A Hybrid Machine Learning/Physics‐Based Modeling Framework for 2‐Week Extended Prediction of Tropical Cyclones

Author:

Liu Hao‐Yan12ORCID,Tan Zhe‐Min3ORCID,Wang Yuqing4ORCID,Tang Jianping3ORCID,Satoh Masaki5ORCID,Lei Lili3ORCID,Gu Jian‐Feng3ORCID,Zhang Yi3,Nie Gao‐Zhen6,Chen Qi‐Zhi7

Affiliation:

1. Key Laboratory of Marine Hazards Forecasting Ministry of Natural Resources Hohai University Nanjing China

2. State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing China

3. Key Laboratory of Mesoscale Severe Weather Ministry of Education, and School of Atmospheric Sciences Nanjing University Nanjing China

4. International Pacific Research Center and Department of Atmospheric Sciences School of Ocean and Earth Science and Technology University of Hawaii at Manoa Honolulu HI USA

5. Atmosphere and Ocean Research Institute The University of Tokyo Kashiwa Japan

6. National Meteorological Center China Meteorological Administration Beijing China

7. Nanjing Pulan Atmospheric Environment Research Institute Nanjing China

Abstract

AbstractPrediction of tropical cyclones (TCs) beyond a week is challenging but of great importance for disaster prevention and mitigation. We propose a hybrid machine learning (ML)/physics‐based modeling framework to extend TC forecasts to 2 weeks. This framework integrates a recently launched ML‐based global weather prediction model (Pangu) and the high‐resolution physics‐based regional weather research and forecasting (WRF) model. The Pangu model shows promise in enhancing the accuracy of predictions for large‐scale circulation and TC tracks, while the high‐resolution WRF model is capable of capturing the core processes underlying TC evolution. To capitalize on the complementary strengths of both the Pangu and WRF models in predicting TCs, the framework comprises three key components: downscaling the Pangu model using the WRF model, adjusting large‐scale circulation through spectral nudging driven by the Pangu model forecasts, and updating sea surface temperature using an ocean mixed‐layer model. These components also ensure the framework's feasibility for real‐time TC forecasting. The prediction skill of the framework has been demonstrated for five long‐lived TCs across various basins from 2018 to 2023. Results indicate that the hybrid ML/physics‐based modeling framework decreased the 2‐week mean TC track and intensity errors by 59% and 32% compared to the global numerical weather prediction models, by 2% and 59% compared to the ERA5‐driven Pangu model, and by 32% and 23% compared to the ERA5‐driven WRF model, respectively. This implies that the framework has great potential to be used for 2‐week extended prediction of TCs.

Funder

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Reference68 articles.

1. Biswas M. K. Bernardet L. Abarca S. Ginis I. Grell E. Kalina E. &Zhang Z.(2018).Hurricane Weather Research and Forecasting (HWRF) model: 2017 scientific documentation. (No. NCAR/TN‐544+STR)(pp.1–111).https://doi.org/10.5065/D6MK6BPR

2. The THORPEX Interactive Grand Global Ensemble

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