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Accurate observation of sea surface wind fields is essential for tropical cyclone forecasting and meteorological hazard mitigation. The HY-2 series microwave scatterometer continuously measures Ku-band ocean surface winds. However, its current wind speed retrieval algorithm struggles in high wind conditions and systematically underestimates speeds during extreme events such as typhoons. To address this bias, this study utilized the HY-2 wind speed data of nine tropical cyclones between 2021 and 2022 as the data source. The Stepped Frequency Microwave Radiometer (SFMR) wind speed measurements served as the ground truth. A modeling dataset was constructed by resampling the SFMR reference data to match the 25 km spatial resolution of the HY-2 scatterometer, followed by spatiotemporal matching within a two-hour time window. The matched dataset was then randomly divided into a training set and a testing set at a 7∶3 ratio. Subsequently, the Broad Learning System (BLS) was employed to conduct the regression analysis and develop a high-wind-speed correction model. BLS employs a shallow, flat architecture in which input features are expanded into “enhanced nodes”, avoiding the deep stacks typical of conventional neural networks. This structure reduces computational cost and accelerates convergence while maintaining predictive performance. Validation results demonstrate that the corrected HY-2 wind speeds achieved a Root Mean Square Error (RMSE) of 4.47 m·s–1, representing a 35% improvement compared to the uncorrected data. For wind speeds exceeding 25 m·s–1, the corrected RMSE reached 6.76 m·s–1, marking significant enhancements over the original values of 13.27 m·s–1. Additionally, a comparative analysis using Typhoon Chanthu (in 2021) as a case study revealed that the corrected HY-2C maximum wind speed increased from 22.09 m·s–1 to 32.73 m·s–1, closely matching wind fields retrieved by Synthetic Aperture Radar (SAR). Further validation through wind speed profile comparisons confirmed the effectiveness of the proposed model. These results demonstrate that our correction framework markedly improves extreme-wind retrieval accuracy, yielding bias-corrected HY-2 products that are more reliable for applications, such as storm surge simulation and typhoon track forecasting.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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