2013 Interdisciplinary Summer School: Data Assimilation in Geoscience

2013年跨学科暑期学校：地球科学数据同化

• 批准号: 1248406
• 负责人: Kayo Ide
• 金额: $ 3万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248406&HistoricalAwards=false
• 关键词: 2013; Interdisciplinary; Summer; Data; Assimilation

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.

“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日在马里兰州大学（UMD）科学计算和数学建模中心（CSCAMM）举行，目标是处于职业生涯早期阶段的研究生和科学家。数据同化的数学问题是一个基础性和挑战性的问题，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。这个为期两周的密集暑期学校旨在通过教程讲座和计算实验室为数据同化奠定坚实的基础，同时提供对美国业务数值天气预报（NWP），先进方法和新思路的最新技术的了解。它由该领域的领先科学家组织和教授，他们在年轻研究人员的数据同化学校以及专家研讨会方面经验丰富。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NASA戈达德空间和飞行中心（GSFC）的地理位置接近，为参观这些中心提供了独特的体验。UMD的CSCAMM在成功的暑期学校和研讨会方面有着良好的记录。整个暑期学校精心设计，以利用所有这些功能。暑期班的讲稿和介绍将通过南极海洋学和海洋气象学委员会的网站传播，以产生更广泛的影响。最常见的数据同化实践可能是由NOAA和世界上其他业务NWP中心执行并向公众提供的天气预报。为了利用数值预报模式进行天气预报，数据同化通过融合当代大气观测数据来调整复杂计算模式中所代表的当前大气状况。预报的质量在很大程度上取决于所使用的数据同化系统。因此，数据同化处于科学与具有重大社会影响的业务技术的交界处。改善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都有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的科学家提供一个理想的场所，集思广益，开始他们未来的职业生涯。