A deep learning approach to detecting storms on Saturn is set to transform our understanding of planetary atmospheres, according to UCL and University of Arizona researchers.
The new technique called PlanetNet identifies and maps the components and features in turbulent regions of Saturn's atmosphere, giving insights into the processes that drive them.
A study, published today in Nature Astronomy , provides results from the first demonstration of the PlanetNet algorithm, which clearly shows the fixed regions affected by storms, and that Saturn's dark storm clouds contain material swept up from the lower atmosphere at strong vertical winds.
Developed by UCL and the University of Arizona researchers, PlanetNet was trained and tested using infrared data from the Visible and Infrared Mapping Spectrometer (VIMS) instrument on Cassini, a joint mission between NASA, the European Space Agency and the Italian Space Agency.
A dataset containing multiple , adjacent to storms observed at Saturn in February 2008 was chosen to provide a range of complex atmospheric features to challenge PlanetNet's capabilities.  Previous analysis of the dataset indicated by a rare detection of ammonia in Saturn's atmosphere, in the form of an S-shaped cloud
The map produced through PlanetNet shows that this feature is a prominent part of a much larger upwelling or ammonia ice clouds around a central dark storm. PlanetNet identifies similar upwelling around another storm, suggesting that such features are quite common.
The map also shows pronounced differences between the center of storms and the surrounding areas, indicating that the eye gives a clear view into the warmer, deep atmosphere
"Missions like Cassini gather enormous amounts of data but classical techniques for analysis of drawbacks, either in the accuracy of information that can be extracted or in the time they take to perform. sets, "said Dr. Ingo Waldmann (UCL Physics & Astronomy), lead author and deputy director of the UCL Center for Space and Exoplanet Data.
"This gives us the potential to analyze atmospheric phenomena over large areas and from different viewing angles, and to make new associations between the features and the chemical and physical properties that create them. "
Initially, PlanetNet searches the data for signs of clustering in the cloud structure and gas composition. For areas of interest, it trims the data to remove uncertainties at the edges and runs a parallel analysis of the spectral and spatial properties. Recombination of the two data streams, PlanetNet creates a map that presents quickly and accurately the major components of Saturn's storms with unprecedented precision.
PlanetNet's accuracy has been validated on Cassini data not included in the training phase. The whole dataset has also been rotated and resampled to create synthetic data for further testing. PlanetNet has achieved over 90% accuracy in both test cases.
"PlanetNet enables us to analyze much larger volumes of data, and this gives insights into the large-scale dynamics of Saturn," said Professor Caitlin Griffith (University of Arizona ), who co-authored this paper. "The results reveal atmospheric features that were previously undetected. PlanetNet can easily be adapted to other datasets and planets, making it an invaluable potential tool for many future missions."
Image: Infrared Saturn clouds
Mapping Saturn using deep learning, Nature Astronomy (2019). DOI: 10.1038 / s41550-019-0753-8, https://www.nature.com/articles/s41550-019-0753-8
Deep learning takes Saturn by storm (2019, April 29)
retrieved April 29, 2019
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.