Bayesian Data Assimilation

成功的环境预测需要有效地利用模拟模型和可用的观测数据。统计建模提供了一种将这两个信息源和科学知识结合起来的手段，以便尽可能多地了解所审议的系统，这一过程有时被称为数据同化。关键应用领域是天气、气候和海洋预报。必须开发和应用特殊的估计方法，因为随着科学和计算的进步被纳入到模拟代码中，复杂的环境模型正在不断地得到完善。观测技术已变得越来越复杂，从新型传感器的时间序列、卫星的空间图像，到探测和自适应穿越海洋深处的复杂自主仪器。在数据和模型的融合中，存在着巧妙的环境预测和对这些系统更好的科学理解的前景和可能性。我提议的工作涉及高级数据同化的进一步发展，它使用最先进的统计方法来推动我们实现这一目标。

Successful environmental prediction requires making effective use of simulation models and available observations. Statistical modeling provides a means to combine these two sources of information, as well as scientific knowledge, in order to learn as much as possible about the system under consideration, a procedure that is sometimes referred to as data assimilation. Key application areas are in weather, climate, and marine forecasting. Special estimation methods must be developed and applied since complex environmental models are constantly being refined as scientific and computational advances are incorporated in the simulation code. Observational technologies have become increasingly sophisticated ranging from time series from new sensor types, spatial imagery from satellites, and complex autonomous instruments that probe and adaptively transit the ocean depths. In the fusion of data and model lies the promise and possibility of skillful environmental prediction and a better scientific understanding of these systems. My proposed work deals with further development of advanced data assimilation that uses state-of-the-art statistical methods to move us towards this goal.