Bayesian Data Assimilation

贝叶斯数据同化

• 批准号: RGPIN-2017-05151
• 负责人: Dowd, Michael
• 金额: $ 2.16万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711415
• 关键词: Bayesian; Data; Assimilation

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.

成功的环境预测需要有效地利用模拟模型和现有的观测资料。统计建模提供了一种将这两种信息来源以及科学知识联合收割机结合起来的手段，以便尽可能多地了解所考虑的系统，这一程序有时被称为数据同化。主要应用领域是天气、气候和海洋预报。必须制定和应用特殊的估算方法，因为随着科学和计算技术的进步被纳入模拟代码，复杂的环境模型正在不断完善。观测技术已经变得越来越复杂，从新型传感器的时间序列，卫星的空间图像，以及探测和自适应穿越海洋深度的复杂自主仪器。在数据和模型的融合中，存在着熟练的环境预测和对这些系统更好的科学理解的希望和可能性。我建议的工作涉及进一步发展先进的数据同化，使用最先进的统计方法，使我们朝着这一目标前进。