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

• URL: http://arxiv.org/abs/2505.15934v1
• Date: Wed, 21 May 2025 18:40:40 GMT
• Title: Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
• Authors: Haoyuan Chen, Emil Constantinescu, Vishwas Rao, Cristiana Stan,
• Abstract summary: The Madden--Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns.<n>Most machine learning algorithms, such as neural networks, cannot provide the uncertainty levels in the MJO forecasts directly.
• Score: 0.8749675983608172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 ANN models for the first five lead days. Additionally, our posteriori covariance correction extends the probabilistic coverage by more than three weeks.

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