CMG Collaborative Research: Particle Filters and Ecological Models (PFEM): Application of chainless Monte-Carlo methods to mapping the ecology of the North Pacific Ocean

CMG 合作研究：粒子过滤器和生态模型 (PFEM)：应用无链蒙特卡罗方法绘制北太平洋生态图

• 批准号: 0934298
• 负责人: Alexandre Chorin
• 金额: $ 28.3万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0934298&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Particle; Filters

Intellectual Merit. Dynamic response of the world ocean ecosystem to interannual and decadal climate variability has been characterized by changes either in rates (e.g. rate of primary and export production) or in community structure. These changes in community structure cannot be easily modeled without introducing additional complexity in the ecosystem model. Similar limitations exist when ecosystem models have to reflect changes in the community from one region of the world ocean to another. The working hypothesis of this study is that advances in data assimilation techniques, coupled physical-biological modeling and high performance computing will help construct maps of parameter estimates for ecological models that will reflect these changes in species and community structure.Parameter estimation using remotely sensed ocean color and in situ observations is especially challenging since the state and/or measurement functions are highly non-linear, and the posterior distribution of the process states is not Gaussian. Several data assimilation techniques have been utilized in the last decade or so for coupled models of ocean physics and biogeochemistry but most of them rely on weakly non-linear and Gaussian systems (e.g. variational methods, Ensemble Kalman Filters) or are prohibitively expensive (e.g. particle filters). In particle methods one estimates the probability density of an ensemble of particles (instances of the model modified by observations). This is usually done by a Bayesian construction, with the resampling needed for accuracy done by Markov chain Monte Carlo. For large-scale systems this procedure can be prohibitively expensive, because the number of particles may need to be substantial and because the MCMC may have an excessive fraction of rejections. This study will employ newly-developed methods, where the probability density is sampled directly, without a Bayesian step, and where the resampling is done without recourse to Markov chains. A chainless Monte-Carlo particle filter is designed to overcome the shortcomings of variational methods and Markov chain Monte-Carlo particle methods for highly nonlinear, non-Gaussian systems with very high state dimensions. These chainless methods have performed well in smaller systems. The aim is to produce spatially and temporally varying parameter estimates for an ecological model of medium complexity coupled to a mixed layer model, based on assimilation of remotely-sensed surface chlorophyll-a observations in the north Pacific basin. Parameter values will be interpreted in terms of species distribution and nutrient cycling. This project will evaluate the ability of this coupled model to simulate observed seasonal to interannual variability, evaluate its sensitivity to forcing, and relate the results to considerations of ocean ecology.Broader Impact. The new data assimilation techniques will be broadly applicable to the highly nonlinear high dimensional problems that appear in interdisciplinary oceanography and applied mathematics. Examination of error estimates will lead to quantitative assessment of the information content of remotely sensed observations, and facilitate planning of future observing programs. The results of the coupled model simulations should provide useful information for assessment of the impact of climate change. The particle methods will be a significant advance in the state of the nonlinear filtering art, and be widely applicable in science and engineering. On the educational side, the project will provide training for future scientists in the field of interdisciplinary modeling and data assimilation.

智力上的功绩。世界海洋生态系统对年际和年代际气候变化的动态反应的特点是速率(例如初级生产率和出口生产率)或群落结构的变化。如果不在生态系统模型中引入额外的复杂性，则很难对群落结构的这些变化进行建模。当生态系统模型必须反映从世界海洋的一个区域到另一个区域的群落变化时，也存在类似的限制。这项研究的工作假设是，数据同化技术、物理-生物耦合建模和高性能计算的进步将有助于构建反映这些物种和群落结构变化的生态模型参数估计图。利用遥感海洋颜色和现场观测进行参数估计尤其具有挑战性，因为状态和/或测量函数是高度非线性的，过程状态的后验分布不是高斯的。在过去十年左右的时间里，一些数据同化技术被用于海洋物理和生物地球化学的耦合模型，但其中大多数依赖于弱非线性和高斯系统(例如变分方法、集合卡尔曼滤波)或昂贵得令人望而却步(例如粒子滤波)。在粒子方法中，人们估计粒子集合(通过观测修正的模型的实例)的概率密度。这通常是通过贝叶斯构造来完成的，由马尔科夫链蒙特卡罗来完成精度所需的重采样。对于大型系统，这一过程的成本可能高得令人望而却步，因为颗粒的数量可能需要很大，而且MCMC可能会有过多的废品率。这项研究将使用新开发的方法，其中概率密度被直接采样，而不是贝叶斯步骤，并且重采样不求助于马尔科夫链。为了克服变分方法和马尔可夫链蒙特卡罗粒子方法的缺点，设计了一种无链蒙特卡罗粒子滤波器，用于求解具有很高状态维的高度非线性、非高斯系统。这些无链方法在较小的系统中表现良好。其目的是根据对北太平洋盆地地表叶绿素a遥感观测的同化，为与混合层模式耦合的中等复杂性生态模式产生空间和时间变化的参数估计。参数值将根据物种分布和养分循环进行解释。该项目将评估该耦合模式模拟观测到的季节到年际变化的能力，评估其对强迫的敏感性，并将结果与海洋生态方面的考虑联系起来。这种新的数据同化技术将广泛适用于海洋学和应用数学交叉学科中出现的高度非线性的高维问题。对误差估计的检查将导致对遥感观测的信息量进行定量评估，并有助于规划未来的观测方案。耦合模式模拟的结果应能为评估气候变化的影响提供有用的信息。粒子方法将是非线性滤波技术的重大进步，在科学和工程中具有广泛的应用前景。在教育方面，该项目将在跨学科建模和数据同化领域为未来的科学家提供培训。