ITR/AP: An Ensemble Approach to Data Assimilation in the Earth Sciences

ITR/AP：地球科学数据同化的整体方法

• 批准号: 0121182
• 负责人: Dennis McLaughlin
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0121182&HistoricalAwards=false
• 关键词: ITR; AP; Ensemble; Approach; Data

ITR/AP: An Ensemble Approach to Data Assimilation in the Earth SciencesNew data sources are beginning to have a dramatic impact on our ability to understand the earth as an integrated system. Our prospects for dealing with the environmental issues of the 21st century -- climate change, population pressures on natural resources, and major modifications in global element cycles -- depend largely on this new information. However, our ability to process and interpret environmental data is not keeping pace with the dramatic increase in available information, especially information from airborne and orbital remote sensing platforms. If we are to realize the potential benefits of new sensing technologies we will need to develop intelligent environmental data assimilation procedures that are able to efficiently extract useful information about the earth from a diverse set of data sources. Environmental data assimilation can be posed as a problem of estimating a large number of unobservable or highly uncertain variables (e.g. sea surface heights, atmospheric pressures, hydrologic fluxes, etc.) from a large number of related but noisy measurements (e.g. microwave radiances or backscatter detected by a satellite sensor). The estimation procedure relies on mathematical models that relate unknowns to measurements. Environmental estimation problems are challenging because the systems of interest: 1) are spatially distributed and highly variable over a wide range of space and time scales, 2) are difficult to describe with precision, 3) are often nonlinear, even chaotic, and 4) are often characterized by non-unique relationships between unknowns and measurements.This project is concerned with very large problems (many measurements and many unknowns) which are not amenable to traditional data assimilation techniques but are of crucial interest to researchers in the earth sciences. An interdisciplinary team will develop a better understanding of the issues of dimensionality reduction and uncertainty propagation that are crucial to large-scale data assimilation. So-called ensemble methods provide a particularly informative way to identify these key features. A new generation of "intelligent" data assimilation methods will be developed that build on the understanding gained from the reduced problem. The applicability of these methods will be investigated on problems of broad interest in the earth sciences, including problems that 1) deal with coupled systems, 2) cut across traditional disciplines, and 3) work with remote sensing data sets.This ITR project brings together acknowledged experts on environmental data assimilation. It is a group ITR project, rather than several individual projects, which cuts across earth science disciplines. The research will be coordinated with: 1) a seminar series, 2) joint supervision of Ph.D. students and post-doctoral researchers, 3) a Ph.D. mentoring program, 4) a selection of cross-cutting sample problems, and 5) co-authored publications.

ITR/AP：地球科学数据同化的综合方法新数据源开始对我们将地球理解为一个综合系统的能力产生巨大影响。我们处理21世纪环境问题的前景——气候变化、人口对自然资源的压力以及全球元素循环的重大变化——很大程度上取决于这些新信息。然而，我们处理和解释环境数据的能力跟不上可用信息的急剧增长，特别是来自机载和轨道遥感平台的信息。如果我们要实现新的传感技术的潜在好处，我们将需要开发智能环境数据同化程序，能够有效地从各种数据源中提取有关地球的有用信息。环境数据同化可以提出一个问题，即从大量相关但有噪声的测量（例如卫星传感器探测到的微波辐射或后向散射）估计大量不可观测或高度不确定的变量（例如海面高度、大气压力、水文通量等）。估计过程依赖于将未知数与测量联系起来的数学模型。环境估计问题是具有挑战性的，因为感兴趣的系统：1)空间分布和高度可变在大范围的空间和时间尺度上，2)难以精确描述，3)通常是非线性的，甚至是混沌的，4)通常以未知和测量之间的非唯一关系为特征。这个项目涉及的是非常大的问题（许多测量值和许多未知数），这些问题不适合传统的数据同化技术，但对地球科学研究人员来说是至关重要的。一个跨学科团队将更好地理解对大规模数据同化至关重要的降维和不确定性传播问题。所谓的集成方法提供了一种特别有用的方法来识别这些关键特征。新一代的“智能”数据同化方法将建立在从简化问题中获得的理解的基础上。这些方法的适用性将在地球科学中广泛关注的问题上进行研究，包括1)处理耦合系统的问题，2)跨越传统学科的问题，以及3)处理遥感数据集的问题。这个ITR项目汇集了环境数据同化方面公认的专家。这是一个小组ITR项目，而不是几个单独的项目，它跨越了地球科学学科。该研究将协调：1)系列研讨会，2)博士生和博士后研究员的联合监督，3)博士指导计划，4)交叉样本问题的选择，以及5)共同撰写的出版物。