2013 Interdisciplinary Summer School: Data Assimilation in Geoscience

“2013年跨学科暑期学校：地球科学中的数据同化”将于2013年6月3日至14日在马里兰大学科学计算和数学建模中心（CSCAMM）举行，目标是研究生和处于职业生涯早期阶段的科学家。数据同化的数学问题既基础又具有挑战性，因为它的目的是监测和预测时间演化系统的未知真实状态。这个为期两周的强化暑期学校旨在通过辅导课和计算实验室为数据同化奠定坚实的基础，同时提供接触美国业务数值天气预报（NWP）的最新技术，先进方法和新思想的机会。它是由该领域的主要科学家组织和教授的，他们在为年轻研究人员举办的数据同化学校以及为专家举办的讲习班方面经验丰富。UMD与美国国家海洋和大气管理局（NOAA）国家环境预测中心（NCEP）和美国国家航空航天局戈达德空间和飞行中心（GSFC）的地理位置接近，为访问这些中心提供了独特的体验。UMD的CSCAMM在暑期学校和讲习班方面有着成功的记录。整个暑期学校都是精心设计的，充分利用了所有这些特点。暑期学校的课堂讲稿和报告将通过CSCAMM网站发布，以产生更广泛的影响。从本质上讲，数据同化是一门复杂的跨学科学科，涉及使用计算模型和观测进行科学预测。最熟悉的数据同化实践可能是天气预报，由NOAA和世界上其他运作的NWP中心执行并向公众提供。为了利用NWP预报天气，资料同化通过融合当代大气观测资料来调整复杂计算模式中所表示的当前大气状况。预报的质量很大程度上取决于所使用的数据同化系统的质量。因此，数据同化是具有重大社会影响的科学和操作技术的交汇点。改进对极端天气事件的预测有助于更好地支持决策过程，并产生积极的社会经济影响。此外，数据同化在地球科学内外有着广泛的重要应用领域。其中一个领域是气候变化预测和监测。另一个是未来观测系统和网络的设计。这个暑期学校旨在为年轻科学家提供一个集思广益和开创未来事业的理想场所。

The "2013 Interdisciplinary Summer School: Data Assimilation in Geosciences" to be held June 3-14, 2013, at the Center of Scientific Computing and Mathematical Modeling (CSCAMM), University of Maryland (UMD), College Park, MD, targets graduate students and scientists at an early stage of their career. The mathematical problem of data assimilation is both fundamental and challenging in that it aims at the monitoring and prediction of unknown true state of time-evolving system. This two-week intensive summer school is designed to build a solid foundation for data assimilation through the tutorial lectures and the computational laboratory, while providing an exposure to the current state-of-the-art of the US operational Numerical Weather Prediction (NWP), advanced methods, and new ideas. It is organized and taught by the leading scientists in the field, who are experienced with data assimilation schools for young researchers as well as workshops for specialists. Geographical proximity of UMD to the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and NASA Goddard Space and Flight Center (GSFC) provides a unique one-of-the-kind experience to visit these centers. CSCAMM at UMD has a track record for successful summer schools and workshops. Entire summer school is carefully structured to take advantages of all these features. Lecture notes and presentations of the summer school will be disseminated through the CSCAMM website for the broader impact.In essence, data assimilation is a complex interdisciplinary subject that involves scientific prediction using computational models and observations. The most familiar practice of data assimilation may the weather forecast, performed and provided to public by NOAA and other operational NWP centers in the world. To forecast the weather by the NWP, data assimilation adjusts the current condition of the atmosphere represented in the sophisticated computational model by fusing the contemporary atmospheric observations. Quality of the forecast largely depends on that of the data assimilation system in use. Data assimilation is thus at the very interface of science and operational technology that has significant societal impact. Improving the prediction of extreme weather events can help better support the decision making processes and lead to the positive socio-economic impact. Moreover, data assimilation has a wide range of important application areas within and beyond geosciences. One such area is the climate change projection and monitoring. Another is the design of the future observing systems and networks. This summer school aims to provide an ideal venue for young scientists to brainstorm and initiate their future career.