Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder

High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes. These interactions often manifest as aurora in the form of visible light in the Earth's ionosphere. These interactions also result in irregularities in the electron density, which cause disruptions in the amplitude and phase of the radio signals from the Global Navigation Satellite Systems (GNSS), known as 'scintillation'. In this paper we use a multi-scale residual autoencoder (Res-AE) to show the correlation between specific dynamic structures of the aurora and the magnitude of the GNSS phase scintillations ($\sigma_{\phi}$). Auroral images are encoded in a lower dimensional feature space using the Res-AE, which in turn are clustered with t-SNE and UMAP. Both methods produce similar clusters, and specific clusters demonstrate greater correlations with observed phase scintillations. Our results suggest that specific dynamic structures of auroras are highly correlated with GNSS phase scintillations.

[1]  Michael T. Rietveld,et al.  Severe and localized GNSS scintillation at the poleward edge of the nightside auroral oval during intense substorm aurora , 2015 .

[2]  Hannes Nickisch,et al.  Automatic Classification of Auroral Images From the Oslo Auroral THEMIS (OATH) Data Set Using Machine Learning , 2018, Journal of Geophysical Research: Space Physics.

[3]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[4]  J. Moen,et al.  Statistical study of the GNSS phase scintillation associated with two types of auroral blobs , 2016 .

[5]  Harald U. Frey,et al.  The THEMIS Array of Ground-based Observatories for the Study of Auroral Substorms , 2008 .

[6]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[7]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[8]  Dacheng Tao,et al.  Extracting Auroral Key Local Structures From All‐Sky Auroral Images by Artificial Intelligence Technique , 2019, Journal of Geophysical Research: Space Physics.

[9]  Marcio Aquino,et al.  Climatology of GPS ionospheric scintillations over high and mid-latitude European regions , 2009 .

[10]  Bin Song,et al.  BoSR: A CNN-based aurora image retrieval method , 2019, Neural Networks.

[11]  J. Moen,et al.  GPS scintillation effects associated with polar cap patches and substorm auroral activity: direct comparison , 2014 .

[12]  Kan Liou,et al.  Global Positioning System phase fluctuations and ultraviolet images from the Polar satellite , 2000 .

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Scintillation and loss of signal lock from poleward moving auroral forms in the cusp ionosphere , 2015, 1606.02654.