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 le...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-05-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023MS004188 |
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| author | Haoyuan Chen Emil Constantinescu Vishwas Rao Cristiana Stan |
| author_facet | Haoyuan Chen Emil Constantinescu Vishwas Rao Cristiana Stan |
| author_sort | Haoyuan Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-44df9ddbd407418da50ed7226e5c7b57 |
| institution | Kabale University |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-44df9ddbd407418da50ed7226e5c7b572025-08-20T03:47:57ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-05-01175n/an/a10.1029/2023MS004188Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process ModelsHaoyuan Chen0Emil Constantinescu1Vishwas Rao2Cristiana Stan3Department of Industrial and Systems Engineering Texas A&M University College Station TX USAMathematics and Computer Science Division, Argonne National Laboratory Lemont IL USAMathematics and Computer Science Division, Argonne National Laboratory Lemont IL USADepartment of Atmospheric Oceanic and Earth Sciences George Mason University Fairfax VA USAAbstract 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.https://doi.org/10.1029/2023MS004188Madden–Julian oscillationGaussian process modelsprobabilistic forecastinguncertainty quantificationsubseasonal‐to‐seasonal prediction |
| spellingShingle | Haoyuan Chen Emil Constantinescu Vishwas Rao Cristiana Stan Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models Journal of Advances in Modeling Earth Systems Madden–Julian oscillation Gaussian process models probabilistic forecasting uncertainty quantification subseasonal‐to‐seasonal prediction |
| title | Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models |
| title_full | Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models |
| title_fullStr | Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models |
| title_full_unstemmed | Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models |
| title_short | Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models |
| title_sort | improving the predictability of the madden julian oscillation at subseasonal scales with gaussian process models |
| topic | Madden–Julian oscillation Gaussian process models probabilistic forecasting uncertainty quantification subseasonal‐to‐seasonal prediction |
| url | https://doi.org/10.1029/2023MS004188 |
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