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Forecasting GICs and geoelectric fields from solar wind data using LSTMs: application in Austria
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  • Rachel L. Bailey,
  • Roman Leonhardt,
  • Christian Moestl,
  • Ciaran Beggan,
  • Martin Reiss,
  • Ankush Bhaskar,
  • Andreas Jeffrey Weiss
Rachel L. Bailey
ZAMG, ZAMG

Corresponding Author:r.bailey@zamg.ac.at

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Roman Leonhardt
ZAMG, ZAMG
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Christian Moestl
Austrian Academy of Sciences, Austrian Academy of Sciences
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Ciaran Beggan
British Geological Survey Edinburgh Office, British Geological Survey Edinburgh Office
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Martin Reiss
Austrian Academy of Sciences, Austrian Academy of Sciences
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Ankush Bhaskar
ISRO/Vikram Sarabhai Space Centre, ISRO/Vikram Sarabhai Space Centre
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Andreas Jeffrey Weiss
Space Research Institute, Space Research Institute
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Abstract

The forecasting of local GIC effects has largely relied on the forecasting of dB/dt as a proxy and, to date, little attention has been paid to directly forecasting the geoelectric field or GICs themselves. We approach this problem with machine learning tools, specifically recurrent neural networks or LSTMs by taking solar wind observations as input and training the models to predict two different kinds of output: first, the geoelectric field components Ex and Ey; and second, the GICs in specific substations in Austria. The training is carried out on the geoelectric field and GICs modelled from 26 years of one-minute geomagnetic field measurements, and results are compared to GIC measurements from recent years. The GICs are generally predicted better by an LSTM trained on values from a specific substation, but only a fraction of the largest GICs are correctly predicted. This model had a correlation with measurements of around 0.6, and a root-mean-square error of 0.7 A. The probability of detecting mild activity in GICs is around 50%, and 15% for larger GICs.
Mar 2022Published in Space Weather volume 20 issue 3. 10.1029/2021SW002907