Machine Learning (ML)-Physics Fusion Model Outperforms Both Physics-Only and ML-Only Models in Typhoon Predictions
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917549786005504 |
|---|---|
| author | Niu, Zeyi Huang, Wei Li, Hao Fan, Xuliang Yang, Yuhua Yang, Mengqi Qin, Bo |
| author_facet | Niu, Zeyi Huang, Wei Li, Hao Fan, Xuliang Yang, Yuhua Yang, Mengqi Qin, Bo |
| contents | Data-driven machine learning (ML) models, such as FuXi, exhibit notable limitations in forecasting typhoon intensity and structure. This study presents a comprehensive evaluation of FuXi-SHTM, a hybrid ML-physics model, using all 2024 western North Pacific typhoon cases. The FuXi-SHTM hybrid demonstrates clear improvements in both track and intensity forecasts compared to the standalone SHTM, FuXi, and ECMWF HRES models. Compared to FuXi alone, FuXi-SHTM reduces typhoon track forecast errors by 16.5% and 5.2% at lead times of 72 h and 120 h, respectively, and reduces intensity forecast errors by 59.7% and 47.6%. Furthermore, FuXi-SHTM simulates cloud structures more realistically compared to SHTM, and achieves superior representation of the 10-m wind fields in both intensity and spatial structure compared to FuXi and SHTM. Increasing the resolution of FuXi-SHTM from 9 km to 3 km further enhances intensity forecasts, highlighting the critical role of the resolution of the physical model in advancing hybrid forecasting capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning (ML)-Physics Fusion Model Outperforms Both Physics-Only and ML-Only Models in Typhoon Predictions Niu, Zeyi Huang, Wei Li, Hao Fan, Xuliang Yang, Yuhua Yang, Mengqi Qin, Bo Atmospheric and Oceanic Physics Data-driven machine learning (ML) models, such as FuXi, exhibit notable limitations in forecasting typhoon intensity and structure. This study presents a comprehensive evaluation of FuXi-SHTM, a hybrid ML-physics model, using all 2024 western North Pacific typhoon cases. The FuXi-SHTM hybrid demonstrates clear improvements in both track and intensity forecasts compared to the standalone SHTM, FuXi, and ECMWF HRES models. Compared to FuXi alone, FuXi-SHTM reduces typhoon track forecast errors by 16.5% and 5.2% at lead times of 72 h and 120 h, respectively, and reduces intensity forecast errors by 59.7% and 47.6%. Furthermore, FuXi-SHTM simulates cloud structures more realistically compared to SHTM, and achieves superior representation of the 10-m wind fields in both intensity and spatial structure compared to FuXi and SHTM. Increasing the resolution of FuXi-SHTM from 9 km to 3 km further enhances intensity forecasts, highlighting the critical role of the resolution of the physical model in advancing hybrid forecasting capabilities. |
| title | Machine Learning (ML)-Physics Fusion Model Outperforms Both Physics-Only and ML-Only Models in Typhoon Predictions |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2504.20852 |