报告题目：Improving Seasonal Forecast Using Probabilistic Deep Learning

报  告 人： 潘宝祥 研究员

邀  请 人： 刘德地 教授

时      间： 2022年7月11日（星期一）下午4:00-5:30

地      点： 水电科技大楼A区202会议室

会议链接： https://meeting.tencent.com/dm/SiKx5HO4az3W

 腾讯视频会议（ID: 448 688 164）   B站直播 （ID: 23115892)

报告人简介：

潘宝祥，美国劳伦斯利弗摩尔国家实验室研究员，主要研究兴趣包括概率信息理论、结合机器学 习与动力模式的天气-气候尺度预报、动力系统可预报性。2012年本科毕业于武汉大学，2015年 在清华大学获得工学硕士学位，2019年于加州大学欧文分校(University of California, Irvine) 获得工学博士学位，师从Dr. Soroosh Sorooshian, Dr. Kuolin Hsu, Dr. Amir AghaKouchak。

 报告简介：

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and  2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

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