Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
Tropical cyclones (TCs) pose severe threats to life, infrastructure, and economies in tropical and subtropical regions, underscoring the critical need for accurate and timely forecasts of both track and intensity. Recent advances in AI-based weather forecasting have shown promise in improving TC track forecasts. However, these systems are typically trained on coarse-resolution reanalysis data (e.g., ERA5 at 0.25 degree), which constrains predicted TC positions to a fixed grid and introduces significant discretization errors. Moreover, intensity forecasting remains limited especially for strong TCs by the smoothing effect of coarse meteorological fields and the use of regression losses tha...
| Authors | Peisong Niu, Haifan Zhang, Yang Zhao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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