CMG Collaborative Research: Non-assimilation Fusion of Data and Models

CMG协同研究：数据与模型的非同化融合

• 批准号: 1025453
• 负责人: Leonid Piterbarg
• 金额: $ 26.58万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025453&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Non; assimilation

The PIs will develop a methodology for improving estimate and prediction of the state of a dynamical system, with particular focus on analyzing ocean dynamics. The primary goals of this project are thus to develop innovative approaches for representation and manipulation of data uncertainty and model error using a fuzzy set formulation and to then apply these approaches for the data and model fusion formulated as the global optimization problem. Convenient and fast numerical algorithms will be developed to solve the problem using high-performance parallel computing. Such an approach differs from usual statistical estimates but with advantages and drawbacks of its own. The general mathematical theory will be applied to a long-standing but important problem of improving estimates and prediction of the state of the ocean. In particular, the proposed study targets a synthesis of submesoscale/mesoscale fronts, jets and eddies by fusing satellite observations, float and shipboard data of lower resolution, as well as ROMS simulation results for Central California. The theory should provide new tools to be applied in oceanography, meteorology, climatology, artificial intelligence, computer science, control engineering, decision theory, expert systems, operational research and pattern recognition. As the first step in using these tools for broader oceanography community goals, the fusion approach will be applied to different data bases to understand and quantify heat storage and carbon content of the North Atlantic in collaboration with scientists from Great Britain and Germany and to allow junior scientists to obtain excellent training and learning in cross disciplinary/multi-disciplinary areas of great scientific and practical importance. The PIs will address a long-standing but important problem involved with improving the estimation and prediction of the state of the ocean. The primary goals of this project are to develop an innovative approach for representation and manipulation of uncertainty coming from a wide variety of sources such as sensor outputs, model outputs, aggregating expert opinions as well as merging different databases and data even when distinct pieces of information are contradictory, and to suggest methods to fuse this information in decision making goals. The study will provide new mathematical theory and tools relevant for this problem, but also for more general applications in oceanography, meteorology and climatology. Mathematically the approach uses a fuzzy set formulation which originated in pure mathematics and which will be adapted for representing and manipulating data uncertainty and ocean model error. Results of the work will advance development of new forecast metrics in terms of fuzzy sets as well as new methods for quantification of model predictability through data-model and model-model comparisons at weather and climatic scales. As the first step in using these tools for broader oceanography community goals, the approach will be applied to different data bases which relate to quantifying heat storage and carbon content of the North Atlantic. The PIs will collaborate with scientists from Great Britain and Germany. Junior scientists involved in the project will obtain excellent training and learning in cross disciplinary/multi-disciplinary areas of great scientific and practical importance.

PI将开发一种方法，用于改进对动力系统状态的估计和预测，特别侧重于分析海洋动力学。因此，该项目的主要目标是开发创新的方法，表示和操纵的数据不确定性和模型误差使用模糊集制定，然后将这些方法用于制定为全局优化问题的数据和模型融合。将开发方便和快速的数值算法，使用高性能并行计算来解决这个问题。这种方法不同于通常的统计估计，但有其自身的优点和缺点。 一般数学理论将应用于改善海洋状况的估计和预测这一长期存在但重要的问题。特别是，拟议的研究目标的合成亚中尺度/中尺度锋，急流和涡融合卫星观测，浮动和船载数据的分辨率较低，以及ROMS模拟结果为中央加州。 该理论应为海洋学、气象学、气候学、人工智能、计算机科学、控制工程、决策理论、专家系统、运筹学和模式识别等领域提供新的应用工具。 作为利用这些工具实现更广泛的海洋学界目标的第一步，将与英国和德国的科学家合作，对不同的数据库采用融合方法，以了解和量化北大西洋的热储存和碳含量，并使年轻科学家能够在具有重大科学和实践意义的跨学科/多学科领域获得出色的培训和学习。 PI将解决一个长期存在但重要的问题，涉及改善对海洋状况的估计和预测。该项目的主要目标是开发一种创新的方法，用于表示和操纵来自各种来源的不确定性，如传感器输出，模型输出，汇总专家意见以及合并不同的数据库和数据，即使不同的信息是矛盾的，并建议将这些信息融合在决策目标中的方法。这项研究将为解决这一问题提供新的数学理论和工具，同时也为海洋学、气象学和气候学的更广泛应用提供新的数学理论和工具。在数学上，该方法使用模糊集配方起源于纯数学，这将是适应代表和操纵数据的不确定性和海洋模型误差。这项工作的结果将促进发展新的预测指标的模糊集，以及新的方法，量化模型的可预测性，通过数据模型和模型模型的比较，在天气和气候尺度。作为利用这些工具实现更广泛的海洋学社区目标的第一步，该方法将适用于与量化北大西洋的热储存和碳含量有关的不同数据库。PI将与来自英国和德国的科学家合作。参与该项目的年轻科学家将在具有重要科学和实践意义的跨学科/多学科领域获得出色的培训和学习。