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Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction

Peisong Niu, Haifan Zhang, Yang Zhao, Tian Zhou, Ziqing Ma, Wenqiang Shen, Junping Zhao, Huiling Yuan, Liang Sun

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.

Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction

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.
Paper Structure (34 sections, 6 equations, 8 figures, 4 tables)

This paper contains 34 sections, 6 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Challenges in Typhoon Prediction
  4. Deep Learning for Tropical Cyclones
  5. Methodology
  6. Probabilistic Center Refinement Model
  7. Kinematic Initialization
  8. Probabilistic Density Mapping
  9. Neural Correction and Reconstruction
  10. Why AI Models Failed in Intensity Forecasting: A Data-centric Perspective
  11. Region-aware Intensity Forecasting Model
  12. Joint Deployment of Tropical Cyclone Forecasting Models at Inference
  13. Experiments
  14. Settings
  15. Main Results
  16. ...and 19 more sections

Figures (8)

  • Figure 1: Tropical cyclone intensities and tracks. IBTrACS data (2000–2024) colored by Saffir-Simpson category.
  • Figure 2: Overview of BaguanCyclone’s architecture. (a) The input consists of an initial position and 2 subsequent forecast frames. (b) The probabilistic center refinement model first employs a nonparametric algorithm to obtain an initial tracking distribution, which is then refined using a tracking correction model. (c) Region-aware intensity forecasting model leverages a Swin-transformer with patch merging to compute the per-region intensity.
  • Figure 3: Bias in track and intensity predictions. Solid lines correspond to forecast models:BaguanCyclone (ours), HRES (ECMWF), Vanilla Baguan, Pangu-Weather, and Graphcast. As ERA5 (the horizontal dashed lines) represents reanalysis data rather than predictive forecasts, its 6-hour intrinsic error is depicted as a constant baseline. The gray histograms indicate the sample count of the validation set.
  • Figure 4: Forecast evaluation for tropical cyclone Beryl: (a) track error, (b) max wind error, and (c) observation vs. prediction. Black line and gray shading indicate landfall and re-emergence; dashed line denotes mean error.
  • Figure 5: Forecast evaluation for tropical cyclone Pulasan, following the same format as Fig. \ref{['fig:beryl']}.
  • ...and 3 more figures