北京雁栖湖应用数学研究院

塔赫蕾·埃夫特哈里 , 胡丕丕 , 梁鑫 , 马志婷 , 哈米德·莫菲迪 , 苏春梅 , Axel G.R. Turnquist , 王丽 , 熊繁升 , 杨朔 , 杨武岳

许鹏博

2026年05月20日 14:00 至 15:00

Online

Zoom 518 868 7656 (BIMSA)

Recently, artificial intelligence-based models for forecasting global weather have been rapidly developed. Most of the global models are trained on reanalysis datasets with a spatial resolution of 0.25◦ × 0.25◦. However, study on artificial intelligence-based limited area weather forecasting models is still limited. In this study, an artificial intelligence-based limited area weather forecasting model (YingLong) is developed. YingLong utilizes a parallel structure of global and local blocks to capture multiscale meteorological features. Its predictability on surface temperature, humidity and wind speed is comparable to the predictability of the dynamical limited area model WRF-ARW, but with a much faster running speed. YingLong is also applied to investigate the issues related to the lateral boundary condition of artificial intelligence-based limited area models. The difference between artificial intelligence-based limited area models and dynamical limited area models is also discussed.

许鹏博，副教授，博士毕业于兰州大学数学与统计学院，现任职于华东师范大学数学科学学院。主要从事 AI for Science、统计物理、随机分析等方向的研究，长期关注人工智能方法在科学问题建模与复杂系统分析中的应用。近年来，主持国家自然科学基金青年项目、博士后面上项目及百度松果基金项目，并作为骨干成员参与国家自然科学基金委重大项目。在科研成果方面，已在 Communications Earth & Environment、New Journal of Physics、Physical Review E 等国际期刊发表论文20篇。