Exploring the typhoon intensity forecasting through integrating AI weather forecasting with regional numerical weather model
Abstract Recent advancements in artificial intelligence (AI) have notably enhanced global weather forecasting, yet accurately predicting typhoon intensity remains challenging. This is largely due to constraints inherent in regression algorithm properties including deep neural networks and inability...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-00926-z |
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| Summary: | Abstract Recent advancements in artificial intelligence (AI) have notably enhanced global weather forecasting, yet accurately predicting typhoon intensity remains challenging. This is largely due to constraints inherent in regression algorithm properties including deep neural networks and inability of coarse resolution to capture the finer-scale weather processes. To address these insufficiencies in typhoon intensity forecasting, we propose an attractive approach by initiating regional Weather Research and Forecasting (WRF) model with Pangu-weather, a state-of-the-art AI weather forecasting system (AI-Driven WRF), whose forecasting power can be further augmented by the implementation of dynamic vortex initialization. The results highlight limitations in Pangu-Weather’s capability to accurately forecast typhoon intensity. In contrast, the AI-Driven WRF model demonstrated notable advancements over Pangu-Weather, achieving more reliable and accurate predictions of typhoon intensity. Furthermore, the AI-Driven WRF model demonstrated promising results in predicting typhoon intensity and wind details, showing commendable performance to traditional global numerical model-driven WRF models. Our analysis underscores the potential of AI weather forecasting models as a viable alternative for driving regional models, suggesting a promising avenue for future research in meteorology. |
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| ISSN: | 2397-3722 |