CMG: Quantifying Uncertainty in Oceanic State Estimation

CMG：量化海洋状态估计的不确定性

• 批准号: 0530844
• 负责人: Thomas Haine
• 金额: $ 62万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0530844&HistoricalAwards=false
• 关键词: CMG; Quantifying; Uncertainty; Oceanic; State; CMG; Quantifying; Uncertainty; Oceanic; State

The main research objective of this project is to develop accurate schemes to calculate posterior covariance estimates of predicted ocean states derived by data assimilation. These schemes will then be applied to quantify confidence in estimates of the climate-critical ocean circulation at Denmark Strait and the Irminger Sea. The project will pursue three overlapping objectives: to quantify posterior covariance in (i) four-dimensional variational assimilation, (ii) particle filters, and, (iii) particle smoothers. For (i) a novel scheme of constrained variation will be employed to calculate selected elements of the inverse Hessian (or Fisher) matrix of the maximum-likelihood cost function. For (ii) and (iii) a combination of parametric and moment-closure methods will be developed to overcome rank-deficiency problems in the particle/ensemble covariance estimates. In each case, the new methods will be initially developed for simple low-order dynamical systems, then applied to an eddy-resolving model of the ocean circulation in the Denmark Strait and Irminger Sea. The project will focus on the importance of non-Gaussian error statistics which are clearly present in this oceanographic application.It is now widely accepted that data assimilation will play a major role in the future of ocean sciences with repercussions for diverse users in the fishing industry, marine transportation, naval operations, and recreation. Data assimilation provides a merger of oceanic measurements (from in-situ instruments and satellites) and knowledge of ocean physics in a numerical algorithm. In principle, it permits critical estimates of ocean temperatures, salinities, and currents in the past, present, and future, depending on the data coverage and the computer power available. However, because of natural variability in the chaotic ocean dynamics, some quantities are intrinsically unpredictable and a range of outcomes with widely different consequences are equally compatible with the available measurements. To be useful for practical decision-making, state and parameter estimates must be accompanied by a realistic assessment of their uncertainty. This project will apply some recent theoretical breakthroughs in applied mathematics to develop accurate assessments of uncertainty in calculated ocean estimates from data assimilation. The project will focus on an important application of great practical and theoretical interest, the ocean circulation southeast of Greenland. Knowledge of the ocean conditions in this area is particularly important for monitoring and predicting climate change. The project will also educate and train undergraduate, postgraduate, and post-doctoral students in the mathematical foundations of data assimilation and its practical application to the oceans.

该项目的主要研究目标是开发准确的方案，以计算数据同化衍生的预测海洋状态的后验协方差估计。然后，这些方案将用于量化对丹麦海峡和Irminger Sea气候关键海洋循环的估计的信心。该项目将追求三个重叠的目标：量化（i）四维变分同化，（ii）粒子过滤器和（iii）粒子smohorths中的后协方差。对于（i），将采用一种新的约束变化方案来计算最大可能的成本函数的逆Hessian（或Fisher）矩阵的选定元素。对于（ii）和（iii），将开发参数和矩闭合方法的组合，以克服粒子/集成协方差估计中的等级缺陷问题。在每种情况下，新方法最初都会针对简单的低阶动态系统开发，然后应用于丹麦海峡和Irminger Sea中海洋循环的涡流模型。该项目将重点关注非高斯错误统计数据的重要性，这些统计数据显然存在于该海洋应用程序中。现在，该项目已被广泛接受，数据同化将在海洋科学的未来中起着重要作用，对捕鱼行业，海上运输，海军运营和休闲的多元化用户的影响。数据同化提供了海洋测量值（来自原位仪器和卫星）和数值算法中海洋物理学知识的合并。原则上，根据数据覆盖范围和可用的计算机功率，它允许对过去，现在和将来的海洋温度，盐度和电流进行关键估计。 但是，由于混乱的海洋动力学的自然变异性，某些数量在本质上是不可预测的，并且一系列结果与可用的测量结果同样兼容。为了对实际决策有用，状态和参数估计必须伴随对其不确定性的现实评估。该项目将在应用数学中采用一些最新的理论突破，以在数据同化的海洋估计中对不确定性进行准确的评估。 该项目将着重于格陵兰东南部海洋循环的重要实用和理论兴趣的重要应用。对该地区海洋状况的了解对于监测和预测气候变化尤为重要。该项目还将在数据同化的数学基础上教育和培训本科生，研究生和博士后学生及其对海洋的实际应用。