. (b) EPBs exhibit opposite geomagnetic activity dependencies during different storm phases: EPBs are intensified concurrently with an increasing Kp, but are suppressed with high Kp occurring 3–6 hr earlier. (c) For the first time, we found that EPBs' day‐to‐day variation exhibited quasi‐3‐day and quasi‐6‐day periods. A coordinated analysis of Ionospheric Connection Explorer (ICON) winds and ionosonde data suggests that this multi‐day periodicity was related to the planetary wave modulation through the wind‐driven dynamo.
more » « less- PAR ID:
- 10409245
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 50
- Issue:
- 8
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract R 2) 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 EPBR 2(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. -
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