Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908374829891584 |
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| author | Chen, Haoyuan Constantinescu, Emil Rao, Vishwas Stan, Cristiana |
| author_facet | Chen, Haoyuan Constantinescu, Emil Rao, Vishwas Stan, Cristiana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15934 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models Chen, Haoyuan Constantinescu, Emil Rao, Vishwas Stan, Cristiana Numerical Analysis Atmospheric and Oceanic Physics Machine Learning 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. |
| title | Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models |
| topic | Numerical Analysis Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2505.15934 |