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; 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将开发一种方法来改善动力系统状态的估计和预测，特别关注分析海洋动力学。因此，该项目的主要目标是开发创新方法，以使用模糊集公式来表示和操纵数据不确定性和模型误差，然后将这些方法应用于数据和模型融合作为全球优化问题的模型融合。将开发方便且快速的数值算法，以使用高性能并行计算来解决问题。这种方法与通常的统计估计不同，但其优点和缺点。 一般的数学理论将应用于改善海洋状态估计和预测的长期但重要的问题。尤其是，拟议的研究针对融合卫星观测值，浮点和船上的数据，以较低分辨率的卫星观测，浮点和船上数据以及中部加利福尼亚州中部的ROM仿真结果，以构成子尺度/中尺度前线，喷气机和涡流的合成。 该理论应提供新的工具，可用于海洋学，气象，气候学，人工智能，计算机科学，控制工程，决策理论，专家系统，运营研究和模式识别。 作为将这些工具用于更广泛的海洋学社区目标的第一步，将使用融合方法将其应用于不同的数据库，以与大不列颠和德国的科学家合作了解和量化北大西洋的供热储存和碳含量，并允许初级科学家在跨学科/多学科领域中获得出色的培训和学习的伟大科学和实践重要性。 PI将解决一个长期但重要的问题，涉及改善海洋状态的估计和预测。该项目的主要目标是开发一种创新的方法来代表和操纵来自各种来源的不确定性，例如传感器输出，模型输出，汇总专家意见以及即使在不同的信息矛盾的情况下，也将不同的数据库和数据合并，并建议将此信息融合到决策目标中。该研究将提供与此问题相关的新数学理论和工具，还将提供有关海洋学，气象和气候学上更一般应用。从数学上讲，该方法使用模糊的集合公式，该公式起源于纯数学，并将适应表示和操纵数据不确定性和海洋模型误差。这项工作的结果将根据模糊集以及通过数据模型和模型模型比较在天气和气候尺度上进行量化模型可预测性的新方法来推动新的预测指标的开发。作为将这些工具用于更广泛的海洋学社区目标的第一步，该方法将应用于与量化北大西洋的供热存储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。参与该项目的初级科学家将在跨学科/多学科领域获得出色的培训和学习，具有伟大的科学和实际重要性。