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Life sciences · Preprint
arXiv · August 19, 2026
Early or partial results. Treat as a signal, not a conclusion.
Tianmu-TC is a physics-constrained generative AI model for tropical cyclone track and intensity forecasting, trained on Western North Pacific data and reported to outperform deterministic ensemble meteorological AI models and ECMWF in global ocean basins with lower computational cost. The work is preliminary: training is geographically limited, validation methods are not explicitly described, and results have not undergone peer review.
Computational model development with experimental comparison. Tropical cyclone events in global ocean basins; training data from Western North Pacific. Intervention: Physics-constraints generative AI framework (Tianmu-TC) for TC track and intensity forecasting. Compared with: Deterministic and ensemble meteorological AI models; ECMWF numerical weather prediction system. Training: Western North Pacific; validation: global ocean basins.
Tianmu-TC outperforms deterministic and ensemble meteorological AI models and ECMWF in global ocean basins Model demonstrates lower computational cost than NWP systems Framework performs in challenging scenarios including data sparsity, anomaly tracks, rapid intensification and weakening
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If validated externally, this framework could offer faster, more reliable TC forecasts with reduced computational burden. Current evidence is insufficient to recommend operational deployment without peer review and independent validation across multiple ocean basins and seasons.
A novel computational framework for tropical cyclone forecasting showing promising experimental results, but a single-site training dataset, unspecified validation methodology, and absence of peer review limit confidence in generalizability and clinical applicability.
As stated by the source record.
If validated externally, this framework could offer faster, more reliable TC forecasts with reduced computational burden. Current evidence is insufficient to recommend operational deployment without peer review and independent validation across multiple ocean basins and seasons.
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Tropical cyclones (TCs) pose severe risks from strong winds and heavy rainfall. However, forecasting their track and intensity remains challenging due to chaotic atmosphere and the rapid amplification of initial condition errors, leading to growing forecast uncertainty. While numerical weather prediction (NWP) and deep learning models have made progress, they remain computationally demanding and often fail under complex meteorological scenarios. Here, we present Tianmu-TC, a physics-constraints generative framework for global TC forecasting. Trained on Western North Pacific data, Tianmu-TC leverages physics-constraints to generate controllable outputs with reduced uncertainty thus improving forecast reliability. Experiments show Tianmu-TC outperforms deterministic and ensemble meteorological artificial intelligence models and authoritative NWP systems such as ECMWF in global ocean basins, with significantly lower computational cost. We further show Tianmu-TC performs well in challenging scenarios such as data sparsity, anomaly tracks, rapid intensification and weakening. These findings suggest physics-constraints generative AI offers a promising approach for reliable, efficient global TC forecasting.
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