ITR: A Computational Framework for Observational Science: Data Assimilation Methods and their Application for Understanding North Atlantic Zooplankton Dynamics.

ITR：观测科学的计算框架：数据同化方法及其在理解北大西洋浮游动物动力学中的应用。

• 批准号: 0312610
• 负责人: Andrew Pershing
• 金额: $ 47.85万
• 依托单位: Cornell Univ - State: AWDS MADE PRIOR MAY 2010
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312610&HistoricalAwards=false
• 关键词: ITR; Computational; Framework; Observational; Science

This project will develop a modular data assimilation system, investigate several algorithms to make data assimilation more efficient, and will apply this system to investigate zooplankton dynamics in the North Atlantic. The goal of data assimilation is to find the value of the control variables (typically, the initial conditions or boundary conditions or model parameters) producing the best agreement between the model and the data. A data assimilation system consists of a forward model representing known dynamics. This model is integrated and the deviation between its predictions and available observations are quantified by a cost function. An adjoint model, representing the inverse of the known dynamics, is then run to determine the dependence of the cost function on the control variables. From the results of the adjoint model, the control variables are adjusted and the entire procedure repeats until the system converges on an answer. Because of the many iterations of the forward/adjoint system are required to find an answer, data assimilation is a computationally intensive process. The proposed data assimilation system will attempt to improve the effciency through parallelization and algorithmic improvements. Specifically, this project will evaluate three standard minimization algorithms and a new algorithm based on multigrid techniques. Using this system, data from the Continous Plankton Recorder survey, the only ongoing basin-wide plankton survey, will be assimilated to provide an accurate, quantitative description of the seasonal and interannual changes of North Atlantic zooplankton populations (especially, Calanus finmarchicus) in the Gulf of Maine and across the entire North Atlantic. This description will provide a better mechanistic understanding of the processes responsible for observed patterns in these populations. Such an understanding is prerequisite for predicting the impact of climate variability and change on zooplankton populations and the ecosystems they support.Broader Impacts: The proposed data assimilation system is a general model for many data assimilation problems including operational oceanography and numerical weather prediction. This project's association with the Cornell Theory Center (CTC) allows a unique opportunity to share its data assimilation system to a wide audience. With the help of CTC staff, a web interface to the system running on CTC's .NET cluster will be built. This interface will allow researchers and students across the world to access a high-performance data assimilation system. The development of the data assimilation system will be integrated into a series of computational tools courses offered at Cornell. This project will also provide research opportunities for both graduate students and undergraduates.

该项目将开发一个模块化数据同化系统，研究几种算法，使数据同化更有效，并将应用该系统研究北大西洋的浮游动物动态。数据同化的目标是找到控制变量（通常是初始条件或边界条件或模型参数）的值，从而在模型和数据之间产生最佳一致性。数据同化系统由代表已知动力学的正演模型组成。该模型是集成的，其预测和可用的观测值之间的偏差量化的成本函数。然后运行表示已知动态的逆的伴随模型以确定成本函数对控制变量的依赖性。根据伴随模型的结果，调整控制变量，重复整个过程，直到系统收敛于一个答案。由于前向/伴随系统需要多次迭代才能找到答案，因此数据同化是一个计算密集型过程。建议的数据同化系统将试图通过并行化和算法改进来提高效率。具体来说，这个项目将评估三个标准的最小化算法和一个新的算法的基础上多重网格技术。利用这一系统，从连续浮游生物记录仪调查，唯一正在进行的全流域浮游生物调查的数据，将被同化，以提供一个准确的，定量的描述北大西洋浮游动物种群（特别是，哲水蚤finmarchicus）在缅因州湾和整个北大西洋的季节性和年际变化。这种描述将提供一个更好的机制的理解负责在这些人群中观察到的模式的过程。这种理解是预测气候变率和变化对浮游动物种群及其所支持的生态系统的影响的先决条件。更广泛的影响：拟议的数据同化系统是许多数据同化问题的通用模型，包括业务海洋学和数值天气预报。该项目与康奈尔理论中心（CTC）的联系为向广大受众分享其数据同化系统提供了一个独特的机会。在反恐委员会工作人员的帮助下，将建立一个在反恐委员会.NET集群上运行的系统的网络界面。该接口将使世界各地的研究人员和学生能够访问高性能的数据同化系统。数据同化系统的开发将被纳入康奈尔大学提供的一系列计算工具课程。该项目还将为研究生和本科生提供研究机会。