Lamichhane et al. (2025) Dynamical prediction of sub-seasonal tropical cyclones: IAP-CAS model advances

Identification

• Journal: Atmospheric Research
• Year: 2025
• Date: 2025-10-10
• Authors: Dipendra Lamichhane, Qing Bao, Rui Jin, Zifeng Yu, Bikash Nepal, Widange Charith Madusanka, Ke Ni
• DOI: 10.1016/j.atmosres.2025.108551

Research Groups

• State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
• University of Chinese Academy of Sciences, Beijing, China
• Shanghai Typhoon Institute of CMA, Shanghai, China
• Department of Hydrology and Meteorology, Government of Nepal, Kathmandu, Nepal
• Nanjing University of Information Science and Technology, Nanjing, China

Short Summary

This study evaluates the sub-seasonal tropical cyclone (TC) forecasts from the IAP-CAS dynamic prediction system at one to eight weeks lead. The model demonstrates skill in capturing TC climatology and annual cycles, identifies areas for improved anomaly prediction, and highlights the influence of MJO and ENSO-MJO interactions on forecast proficiency.

Objective

• To analyze the performance of the Institute of Atmospheric Physics of the Chinese Academy of Sciences (IAP-CAS) dynamic sub-seasonal to seasonal (S2S) prediction system in forecasting tropical cyclones (TCs) at sub-seasonal leads (one to eight weeks).
• To identify the strengths and weaknesses of the IAP-CAS model in capturing TC characteristics, annual cycles, anomalies, and the influence of climate modes like MJO and ENSO.

Study Configuration

• Spatial Scale: Global, with specific analysis on Northern Hemisphere basins, Northwest Pacific, and North Indian Ocean.
• Temporal Scale: Sub-seasonal (1 to 8 weeks lead time), annual cycle, and interannual variations.

Methodology and Data

• Models used: IAP-CAS dynamic sub-seasonal to seasonal (S2S) prediction system.
• Data sources: Analysis of forecast outputs from the IAP-CAS S2S prediction system. The study focuses on model performance and biases, implying comparison with observed climatology and events, though specific observational datasets for validation are not detailed in the provided text.

Main Results

• The IAP-CAS model accurately captures the spatiotemporal pattern, landfalling TC, and translation speed climatology.
• The model is skillful in capturing the annual cycle of TCs across most basins and lead weeks.
• Skill in predicting TC anomalies (deviation from the seasonal cycle) is currently limited to the Northwest Pacific during the first week.
• Forecast skill can be significantly enhanced through the application of lag-ensemble and post-processing calibration techniques.
• Interannual variations of accumulated cyclone energy (ACE) are skillfully predicted across Northern Hemisphere basins, with the exception of the North Indian Ocean.
• The model successfully captures the Madden-Julian Oscillation (MJO) influence on TC genesis, showing an eastward propagation of enhanced genesis up to the 3rd week.
• Model proficiency is further improved when the combined impact of El Niño Southern Oscillation (ENSO)-MJO TC relationships is effectively resolved.
• Biases in the dynamic genesis potential index (DGPI) are found to effectively explain the biases in TC forecast errors across most basins and lead times.

Contributions

• Provides a comprehensive evaluation of the IAP-CAS S2S prediction system for sub-seasonal TC forecasting, detailing its current capabilities and limitations.
• Identifies specific areas where the model excels (climatology, annual cycle, MJO influence) and where improvements are needed (anomaly prediction, certain basins).
• Proposes concrete strategies (lag-ensemble, post-processing) for enhancing sub-seasonal TC forecast skill.
• Highlights the critical role of resolving ENSO-MJO interactions for improved TC prediction.
• Pinpoints DGPI biases as a key diagnostic for understanding and addressing TC forecast errors, offering valuable insights for future model development.

Funding

Funding information is not available in the provided text.

Citation

@article{Lamichhane2025Dynamical,
  author = {Lamichhane, Dipendra and Bao, Qing and Jin, Rui and Yu, Zifeng and Nepal, Bikash and Madusanka, Widange Charith and Ni, Ke},
  title = {Dynamical prediction of sub-seasonal tropical cyclones: IAP-CAS model advances},
  journal = {Atmospheric Research},
  year = {2025},
  doi = {10.1016/j.atmosres.2025.108551},
  url = {https://doi.org/10.1016/j.atmosres.2025.108551}
}