Climate Prediction via Matrix Completion

Mahsa Ghafarianzadeh, Claire Monteleoni

Abstract: Recently, machine learning has been applied to the problem of predicting future climates, informed by the multimodel ensemble of physics-based climate models that inform the Intergovernmental Panel on Climate Change (IPCC). Past work (Monteleoni et al., 2011, McQuade and Monteleoni, 2012) demonstrated the promise of online learning algorithms applied to this problem. Here we propose a novel approach, using sparse matrix completion.