Reliable precipitation nowcasting using probabilistic diffusion models
文献类型：期刊论文
| 作者 | Nai,Congyi1,3; Pan,Baoxiang2; Chen,Xi2; Tang,Qiuhong1,3; Ni,Guangheng4; Duan,Qingyun5; Lu,Bo6; Xiao,Ziniu2; Liu,Xingcai1,3 |
| 刊名 | Environmental Research Letters
 |
| 出版日期 | 2024-02-27 |
| 卷号 | 19期号:3 |
| 关键词 | probabilistic diffusion model ensemble forecast nowcasting |
| DOI | 10.1088/1748-9326/ad2891 |
| 通讯作者 | Liu,Xingcai() |
| 英文摘要 | Abstract Precipitation nowcasting is a crucial element in current weather service systems. Data-driven methods have proven highly advantageous, due to their flexibility in utilizing detailed initial hydrometeor observations, and their capability to approximate meteorological dynamics effectively given sufficient training data. However, current data-driven methods often encounter severe approximation/optimization errors, rendering their predictions and associated uncertainty estimates unreliable. Here a probabilistic diffusion model-based precipitation nowcasting methodology is introduced, overcoming the notorious blurriness and mode collapse issues in existing practices. Diffusion models learn a sequential of neural networks to reverse a pre-defined diffusion process that generates the probability distribution of future precipitation fields. The precipitation nowcasting based on diffusion model results in a 3.7% improvement in continuous ranked probability score compared to state-of-the-art generative adversarial model-based method. Critically, diffusion model significantly enhance the reliability of forecast uncertainty estimates, evidenced in a 68% gain of spread-skill ratio skill. As a result, diffusion model provides more reliable probabilistic precipitation nowcasting, showing the potential to better support weather-related decision makings. |
| 语种 | 英语 |
| 出版者 | IOP Publishing |
| WOS记录号 | IOP:ERL_19_3_034039 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202760]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu,Xingcai |
| 作者单位 | 1.Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China 2.Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, People’s Republic of China 3.University of Chinese Academy of Sciences, Beijing, People’s Republic of China 4.State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, People’s Republic of China 5.The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, People’s Republic of China 6.Laboratory for Climate Studies and CMA‐NJU Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing, People’s Republic of China |
| 推荐引用方式 GB/T 7714 | Nai,Congyi,Pan,Baoxiang,Chen,Xi,et al. Reliable precipitation nowcasting using probabilistic diffusion models[J]. Environmental Research Letters,2024,19(3). |
| APA | Nai,Congyi.,Pan,Baoxiang.,Chen,Xi.,Tang,Qiuhong.,Ni,Guangheng.,...&Liu,Xingcai.(2024).Reliable precipitation nowcasting using probabilistic diffusion models.Environmental Research Letters,19(3). |
| MLA | Nai,Congyi,et al."Reliable precipitation nowcasting using probabilistic diffusion models".Environmental Research Letters 19.3(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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