Machine learning for predicting the Bz magnetic field component from upstream in situ observations of solar coronal mass ejections

Reiss, Martin A.; Möstl, Christian; Bailey, Rachel L.; Rüdisser, Hannah T.; Amerstorfer, Ute V.; Amerstorfer, Tanja; Weiss, Andreas J.; Hinterreiter, Jürgen; Windisch, Andreas

doi:10.48550/arxiv.2108.04067

Cited by 2 publications

(2 citation statements)

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“…A lower-resolution version of the LSTM-E model will be coupled with the PREDSTORM solar wind forecast (Bailey et al, 2020), which provides forecasts of the ambient solar wind a few days in advance, based on either a recurrence model or data from a spacecraft east of the Sun-Earth line such as STEREO or a future mission to the Lagrange 5 point. We also plan in the future to integrate methods on solar wind B z forecasting (Reiss et al, 2021) or CME flux rope modeling (Weiss et al, 2021) to advance our capabilities in GIC forecasting for any type of solar wind structures.…”

Section: Discussionmentioning

confidence: 99%

Forecasting GICs and Geoelectric Fields From Solar Wind Data Using LSTMs: Application in Austria

et al. 2022

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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 modeled 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 has 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.

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Section: Discussionmentioning

confidence: 99%

Forecasting GICs and Geoelectric Fields From Solar Wind Data Using LSTMs: Application in Austria

et al. 2022

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“…A lower-resolution version of the LSTM-E model will be coupled with the PRED-STORM solar wind forecast (Bailey et al, 2020), which provides forecasts of the ambient solar wind a few days in advance, based on either a recurrence model or data from a spacecraft east of the Sun-Earth line such as STEREO or a future mission to the Lagrange 5 point. We also plan in the future to integrate methods on solar wind B z forecasting (Reiss et al, 2021) or CME flux rope modelling (Weiss et al, 2021) to advance our capabilities in GIC forecasting for any type of solar wind structures.…”

Section: Discussionmentioning

confidence: 99%

Forecasting GICs and geoelectric fields from solar wind data using LSTMs: application in Austria

Bailey,

Leonhardt,

Möstl

et al. 2021

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Machine learning for predicting the Bz magnetic field component from upstream in situ observations of solar coronal mass ejections

Abstract: We hypothesize that upstream in situ measurements are sufficient to predict B z in solar coronal mass ejections.• We present a predictive tool that forecasts the minimum of B z in an ICME with a MAE of 3.12 nT and a PCC of 0.71.

Cited by 2 publications

References 41 publications

Forecasting GICs and Geoelectric Fields From Solar Wind Data Using LSTMs: Application in Austria

Forecasting GICs and Geoelectric Fields From Solar Wind Data Using LSTMs: Application in Austria

Forecasting GICs and geoelectric fields from solar wind data using LSTMs: application in Austria

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