Evaluation of precipitation forecasting methods and an advanced lightweight model
文献类型：期刊论文
| 作者 | Yang, Nan1,2 ; Wang, Chong1,2; Li, Xiaofeng1,2
|
| 刊名 | ENVIRONMENTAL RESEARCH LETTERS
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 19期号:9页码:12 |
| 关键词 | precipitation forecasting model analysis deep learning adversarial generative network |
| ISSN号 | 1748-9326 |
| DOI | 10.1088/1748-9326/ad661f |
| 通讯作者 | Li, Xiaofeng(lixf@qdio.ac.cn) |
| 英文摘要 | Precipitation forecasting is crucial for warning systems and disaster management. This study focuses on deep learning-based methods and categorizes them into three categories: Recurrent Neural Network (RNN-RNN-RNN), Convolutional Neural Network (CNN-CNN-CNN), and CNN-RNN-CNN methods. Then, we conduct a comprehensive evaluation of typical methods in these three categories using the SEVIR precipitation dataset. The results show that RNN-RNN-RNN suffers from instability in long-term forecasts due to error accumulation, CNN-CNN-CNN struggles to capture temporal signals but produces relatively stable forecasts, and CNN-RNN-CNN significantly increases model complexity and inherits the drawbacks of RNN, leading to worse forecasts. Here, we propose an advanced lightweight precipitation forecasting model (ALPF) based on CNN. Experimental results demonstrate that ALPF can effectively forecast spatial-temporal features, maintaining CNN's feature extraction capabilities while avoiding error accumulation in RNN's propagation. ALPF achieves long-term stable precipitation forecasts and can better capture large precipitation amounts. |
| WOS关键词 | EXTREME PRECIPITATION ; RADAR DATA ; ASSIMILATION |
| 资助项目 | National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809[42306214] ; National Natural Science Foundation of China[SDBX2022026] ; Shandong Province Postdoctoral Innovative Talents Support Program[2023M733533] ; China Postdoctoral Science Foundation ; Special Research Assistant Project of the Chinese Academy of Sciences[XDB42000000] ; Strategic Priority Research Program of the Chinese Academy of Sciences[2019JZZY010102] ; Major Scientific and Technological Innovation Projects in Shandong Province |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001282698700001 |
| 出版者 | IOP Publishing Ltd |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/186195]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Key Lab Ocean Observat & Forecasting, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Nan,Wang, Chong,Li, Xiaofeng. Evaluation of precipitation forecasting methods and an advanced lightweight model[J]. ENVIRONMENTAL RESEARCH LETTERS,2024,19(9):12. |
| APA | Yang, Nan,Wang, Chong,&Li, Xiaofeng.(2024).Evaluation of precipitation forecasting methods and an advanced lightweight model.ENVIRONMENTAL RESEARCH LETTERS,19(9),12. |
| MLA | Yang, Nan,et al."Evaluation of precipitation forecasting methods and an advanced lightweight model".ENVIRONMENTAL RESEARCH LETTERS 19.9(2024):12. |
入库方式： OAI收割
来源：海洋研究所
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