Tuesday, April 4, 2023

Global Geomagnetic Perturbation Forecasting Using Deep Learning

AGU:

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field, which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation.

Our model outperforms, or has consistent performance with state-of-the-practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.

Phys.org

Like a tornado siren for life-threatening storms in America's heartland, a new computer model that combines artificial intelligence (AI) and NASA satellite data could sound the alarm for dangerous space weather.

The model uses AI to analyze spacecraft measurements of the solar wind (an unrelenting stream of material from the sun) and predict where an impending solar storm will strike, anywhere on Earth, with 30 minutes of advance warning. This could provide just enough time to prepare for these storms and prevent severe impacts on power grids and other critical infrastructure.

To help prepare, an international team of researchers at the Frontier Development Lab—a public-private partnership that includes NASA, the U.S. Geological Survey, and the U.S. Department of Energy—have been using artificial intelligence (AI) to look for connections between the solar wind and geomagnetic disruptions, or perturbations, that cause havoc on our technology. The researchers applied an AI method called "deep learning," which trains computers to recognize patterns based on previous examples. They used this type of AI to identify relationships between solar wind measurements from heliophysics missions (including ACE, Wind, IMP-8, and Geotail) and geomagnetic perturbations observed at ground stations across the planet. 


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