Bayesian statisical estimation for nonlinear dynamic systems in the marine environmental sciences

海洋环境科学中非线性动力系统的贝叶斯统计估计

• 批准号: 293195-2007
• 负责人: Dowd, Michael
• 金额: $ 1.02万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=480518
• 关键词: Bayesian; statisical; estimation; nonlinear; dynamic

Mathematical models for the marine environmental sciences embody theoretical notions of how these systems operate. Models are well established for ocean currents, and are emerging for ocean biology. These mathematical models are typically large and complex, and generally solved numerically on a computer. Technological advances have led to a measurement revolution for marine systems in the form of new sensors for physical, biological and chemical variables, as well as new observing platforms such as satellites, automated ocean observatories, and underwater autonomous vehicles. The environmental data coming from these instruments is complex. A major challenge is how to effectively use the information coming from both model predictions and observations to optimally determine the state of the ocean (a problem that has come to be known as data assimilation).

海洋环境科学的数学模型体现了这些系统如何运作的理论概念。洋流模型已经很成熟，并且海洋生物学模型也正在兴起。这些数学模型通常庞大且复杂，并且通常在计算机上进行数值求解。技术进步引发了海洋系统的测量革命，包括用于物理、生物和化学变量的新型传感器，以及卫星、自动化海洋观测站和水下自主航行器等新型观测平台。来自这些仪器的环境数据非常复杂。一个主要挑战是如何有效地利用来自模型预测和观测的信息来最佳地确定海洋状况（这个问题被称为数据同化）。