Machine‐Learning (ML)‐Physics Fusion Model Accelerates the Paradigm Shift in Typhoon Forecasting With a CNOP‐Based Assimilation Framework
In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning model with the physics‐based Shanghai Typhoon Model (SHTM). By employing spectral nudging, the hybrid FuXi‐SHTM model leverages FuXi's...
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| Published in: | Geophysical research letters Vol. 52; no. 15 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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John Wiley & Sons, Inc
16.08.2025
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| ISSN: | 0094-8276, 1944-8007 |
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| Abstract | In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning model with the physics‐based Shanghai Typhoon Model (SHTM). By employing spectral nudging, the hybrid FuXi‐SHTM model leverages FuXi's robust large‐scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation predictions for super typhoons Yagi (2024) and Krathon (2024). To further improve the forecasting capability for extreme typhoons, the Conditional Nonlinear Optimal Perturbation method is employed for the first time to identify sensitive regions for the hybrid model. Despite being constrained by FuXi's large‐scale forecast fields, the dense assimilation of satellite observations within these sensitive regions can further enhance typhoon forecasts. This study emphasizes the synergy between data‐driven strategies and established physical modeling, which can inspire further depth in understanding of extreme typhoon events.
Plain Language Summary
Accurate typhoon forecasting is crucial for effective disaster preparedness. In this study, we developed a hybrid model that integrates the machine‐learning (ML)‐based FuXi model with the physics‐based Shanghai Typhoon Model (SHTM) using spectral nudging. This approach leverages FuXi's strength in capturing large‐scale weather patterns and SHTM's ability to resolve finer mesoscale details, thereby improving predictions of short‐term typhoon's track, intensity, and rainfall. Additionally, we identified observation‐sensitive regions using the Conditional Nonlinear Optimal Perturbation method and found that assimilating satellite data from these regions further improved the forecasts. Overall, our findings highlight the potential of integrating ML with physical models to advance operational typhoon predictions.
Key Points
This study combines the FuXi model and the Shanghai Typhoon Model (SHTM) to create a hybrid operational typhoon forecasting paradigm
Using the strengths of both models, FuXi‐SHTM improves the accuracy of typhoon track, intensity, and precipitation prediction
Proposed a novel workflow of assimilating target observation in Conditional Nonlinear Optimal Perturbation‐sensitive area of FuXi‐SHTM hybrid model |
|---|---|
| AbstractList | In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning model with the physics‐based Shanghai Typhoon Model (SHTM). By employing spectral nudging, the hybrid FuXi‐SHTM model leverages FuXi's robust large‐scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation predictions for super typhoons Yagi (2024) and Krathon (2024). To further improve the forecasting capability for extreme typhoons, the Conditional Nonlinear Optimal Perturbation method is employed for the first time to identify sensitive regions for the hybrid model. Despite being constrained by FuXi's large‐scale forecast fields, the dense assimilation of satellite observations within these sensitive regions can further enhance typhoon forecasts. This study emphasizes the synergy between data‐driven strategies and established physical modeling, which can inspire further depth in understanding of extreme typhoon events. In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning model with the physics‐based Shanghai Typhoon Model (SHTM). By employing spectral nudging, the hybrid FuXi‐SHTM model leverages FuXi's robust large‐scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation predictions for super typhoons Yagi (2024) and Krathon (2024). To further improve the forecasting capability for extreme typhoons, the Conditional Nonlinear Optimal Perturbation method is employed for the first time to identify sensitive regions for the hybrid model. Despite being constrained by FuXi's large‐scale forecast fields, the dense assimilation of satellite observations within these sensitive regions can further enhance typhoon forecasts. This study emphasizes the synergy between data‐driven strategies and established physical modeling, which can inspire further depth in understanding of extreme typhoon events. Accurate typhoon forecasting is crucial for effective disaster preparedness. In this study, we developed a hybrid model that integrates the machine‐learning (ML)‐based FuXi model with the physics‐based Shanghai Typhoon Model (SHTM) using spectral nudging. This approach leverages FuXi's strength in capturing large‐scale weather patterns and SHTM's ability to resolve finer mesoscale details, thereby improving predictions of short‐term typhoon's track, intensity, and rainfall. Additionally, we identified observation‐sensitive regions using the Conditional Nonlinear Optimal Perturbation method and found that assimilating satellite data from these regions further improved the forecasts. Overall, our findings highlight the potential of integrating ML with physical models to advance operational typhoon predictions. This study combines the FuXi model and the Shanghai Typhoon Model (SHTM) to create a hybrid operational typhoon forecasting paradigm Using the strengths of both models, FuXi‐SHTM improves the accuracy of typhoon track, intensity, and precipitation prediction Proposed a novel workflow of assimilating target observation in Conditional Nonlinear Optimal Perturbation‐sensitive area of FuXi‐SHTM hybrid model In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning model with the physics‐based Shanghai Typhoon Model (SHTM). By employing spectral nudging, the hybrid FuXi‐SHTM model leverages FuXi's robust large‐scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation predictions for super typhoons Yagi (2024) and Krathon (2024). To further improve the forecasting capability for extreme typhoons, the Conditional Nonlinear Optimal Perturbation method is employed for the first time to identify sensitive regions for the hybrid model. Despite being constrained by FuXi's large‐scale forecast fields, the dense assimilation of satellite observations within these sensitive regions can further enhance typhoon forecasts. This study emphasizes the synergy between data‐driven strategies and established physical modeling, which can inspire further depth in understanding of extreme typhoon events. Plain Language Summary Accurate typhoon forecasting is crucial for effective disaster preparedness. In this study, we developed a hybrid model that integrates the machine‐learning (ML)‐based FuXi model with the physics‐based Shanghai Typhoon Model (SHTM) using spectral nudging. This approach leverages FuXi's strength in capturing large‐scale weather patterns and SHTM's ability to resolve finer mesoscale details, thereby improving predictions of short‐term typhoon's track, intensity, and rainfall. Additionally, we identified observation‐sensitive regions using the Conditional Nonlinear Optimal Perturbation method and found that assimilating satellite data from these regions further improved the forecasts. Overall, our findings highlight the potential of integrating ML with physical models to advance operational typhoon predictions. Key Points This study combines the FuXi model and the Shanghai Typhoon Model (SHTM) to create a hybrid operational typhoon forecasting paradigm Using the strengths of both models, FuXi‐SHTM improves the accuracy of typhoon track, intensity, and precipitation prediction Proposed a novel workflow of assimilating target observation in Conditional Nonlinear Optimal Perturbation‐sensitive area of FuXi‐SHTM hybrid model Abstract In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning model with the physics‐based Shanghai Typhoon Model (SHTM). By employing spectral nudging, the hybrid FuXi‐SHTM model leverages FuXi's robust large‐scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation predictions for super typhoons Yagi (2024) and Krathon (2024). To further improve the forecasting capability for extreme typhoons, the Conditional Nonlinear Optimal Perturbation method is employed for the first time to identify sensitive regions for the hybrid model. Despite being constrained by FuXi's large‐scale forecast fields, the dense assimilation of satellite observations within these sensitive regions can further enhance typhoon forecasts. This study emphasizes the synergy between data‐driven strategies and established physical modeling, which can inspire further depth in understanding of extreme typhoon events. |
| Author | Wang, Dongliang Huang, Wei Niu, Zeyi Fan, Xuliang Yang, Mengqi Mu, Mu Qin, Bo |
| Author_xml | – sequence: 1 givenname: Zeyi orcidid: 0000-0003-0594-1144 surname: Niu fullname: Niu, Zeyi organization: Shanghai Typhoon Institute, and Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration – sequence: 2 givenname: Dongliang surname: Wang fullname: Wang, Dongliang email: wangdl@typhoon.org.cn organization: Shanghai Typhoon Institute, and Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration – sequence: 3 givenname: Mu orcidid: 0000-0002-6841-5611 surname: Mu fullname: Mu, Mu organization: Fudan University – sequence: 4 givenname: Wei surname: Huang fullname: Huang, Wei organization: Shanghai Typhoon Institute, and Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration – sequence: 5 givenname: Xuliang surname: Fan fullname: Fan, Xuliang organization: Shanghai Central Meteorological Observatory – sequence: 6 givenname: Mengqi orcidid: 0000-0002-7312-1212 surname: Yang fullname: Yang, Mengqi organization: Shanghai Typhoon Institute, and Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration – sequence: 7 givenname: Bo surname: Qin fullname: Qin, Bo organization: Fudan University |
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| CitedBy_id | crossref_primary_10_1007_s00376_025_5464_8 crossref_primary_10_1029_2025GL115926 crossref_primary_10_1016_j_tcrr_2025_08_006 |
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| Snippet | In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning... In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning... Abstract In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi... |
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| SubjectTerms | Assimilation CNOP Forecasting FuXi model Hurricanes Learning algorithms Machine learning Perturbation method Perturbation methods Physics Satellite observation Typhoon forecasting typhoon forecasts Typhoons |
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| Title | Machine‐Learning (ML)‐Physics Fusion Model Accelerates the Paradigm Shift in Typhoon Forecasting With a CNOP‐Based Assimilation Framework |
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