@misc{tibau2018supernovae2,
    type = {misc},
    key = {tibau2018supernovae2},
    title = {SupernoVAE: Using deep learning to find spatio-temporal dynamics in Earth system data},
    author = {Xavier-Andoni Tibau and Christian Requena-Mesa and Christian Reimers and Joachim Denzler and Veronika Eyring and Markus Reichstein and Jakob Runge},
    howpublished = {American Geophysical Union Fall Meeting (AGU): Abstract + Poster Presentation},
    year = {2018},
    abstract = {Exploring and understanding spatio-temporal patterns on Earth system datasets is one of the principal goals of the climate and geo-science communities. In this direction, Empirical Orthogonal Functions (EOFs) have been used to characterize phenomena such as the El Nino Southern Oscillation, the Arctic jet stream or the Indian Monsoon. However, EOF analysis has several limitations, for example, it can only identify linear and orthogonal patterns. We present a framework that makes use of a convolutional variational autoencoder (VAE) as a learnable feature function to extract spatio-temporal dynamics via PCA. The VAE encodes the information in an abstract space of higher order features representing different patterns. Over this space, PCA is performed to obtain a spatial representation of related temporal dynamics. We have used three datasets, two artificial datasets where the dynamics are ruled by a hidden spatially varying parameter and an observational reanalysis dataset of monthly sea surface temperature from 1898 to 2014. The artificial datasets have chaotic and, chaotic and stochastic dynamics depending on the spatial hidden parameter. As baseline methods, EOF analysis and Kernel PCA were performed over the original spaces. For the two artificial datasets, we found a high correlation between some of the first Principal Components on the feature space and the spatial hidden parameter. This correlation was not found using baseline methods in the original space. In the reanalysis dataset, the method was able to find known modes, such as ENSO, as well as other patterns that baseline methods did not reveal that might have inmmediate effect on how we understand the earth system after expert interpretation. These results provide a proof of concept: SupernoVAE is not only able to extract well-known climate patterns previously characterized with linear EOF analysis, but also allows to extract non-linear and non-orthogonal patterns that can help in analyzing Earth system dynamics that could not be characterized before.},
    url = {https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/403663},
}