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A new "deep learning" algorithm that could help scientists better understand planetary atmospheres has passed its first big test, a new study reports.
The software, called PlanetNet, mapped out a monster 2008 Saturn storm system in detail using data gathered by NASA's Cassini spacecraft, which studied the ringed planet up close from 2004 through 2017.
Missions - Cassini - Amounts - Data - Techniques
"Missions like Cassini gather enormous amounts of data, but classical techniques for analysis have drawbacks, either in the accuracy of information that can be extracted or in the time they take to perform. Deep learning enables pattern recognition across diverse, multiple data sets," study co-lead author Ingo Waldmann, deputy director of the Center for Space and Exoplanet Data at University College London in England, said in a statement.
Cloud distribution as mapped by the PlanetNet algorithm across six overlapping data sets. The stormy region feature (blue) occurs in the vicinity of dark storms (purple/green) in contrast to the unperturbed regions (red/orange). The area covered by the multiple-storm system is equivalent to about 70% of the Earth's surface.
Phenomena - Areas - Angles - Associations - Shape
"This gives us the potential to analyze atmospheric phenomena over large areas and from different viewing angles, and to make new associations between the shape of features and the chemical and physical properties that create them," Waldmann added.
PlanetNet searches data sets for evidence of "clustering" in cloud structure and atmospheric composition, then uses such information to generate precise maps. Waldmann and study co-leader Caitlin Griffith, of the University of Arizona's Lunar and Planetary...
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