Statistics for Dynamic Models with Application in the Marine Sciences

动态模型统计在海洋科学中的应用

• 批准号: 293195-2012
• 负责人: Dowd, Michael
• 金额: $ 0.87万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571411
• 关键词: Statistics; Dynamic; Models; Application; Marine

Successful environmental prediction requires making effective use of simulation models and available observations. Statistical modeling provides a means of combining these two sources of information, as well as other scientific knowledge, in order to learn as much as possible about the system under consideration. This procedure is sometimes referred to as data assimilation, and is based on Bayesian statistical principles. 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. Observations have become increasingly sophisticated ranging from monitoring time series, 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 of more skillful environmental prediction, along with 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.

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以及其他科学知识结合在一起的手段，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，基于贝叶斯统计原理。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。观测已变得越来越复杂，从监测时间序列、卫星提供的空间图像，到探测和自适应穿越海洋深处的复杂自主仪器。数据和模型的融合带来了更巧妙的环境预测，以及对这些系统更好的科学理解。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。