New machine learning tool can improve space weather forecasts, understanding of solar data.
Computers can learn to find solar flares and other events in vast streams of solar images and help NOAA forecasters issue timely alerts, according to a new study. The machine-learning technique, developed by scientists at CIRES and NOAA’s National Centers for Environmental Information (NCEI), searches massive amounts of satellite data to pick out features significant for space weather. Changing conditions on the Sun and in space can affect various technologies on Earth, blocking radio communications, damaging power grids and diminishing navigation system accuracy.
“Being able to process solar data in real time is important because flares erupting on the Sun impact Earth over the course of minutes. These techniques provide a rapid, continuously updated overview of solar features and can point us to areas requiring more scrutiny,” said Rob Steenburgh, a forecaster in the NOAA Space Weather Prediction Center (SWPC) in Boulder.
The research was published in October in the Journal of Space Weather and Space Climate.
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