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

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。洋流模型已经建立，海洋生物学模型也正在出现。这些数学模型通常是大而复杂的，并且通常在计算机上数值求解。技术进步导致了海洋系统的测量革命，其形式是物理、生物和化学变量的新传感器，以及卫星、自动海洋观测站和水下自主航行器等新的观测平台。来自这些仪器的环境数据是复杂的。一个主要的挑战是如何有效地利用来自模型预测和观测的信息，以最佳方式确定海洋的状态（这个问题被称为数据同化）。

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).