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Towards GIC forecasting: Statistical downscaling of the geomagnetic field to improve geoelectric field forecasts

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  • Carl Haines
  • M Owens
  • Luke Barnard
  • Mike Lockwood
  • Ciarán D. Beggan
  • A.W.P. Thomson
  • Neil Rogers
Article numbere2021SW002903
<mark>Journal publication date</mark>31/01/2022
<mark>Journal</mark>Space Weather
Issue number1
Publication StatusPublished
Early online date11/11/21
<mark>Original language</mark>English


Geomagnetically induced currents (GICs) are an impact of space weather that can occur during periods of enhanced geomagnetic activity. GICs can enter into electrical power grids through earthed conductors, potentially causing network collapse through voltage instability or damaging transformers. It would be beneficial to power grid operators to have a forecast of GICs that could inform decision making on mitigating action. Long lead-time GIC forecasting requires magnetospheric models as drivers of geoelectric field models. However, estimation of the geoelectric field is sensitive to high-frequency geomagnetic field variations which operational global magneto-hydrodynamic models do not fully capture. Furthermore, an assessment of GIC forecast uncertainty would require a large ensemble of magnetospheric runs, which is computationally expensive. One solution that is widely used in climate science is “downscaling”, wherein sub-grid variations are added to model outputs on a statistical basis. We present proof-of-concept results for a method that temporally downscales low-resolution magnetic field data on a 1-hour timescale to 1-minute resolution, with the hope of improving subsequent geoelectric field magnitude estimates. An analogue ensemble (AnEn) approach is used to select similar hourly averages in a historical dataset, from which we separate the high-resolution perturbations to add to the hourly average values. We find that AnEn outperforms the benchmark linear-interpolation approach in its ability to accurately drive an impacts model, suggesting GIC forecasting would be improved. We evaluated the ability of AnEn to predict extreme events using the FSS, HSS, cost/loss analysis and BSS, finding that AnEn outperforms the “do-nothing” approach.