A Gray-Box Model for a Probabilistic Estimate of Regional Ground Magnetic Perturbations: Enhancing the NOAA Operational Geospace Model With Machine Learning

Camporeale, E.; Cash, M. D.; Singer, H. J.; Balch, C. C.; Huang, Z.; Toth, G.

doi:10.1029/2019JA027684

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A Gray-Box Model for a Probabilistic Estimate of Regional Ground Magnetic Perturbations: Enhancing the NOAA Operational Geospace Model With Machine Learning

2020
By Camporeale, E. ; Cash, M. D. ; Singer, H. J. ; ...
Source: Journal of Geophysical Research: Space Physics, 125, e2019JA027684

[PDF-2.89 MB]

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Journal Title:

Journal of Geophysical Research: Space Physics
Personal Author:

Camporeale, E. ; Cash, M. D. ; Singer, H. J. ; Balch, C. C. ; Huang, Z. ; ... More +

Camporeale, E. ; Cash, M. D. ; Singer, H. J. ; Balch, C. C. ; Huang, Z. ; Toth, G. Less -
NOAA Program & Office:

CIRES (Cooperative Institute for Research in Environmental Sciences) ; NWS (National Weather Service) ; SWPC (Space Weather Prediction Center)

CIRES (Cooperative Institute for Research in Environmental Sciences) ; NWS (National Weather Service) ; SWPC (Space Weather Prediction Center) Less -
Description:

We present a novel algorithm that predicts the probability that the time derivative of the horizontal component of the ground magnetic field dB/dt exceeds a specified threshold at a given location. This quantity provides important information that is physically relevant to geomagnetically induced currents (GICs), which are electric currents associated with sudden changes in the Earth's magnetic field due to space weather events. The model follows a “gray-box” approach by combining the output of a physics-based model with machine learning. Specifically, we combine the University of Michigan's Geospace model that is operational at the National Oceanic and Atmospheric Administration (NOAA) Space Weather Prediction Center, with a boosted ensemble of classification trees. We discuss the problem of recalibrating the output of the decision tree to obtain reliable probabilities. The performance of the model is assessed by typical metrics for probabilistic forecasts: Probability of Detection and False Detection, True Skill Statistic, Heidke Skill Score, and Receiver Operating Characteristic curve. We show that the ML-enhanced algorithm consistently improves all the metrics considered.
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Forecasting Geomagnetically Induced Currents Geospace Model Machine Learning
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Journal of Geophysical Research: Space Physics, 125, e2019JA027684
DOI:

https://doi.org/10.1029/2019JA027684
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Journal Article
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urn:sha256:7ea216a1236cc03c9ed8d17c9b68bebfdbcf3d0af3cc17863f12df5d6b00e94e
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A Gray-Box Model for a Probabilistic Estimate of Regional Ground Magnetic Perturbations: Enhancing the NOAA Operational Geospace Model With Machine Learning

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