关于在南大举办 “地球科学数据同化”夏季国际讲习班的通知（第一轮）

本次讲习班特别邀请了美国气象学会（AMS）会士、美国国家大气研究中心（NCAR）数据同化研究首席、资深研究员Jeffrey L. Anderson，挪威研究中心（NORCE）数据同化研究首席Geir Evensen，科罗拉多州立大学教授、数据同化研究中心首席Peter Jan van Leeuwen，英国雷丁大学教授、英国国家地球观测中心数据同化中心负责人Amos Lawless、日本东京大学助理教授Masashi Minamide等国际资深专家，围绕地球科学数据同化开展系列讲座和教程。此讲习班将从集合Kalman滤波、变分方法等数据同化的基础出发，延展至数据同化研究领域的国际最新进展和成果，如非高斯滤波、集合平滑、粒子滤波等；既包括关于数据同化理论的实践操作，又涵盖数据同化在地球科学的应用和进展，如石油和海冰应用、卫星数据同化、机器学习与同化的结合等。

此次讲习班致力于提高地球科学领域科研与业务部门青年学者和研究生在数据同化、数值天气预报和地球系统模拟方面的研究水平与创新能力，并拓展其国际视野。

欢迎从事数据同化、数值天气预报、地球系统模拟以及地球系统可预报性等研究方向的研究生、博士后及青年科研人员报名参加。学员需要有一定的数据同化和数值天气、气候预报基础及计算机编程能力，并能流利地进行英文交流。

授课时间： 2024年6月17-6月21日，共5天

授课地点： 南京大学仙林校区大气科学学院

地  址： 南京市栖霞区仙林大道163号南京大学大气科学学院

邮  编： 210023

联系人：

雷荔傈：[email protected]，手机：13182909059

张威：[email protected]，手机：13057590052

具体详情将于后续通知中发布。

差旅和住宿费用自理，无会议费。

June 17 2024 – June 21 2024

School of Atmospheric Sciences

Xianlin Campus, Nanjing University

Key Laboratory of Mesoscale Severe Weather of Ministry of Education,

School of Atmospheric Sciences,

Nanjing University

Jeffrey L. Anderson (National Center for Atmospheric Research)

Geir Evensen (Norce Norwegian Research Centre)

Peter Jan van Leeuwen (Colorado State University)

Amos Lawless (University of Reading)

Masashi Minamide (The University of Tokyo)

Data assimilation, the fusion of observations with model forecasts, is one of the most important and challenging aspects of prediction of our earth system, including the atmosphere, ocean, land, sea ice, and etc. Sophisticated data assimilation algorithms that combine ingredients from numerical modeling, observational studies, statistics, and dynamics of the earth system, have been developed at operational centers around the world. Exciting new data assimilation algorithms that aim to better understand the earth system are also being developed. This one-week summer school will bring together experts from leading prediction centers and researchers from top institutes and universities. A broad overview of statistics fundamentals, widely applied data assimilation methods including the ensemble Kalman filter and variational methods, and advanced data assimilation approaches, such as ensemble smoothers, non-Gaussian filters and particle filters, will be introduced. Applications of advanced data assimilation in geosciences, including oil reservoir and sea ice, and radiance data assimilation, will also be presented. Finally, topics on the frontier of data assimilation integrated with machine learning will be discussed.

Graduate students, recent recipients of a Ph.D., and researchers at operational centers, whose research interests extend data assimilation, numerical weather prediction, and the Earth system predictability.

Address： School of Atmospheric Sciences, Nanjing University, Xianlin Dadao #163, Qixia, Nanjing, 210023

Contacts：

Lili Lei, [email protected], Cell 13182909059

Wei Zhang, [email protected], Cell 13057590052

To be announced.