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

该项目将开发一个模块化的数据同化系统，研究几种使数据同化更有效的算法，并将该系统应用于研究北大西洋的浮游动物动力学。数据同化的目标是找到在模式和数据之间产生最佳一致性的控制变量(通常是初始条件或边界条件或模式参数)的值。一个数据同化系统由一个代表已知动力学的正演模式组成。这个模型是综合的，它的预测和可用观测值之间的偏差通过成本函数来量化。然后运行表示已知动力学的逆的伴随模型，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，整个过程重复，直到系统收敛于一个答案。由于正演/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的过程。拟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。具体地说，本项目将评估三种标准的最小化算法和一种基于多重网格技术的新算法。利用这一系统，来自大陆浮游生物记录器调查的数据将被同化，这是唯一一项正在进行的盆地范围浮游生物调查，以准确、定量地描述缅因湾和整个北大西洋北大西洋浮游动物种群(特别是Calanus finmarchicus)的季节和年际变化。这一描述将提供对在这些种群中观察到的模式负责的过程的更好的机械理解。这样的理解是预测气候变化和变化对浮游动物种群及其支持的生态系统的影响的先决条件。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式，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)的合作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。这个界面将允许世界各地的研究人员和学生访问高性能的数据同化系统。数据同化系统的开发将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。

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.