Uncertainty Quantification of the Madden–Julian Oscillation with Gaussian Processes (Papers Track)

Haoyuan Chen (Texas A&M University); Emil Constantinescu (Argonne National Laboratory); Vishwas Rao (Argonne National Laboratory); Cristiana Stan (George Mason University)

Paper PDF Poster File Recorded Talk NeurIPS 2023 Poster Cite
Climate Science & Modeling Uncertainty Quantification & Robustness


The Madden–Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns. In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty levels in the MJO forecasts directly. To address this problem, we develop a nonparametric strategy based on Gaussian process (GP) models. We calibrate GPs using empirical correlations. Furthermore, we propose a posteriori covariance correction that extends the probabilistic coverage by more than three weeks.

Recorded Talk (direct link)