报告题目：Generative Assimilation and Prediction for Weather and Climate
报告人：潘宝祥 副研究员 中科院大气所
报告时间：2025年5月21日（周三）下午 13:45-15:00
报告地点：逸夫楼S427报告厅
主持人：罗京佳 教授 南京信息工程大学
报告人简介：
潘宝祥，2012年本科毕业于武汉大学，2015年获得清华大学硕士学位，2019年获得加州大学欧文分校理学博士学位。曾就职于美国能源部劳伦斯利弗莫尔实验室，现为中国科学院大气物理研究所副研究员。他的研究兴趣主要集中在将概率机器学习与大气过程模型相结合，以实现更精准的跨尺度天气预报和气候预测。
报告摘要：Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions or climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales.
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人工智能学院（未来技术学院）
2025年5月16日
