LiveWire: Horizontal Geoelectric Field Prediction With 1‐hr Lead‐Time Using Multi‐Fidelity Boosted Neural Networks

Abstract Geomagnetically Induced Currents (GICs) are electrical currents generated by rapid changes in the geomagnetic field during space weather events, posing risks to power grids and pipelines. Traditional approaches predict GICs indirectly by forecasting , the temporal variation of the geomagnetic field, which is proportional to the induced electric field via Faraday's law. However, current physics‐based models driven by in situ solar wind measurements offer only 10–30 min lead times, insufficient for power grid operators to take mitigating actions. Additionally, grid operators prefer direct forecasts of the geoelectric field, which directly influences GICs, rather than relying on intermediate predictions of that require complex and time‐consuming calculations. We present a novel approach that directly forecasts the horizontal geoelectric field with a one‐hour lead time, bypassing predictions. Our method combines magnetometer data, magnetotelluric survey data, and solar wind inputs into a new probabilistic multi‐fidelity machine learning technique, ProBoost, resulting in the LiveWire model. Using data from the Boulder Geomagnetic Observatory (BOU) since 2002, we trained and validated LiveWire on the top 50 geoelectric field events during geomagnetic storms. Our results show that LiveWire outperforms both a persistence forecast and the operational Space Weather Modeling Framework (SWMF) by at least 31% and 23%, respectively. This advancement in geoelectric field forecasting promises more accurate GIC predictions, helping enhance the resilience of critical infrastructure to space weather.