Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales With Gaussian Process Models

Abstract

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 and we propose a posteriori covariance correction. Numerical experiments demonstrate that our model has better prediction skills than the artificial neural network models for the first five lead days. Additionally, our posteriori covariance correction extends the probabilistic coverage by more than 3 weeks.