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Impacts of tropical forecast errors on two extreme precipitation events: insights from relaxation experiments using machine-learning weather prediction models
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DOI：10.5194/wcd-7-787-2026.png)
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Abstract
En 中文
Abstract. This study explores the use of relaxation experiments in 2 machine learning-based weather prediction (MLWP) models to identify sources of subseasonal predictability in comparison to a traditional numerical weather prediction (NWP) system. Tropical relaxation involves nudging specific tropical regions of a model toward reanalysis data to isolate their influence on forecast skill. We apply this technique to Pangu-Weather (fully data-driven) and NeuralGCM (hybrid) and compare the experiments to the Unified Forecast System (UFS). The focus is on the week 3–4 forecast of 2 major precipitation events in western North America in winter 2022/2023; both linked to Madden–Julian Oscillation (MJO) activity. For the 2 cases; MLWP models exhibit higher forecast skill than the UFS at subseasonal lead times. Though tropical relaxation improves the skill in all forecast systems; gains are greater for UFS; reflecting the MLWP models' stronger baseline performance. A Rossby wave source (RWS) analysis shows that tropical relaxation consistently improves the large-scale dynamic processes associated with the tropical–extratropical teleconnections leading to both events. These results highlight the potential of relaxation experiments as an effective diagnostic for understanding and improving subseasonal forecasts; especially in emerging MLWP systems.
Keywords:
subseasonal predictability
machine learning-based weather prediction
tropical relaxation
Madden–Julian Oscillation
Rossby wave source
Journal
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IF:
5.3
Papers:
442
Citations:
1.2K
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