Accuracy and Stability for Lagrangian Data Assimilation
Abstract
The overarching goal of this work is to develop the theoretical foundations for the study and development of algorithms used to estimate properties of the ocean. Detailed knowledge of the ocean state in specific regions is of paramount importance in many areas of science and technology, and in particular for a range of applications of interest to the US Navy. An example concerns the study of sea ice in the Arctics, a subject of fundamental importance in the study of climate change, and also of commercial importance in the determination of shipping routes. Oceanographers use a variety of instruments to aquire oceanographic data, many of them Lagrangian in nature, meaning that they are wholly, or partially, advected by the ocean flow. In particular study of sea ice is being revolutionized by the use gliders. These are instruments which combine pre-set mission plans with oceanographic drift to locate themselves, potentially optimally, for the aquisition of data. Data assimilation provides a methodology for blending the data obtained by these instruments with our knowledge of the physics of the oceans, and of objects transported within them. In mathematical terms this corresponds to merging observed data with imperfect differential equation models of the physics which produce the data. Whilst there are numerous innovative algorithms for carrying this out, those employed in practical oceanography do not come equipped with an underpinning theory which justifies their use, in terms of robustness and accuracy of predictions. The purpose of this work is to provide a theory of robustness and accuracy, and to use it to develop more effective algorithms. 1
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N000141512366
Entities
People
- Andrew M. Stuart
Organizations
- Office of Naval Research
- United States Navy
- University of Warwick