Data-Driven Reduced-Order Model for Atmospheric CO2 Dispersion

Pedro Roberto Barbosa Rocha (IBM Research), Marcos Sebastião de Paula Gomes (Pontifical Catholic University of Rio de Janeiro), João Lucas de Sousa Almeida (IBM Research), Allan Moreira Carvalho (IBM Research) and Alberto Costa Nogueira Junior (IBM Research)

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Machine learning frameworks have emerged as powerful tools for the enhancement of computational fluid dynamics simulations and the construction of reduced-order models (ROMs). The latter are particularly desired when their full-order counterparts portray multiple spatiotemporal features and demand high processing power and storage capacity, such as climate models. In this work, a ROM for CO2 dispersion across Earth‘s atmosphere was built from NASA’s gridded daily OCO-2 carbon dioxide assimilated dataset. For that, a proper orthogonal decomposition was performed, followed by a non-intrusive operator inference (OpInf). This scientific machine learning technique was capable of accurately representing and predicting the detailed CO2 concentration field for about one year ahead, with a normalized root-mean-square error below 5%. It suggests OpInf-based ROMs may be a reliable alternative for fast response climate-related predictions.