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C. C. Balch

Publications and source records attributed to C. C. Balch.

2 recordsLinked to original sources

GIC--Related Observations During the May 2024 Geomagnetic Storm in the United States

The May 2024 geomagnetic storm was one of the most severe in the past 20~years. Understanding how large geomagnetic disturbances (GMDs) impact geomagnetically induced currents (GICs) within electrical power grid networks is key to ensuring their resilience. We have assembled and synthesized a large and unique set of GMD-related data, compared model predictions with measurements, and identified empirical relationships for GICs in the contiguous United States for this storm. Measurement data include GIC data from $47$ sites and magnetometer data from $17$ sites. Model data include GIC computed by the Tennessee Valley Authority (TVA) power system operators at $4$ sites, GIC computed using a reference model at $47$ sites, and the difference in the surface magnetic field from a baseline ($\Delta \mathbf{B}$) computed at $12$ magnetometer sites from three global magnetospheric models -- the Multiscale Atmosphere-Geospace Environment Model (MAGE), Space Weather Modeling Framework (SWMF), and Open Geospace General Circulation Model (OpenGGCM). GIC measured and computed by TVA had a correlation coefficient $\text{r}>0.8$ and a prediction efficiency between 0.4 and 0.7. The horizontal magnetic field perturbation from a baseline, $\Delta B_H$, computed by MAGE, SWMF, and OpenGGCM had a correlation r from $0.21$ to $0.65$. Two empirical relationships were considered: (1) how the correlation between measured GIC site pairs depended on differences in site separation distance, $\beta$ scaling factor (related to ground conductivity), and geomagnetic latitude; and (2) a regression model for the maximum $\mbox{GIC}$ magnitude at each site given the product of $\alpha$ (related to magnetic latitude) and $\beta$.

physics.space-ph

A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning

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 (GIC), which are electric currents { associated to} 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 NOAA Space Weather Prediction Center, with a boosted ensemble of classification trees. We discuss the problem of re-calibrating 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.

physics.space-ph