Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
arXiv:2603.22314v1 Announce Type: new Abstract: 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 that bias predictions toward conditional means. To address these limitations, we propose BaguanCyclone, a novel, unified framework that integrates two key innovations: (1) a probabilistic center refinement module that models the continuous spatial distribut
arXiv:2603.22314v1 Announce Type: new Abstract: 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 that bias predictions toward conditional means. To address these limitations, we propose BaguanCyclone, a novel, unified framework that integrates two key innovations: (1) a probabilistic center refinement module that models the continuous spatial distribution of TC centers, enabling finer track precision; and (2) a region-aware intensity forecasting module that leverages high-resolution internal representations within dynamically defined sub-grid zones around the TC core to better capture localized extremes. Evaluated on the global IBTrACS dataset across six major TC basins, our system consistently outperforms both operational numerical weather prediction (NWP) models and most AI-based baselines, delivering a substantial enhancement in forecast accuracy. Remarkably, BaguanCyclone excels in navigating meteorological complexities, consistently delivering accurate forecasts for re-intensification, sweeping arcs, twin cyclones, and meandering events. Our code is available at https://github.com/DAMO-DI-ML/Baguan-cyclone.
Executive Summary
This article presents BaguanCyclone, a novel AI-based framework for improving tropical cyclone (TC) track and intensity forecasting. By integrating a probabilistic center refinement module and a region-aware intensity forecasting module, BaguanCyclone enhances forecast accuracy by refining TC positions and capturing localized extremes. The system outperforms operational numerical weather prediction models and most AI-based baselines, demonstrating its potential in navigating complex meteorological events. The framework's code is publicly available, allowing for further research and application. The study's findings have significant implications for disaster preparedness and response, highlighting the importance of accurate and timely weather forecasting in tropical and subtropical regions.
Key Points
- ▸ BaguanCyclone is a novel AI-based framework for TC track and intensity forecasting.
- ▸ The framework integrates a probabilistic center refinement module and a region-aware intensity forecasting module.
- ▸ BaguanCyclone outperforms operational numerical weather prediction models and most AI-based baselines.
Merits
Strength in Addressing Limitations
The study effectively addresses the limitations of coarse-resolution reanalysis data and regression losses that bias predictions toward conditional means, presenting a more accurate and robust forecasting framework.
Improved Forecasting Accuracy
BaguanCyclone delivers a substantial enhancement in forecast accuracy, particularly in complex meteorological events such as re-intensification, sweeping arcs, twin cyclones, and meandering events.
Publicly Available Code
The framework's code is publicly available, facilitating further research, development, and application of BaguanCyclone.
Demerits
Limited Generalizability
The study's results may not be generalizable to all tropical cyclone basins and scenarios, highlighting the need for further research and validation.
Computational Resource Intensity
The framework's computational requirements and potential scalability issues may limit its practical application in real-time forecasting scenarios.
Expert Commentary
The study presents a significant advancement in AI-based TC track and intensity forecasting, showcasing the potential of BaguanCyclone in navigating complex meteorological events. The framework's ability to refine TC positions and capture localized extremes demonstrates its potential in improving forecast accuracy. However, the study's limitations, including limited generalizability and computational resource intensity, highlight the need for further research and validation. The study's findings have significant implications for disaster preparedness and response, emphasizing the importance of accurate and timely weather forecasting in tropical and subtropical regions.
Recommendations
- ✓ Future research should focus on developing more generalizable and scalable AI-based weather forecasting systems, capable of handling complex meteorological events and addressing the computational resource intensity of such systems.
- ✓ Policymakers should prioritize investments in AI-based weather forecasting systems and disaster preparedness and response infrastructure, recognizing the critical role of accurate and timely weather forecasting in reducing the risk of loss of life and property in tropical and subtropical regions.
Sources
Original: arXiv - cs.LG
