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特邀德国亥姆霍兹极地海洋研究中心首席资料同化专家Lars Nerger作学术报告——大气•风云讲坛(2023年第17期)

发布者:何琼发布时间:2023-06-21浏览次数:630

报告题目:Ensemble Data Assimilation for Coupled Models of the Earth System

报告专家:Dr. Lars Nerger

报告时间:2023年6月29日(周四)14:30

会议形式:ZOOM-线上会议(会议ID: 329 593 0089, 会议密码:87654321)

会议链接(无需密码):

https://us02web.zoom.us/j/3295930089?pwd=Tis2eU4vVE5mZmI4NGxpVXRDRVhxQT09

主 持 人:曾跃飞 教授

专家简介:

Dr. Lars Nerger, Group leader of  “Data Assimilation” at Alfred Wegener Institute (AWI, also called Helmholtz Center for Polar and Marine Research); Lead developer of the Parallel Data Assimilation Framework (PDAF); Lead Consultant of Bremen Supercomputing Competence Center (BremHLR) at University of Bremen; Co-Organizer & member of Scientific Advisory Committee of “International Symposium on Data Assimilation - Online”; Associate Editor of Monthly Weather Review (2017 - 2021). His research interests cover ensemble data assimilation algorithms and their applications with ocean and ocean-biogeochemical models. He has several cooperation activities with Chinese institutes and universities (e.g., CMA, Peking University, Sun Yat-sen University and etc).

报告摘要: Coupled models simulate different compartments of the Earth system as well as their interactions. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. Ensemble data assimilation methods can be applied with these model systems, however the need to compute an ensemble of model integrations strongly increases the already high computing cost of the models. To allow us to perform the data assimilation in supercomputers, the parallel data assimilation framework (PDAF) has been developed. I will discuss the application and challenges of coupled ensemble data assimilation on the example of two different coupled model systems: the atmosphere-ocean model AWI-CM and the ocean-biogeochemical model HBM-ERGOM.

欢迎广大师生踊跃参加!

气象灾害教育部重点实验室

气象灾害预报预警与评估协同创新中心

大气科学学院

2023.06.21


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