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

arXiv: https://arxiv.org/abs/2505.15934 · HTML: https://mlsanity.com/paper/2505.15934/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 58 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 5 of 5)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [Deep Factors for Forecasting](https://mlsanity.com/paper/1905.12417/) (2019)
- [Notes on the Behavior of MC Dropout](https://mlsanity.com/paper/2008.02627/) (2020)
- [Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances](https://mlsanity.com/paper/2006.07972/) (2020)

## Abstract (arXiv)

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.