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); Alberto Costa Nogueira Junior (IBM Research)
Abstract
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.