Chunhua Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Chunhua Wang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Dazhi Xi
Department of Earth and Planetary Sciences, The University of Hong Kong, Hong Kong, China
1 shared publication - Kekuan Chu
State Key Laboratory of Severe Weather Meteorological Science and Technology Nanjing University Nanjing China
1 shared publication Na He
Laboratory for Research and Application of Aviation Weather Forecasting and Service Technologies CAAC East China Regional Administration Shanghai China
1 shared publication- Shuai Wang
Department of Physics Imperial College London London UK
1 shared publication
Latest Publications
- Open Access
- 11 September 2026
- Published by American Geophysical Union (AGU) in Journal of Geophysical Research: Machine Learning and Computation
- Vol. 3 (5)
Downscaling of Tropical Cyclone Surface Wind Fields With a Hybrid Attention Transformer
This study employs a Hybrid Attention Transformer‐based super‐resolution model to reconstruct tropical cyclone (TC) surface wind fields. The model downscales coarse‐resolution ERA5 reanalysis data to high‐resolution HWind analyses (1998–2013), increasing the horizontal resolution by a factor of five. First, this study assesses the impact of data set partitioning strategies and different meteorological input variables on model performance. The reconstructed TC surface wind fields and the radial profiles of azimuthally averaged surface winds agree well with the HWind analysis. Key TC metrics, including the maximum sustained wind speed (MSW) and the radius of 17 m s −1 winds (R17), show high correlations with HWind (0.81 and 0.92, respectively). When independently validated against IBTrACS records over the North Atlantic (1990–2020), the model maintains robust correlations, confirming its out‐of‐sample reliability across different periods and cases. The reconstructed past TC intensity and size metrics achieved by the developed model provide new opportunities for research, demonstrating the model's future potential in TC climatology and hazard assessment studies.
- 11 September 2026
- Published by American Geophysical Union (AGU) in Journal of Geophysical Research: Machine Learning and Computation
- Vol. 3 (5)
Open AccessDownscaling of Tropical Cyclone Surface Wind Fields With a Hybrid Attention Transformer
This study employs a Hybrid Attention Transformer‐based super‐resolution model to reconstruct tropical cyclone (TC) surface wind fields. The model downscales coarse‐resolution ERA5 reanalysis data to high‐resolution HWind analyses (1998–2013), increasing the horizontal resolution by a factor of five. First, this study assesses the impact of data set partitioning strategies and different meteorological input variables on model performance. The reconstructed TC surface wind fields and the radial profiles of azimuthally averaged surface winds agree well with the HWind analysis. Key TC metrics, including the maximum sustained wind speed (MSW) and the radius of 17 m s −1 winds (R17), show high correlations with HWind (0.81 and 0.92, respectively). When independently validated against IBTrACS records over the North Atlantic (1990–2020), the model maintains robust correlations, confirming its out‐of‐sample reliability across different periods and cases. The reconstructed past TC intensity and size metrics achieved by the developed model provide new opportunities for research, demonstrating the model's future potential in TC climatology and hazard assessment studies.