Solar flares can now be predicted, thanks to a new study
The Sun has truly woken up in 2023. Within the first few weeks of the new year, three X-class solar flares erupted on the Sun's surface. These powerful flares can affect Earth's magnetic field and damage satellites, communication equipment, and power grids.
It looks like scientists have had enough. A team from NorthWest Research Associates, or NWRA, used data from NASA's Solar Dynamics Observatory, or SDO, and found clues that could accurately predict when and where the Sun's next flare might explode, according to a release.
Small signals in the corona, the upper layers of the solar atmosphere, can identify the regions on the Sun plausible to produce the energetic bursts of light and particles released from the Sun.
The new findings are published in The Astrophysical Journal.
A new marker to distinguish which active regions can flare soon
What were the signals?
Above the regions about to flare, the corona produced small-scale flashes. This information is imperative - it could improve predictions of flares and space weather storms known to endanger astronauts, disrupt radio communications, and cause electrical blackouts.
In previous studies, scientists have noted how activity in lower layers of the Sun's atmosphere can herald impending flares in active regions. This new research adds more clarity to that picture.
"We can get some very different information in the corona than we get from the photosphere, or ‘surface’ of the Sun," KD Leka, lead author on the new study who is also a designated foreign professor at Nagoya University in Japan, said in a statement. "Our results may give us a new marker to distinguish which active regions are likely to flare soon and which will stay quiet over an upcoming period of time."
New tools to predict solar flares can be developed
The scientists used a newly created image database (described in a companion paper in The Astrophysical Journal) of the Sun’s active regions captured by SDO. The database mergers over eight years of images taken of active regions in ultraviolet and extreme-ultraviolet light, as per the release.
A large sample of active regions could be identified and studied from the database. The analysis revealed that small flashes in the corona preceded each flare.
The new insights will help researchers understand what happens in these magnetically active regions. New tools to predict solar flares can also be developed.
"Down the road, combining all this information from the surface up through the corona should allow forecasters to make better predictions about when and where solar flares will happen," said Karin Dissauer, a research scientist at NWRA.
A large sample of active-region-targeted time-series images from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA), the AIA Active Region Patch database (Paper I) is used to investigate whether parameters describing the coronal, transition region, and chromospheric emission can differentiate a region that will imminently produce a solar flare from one that will not. Parameterizations based on moment analysis of direct and running-difference images provide for physically interpretable results from nonparametric discriminant analysis. Across four event definitions including both 24 hr and 6 hr validity periods, 160 image-based parameters capture the general state of the atmosphere, rapid brightness changes, and longer-term intensity evolution. We find top Brier Skill Scores in the 0.07–0.33 range, True Skill Statistics in the 0.68–0.82 range (both depending on event definition), and Receiver Operating Characteristic Skill Scores above 0.8. Total emission can perform notably, as can steeply increasing or decreasing brightness, although mean brightness measures do not, demonstrating the well-known active-region size/flare productivity relation. Once a region is flare productive, the active-region coronal plasma appears to stay hot. The 94 Å filter data provide the most parameters with discriminating power, with indications that it benefits from sampling multiple physical regimes. In particular, classification success using higher-order moments of running-difference images indicate a propensity for flare-imminent regions to display short-lived small-scale brightening events. Parameters describing the evolution of the corona can provide flare-imminent indicators, but at no preference over "static" parameters. Finally, all parameters and NPDA-derived probabilities are available to the community for additional research.
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