| 刊名 | Meteorological and Environmental Research |
| 作者 | Xugang LI1,2, Zhiyuan SHU2, Shaoyu HOU1,2,3*, Feng LV1,2, Wuyi WANG1,2, Rong MAI1,2, Haipeng ZHU2 |
| 作者单位 | 1. Xingtai Atmospheric Environment Field Scientific Test Base of CMA, Xingtai 054000, China; 2. Hebei Provincial Weather Modification Center, Shijiazhuang 051432, China; 3. China Meteorological Administration Xiong'an Atmospheric Boundary Layer Key Laboratory, Xiong'an New Area 071000, China |
| DOI | 10.19547/j.issn2152-3940.2025.03.011 |
| 年份 | 2025 |
| 刊期 | 3 |
| 页码 | 51-56 |
| 关键词 | Radar echo extrapolation; Nowcasting; Optical flow; Deep learning |
| 摘要 | This study proposes a novel radar echo extrapolation algorithm, OF-ConvGRU, which integrates Optical Flow (OF) and Convolutional Gated Recurrent Unit (ConvGRU) methods for improved nowcasting. Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area, the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods. Threat Score (TS) and Bias Score (BIAS) were employed to assess extrapolation accuracy across various echo intensities (20-50 dBz) and weather phenomena. Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes (30-40 dBz), effectively combining OF's precise motion estimation with ConvGRU's nonlinear learning capabilities. However, challenges persist in low-intensity (20 dBz) and high-intensity (50 dBz) echo predictions. The study reveals distinct advantages of each method in specific contexts, highlighting the importance of multi-method approaches in operational nowcasting. OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability, particularly for complex weather systems. |
