A couple of months ago, the sun sported the largest sunspot we’ve seen in the last 24 years.
This monstrous spot, visible to the naked eye (that is, without magnification, but with protective eyewear of course), launched more than 100 flares.
The number of the spots on the sun ebbs and flows cyclically, every 11 years. Right now, the sun is in the most active part of this cycle: we’re expecting lots of spots and lots of flares in the coming months.
Usually, the media focuses on the destructive power of solar flares — the chance that, one day, a huge explosion on the sun will fling a ton of energetic particles our way and fry our communication satellites. But there’s less coverage on how we forecast these things, like the weather, so that we can prevent any potential damage.
How do you forecast a solar flare, anyway?
One way is to use machine learning programs, which are a type of artificial intelligence that learns automatically from experience. These algorithms gradually improve their mathematical models every time new data come in.
In order to learn properly, however, the algorithms require large sums of data. Scientists lacked any solar data on this scale before the 2010 launch of the Solar Dynamics Observatory (SDO), a sun-watching satellite that downlinks about a terabyte and a half of data every day—more than the most data of any other satellite in NASA history.
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