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Abstract We analyze three substorms that occur on (1) 9 March 2008 05:14 UT, (2) 26 February 2008 04:05 UT, and (3) 26 February 2008 04:55 UT. Using ACE solar wind velocity and interplanetary magnetic fieldBzvalues, we calculate the rectified (southwardBz) solar wind voltage propagated to the magnetosphere. The solar wind conditions for the two events were vastly different, 300 kV for 9 March 2008 substorm, compared to 50 kV for 26 February 2008. The voltage is input to a nonlinear physics‐based model of the magnetosphere called WINDMI. The output is the westward auroral electrojet current which is proportional to the auroral electrojet (AL) index from World Data Center for Geomagnetism Kyoto and the SuperMAG auroral electrojet index (SML). Substorm onset times are obtained from the superMAG substorm database, Pu et al. (2010,https://doi.org/10.1029/2009JA014217), Lui (2011,https://doi.org/10.1029/2010JA016078) and synchronized to Time History of Events and Macroscale Interactions during Substorms satellite data. The timing of onset, model parameters, and intermediate state space variables are analyzed. The model onsets occurred about 5 to 10 min earlier than the reported onsets. Onsets occurred when the geotail current in the WINDMI model reached a critical threshold of 6.2 MA for the 9 March 2008 event, while, in contrast, a critical threshold of 2.1 MA was obtained for the two 26 February 2008 events. The model estimates 1.99 PJ of total energy transfer during the 9 March 2008 event, with 0.95 PJ deposited in the ionosphere. The smaller events on 26 February 2008 resulted in a total energy transfer of 0.37 PJ according to the model, with 0.095 PJ deposited in the ionosphere.
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Imbalanced Regression Artificial Neural Network Model for Auroral Electrojet Indices (IRANNA): Can We Predict Strong Events?
Abstract We develop an Imbalanced Regression Artificial Neural Network model for the Auroral electrojet index (IRANNA) to predict the SuperMAG SML index, addressing the heavily imbalanced distribution of the SML data set. The data set contains mostly quiet‐time values of lesser importance and very few strong‐to‐extreme values of interest, such as those associated with super substorms. Traditional prediction models, which minimize mean squared error uniformly across the whole data set, are often skewed by this imbalance, prioritizing the lower, quiet‐time values and consequently underestimating strong geomagnetic events. The IRANNA model addresses this issue by using a customized weighting scheme in the loss function, enabling it to predict strong‐to‐extreme events accurately for the first time. The model takes solar wind parameters as inputs and predicts the logarithm of the absolute SML values. It does not rely on past values of the SML index, differentiating it from other models that use historical data for prediction. The model has demonstrated its ability to predict the peak amplitudes of strong‐to‐extreme events across various statistical analyses, event studies, and virtual experiments. Despite this success, challenges remain, particularly during localized electrojet events and when upstream solar wind data propagation is unreliable. This study emphasizes the importance of using imbalanced regression techniques, especially in space physics, where data sets are inherently skewed. It also highlights the potential of the IRANNA model to provide valuable insights into the magnetosphere's response to solar wind driving, improving space weather forecasting and offering new tools for investigating magnetospheric dynamics.
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- Award ID(s):
- 2247256
- PAR ID:
- 10591962
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Space Weather
- Volume:
- 23
- Issue:
- 5
- ISSN:
- 1542-7390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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