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- Title
Toward GIC Forecasting: Statistical Downscaling of the Geomagnetic Field to Improve Geoelectric Field Forecasts.
- Authors
Haines, C.; Owens, M. J.; Barnard, L.; Lockwood, M.; Beggan, C. D.; Thomson, A. W. P.; Rogers, N. C.
- Abstract
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 for 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‐hr timescale to 1‐min resolution, with the hope of improving subsequent geoelectric field magnitude estimates. An analog ensemble (AnEn) approach is used to select similar hourly averages in a historical data set, 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. Plain Language Summary: Forecasting space weather impacts on ground‐based systems, such as power grids, requires the use of computer simulations of the disturbance of the Earth's magnetic field by the solar wind. However, these computer simulations are often too smooth, underestimating small and fast variations in the Earth's magnetic field, which are important for modeling induction hazards that may affect power grids. In this paper we present a proof‐of‐concept scheme that attempts to introduce realistic high‐frequency variations using the idea of looking at how the field has previously behaved in historical events. We test the model and find that it allows for better impact forecasting than if our scheme is not used. Key Points: Operational global MHD models do not fully capture the ground‐level magnetic field variability important for modeling induction hazardsWe provide a proof of concept model to statistically introduce realistic, high‐resolution perturbations with which to drive an impacts modelOur downscaling scheme outperforms a reference linear‐interpolation approach under a range of metrics
- Subjects
GEOPHYSICAL prediction; DOWNSCALING (Climatology); GEOMAGNETISM; MAGNETOSPHERIC currents; ELECTRIC power distribution; ELECTRICAL conductors; SOLAR wind; SPACE environment
- Publication
Space Weather: The International Journal of Research & Applications, 2022, Vol 20, Issue 1, p1
- ISSN
1539-4956
- Publication type
Article
- DOI
10.1029/2021SW002903