Data assimilation (DA) is a term used in weather, ocean, and climate science that refers to the following problem: given a dynamical model (e.g. a model simulating atmospheric motion) and a series of observations (e.g. wind measurements from the real weather), find a trajectory of the model that matches the observed data.
Very similar problems appear in other fields of science and engineering, and one might even say that DA simply goes by other names there. In any event, there is a strong relationship between data assimilation and nonlinear smoothing and filtering (probability theory), nonlinear observers (control theory and engineering), and inverse problems (applied and numerical mathematics).
What is particular about data assimilation in weather, ocean, and climate science is that we have to deal with very large dimensional (strictly speaking, infinite dimensional) systems, since atmosphere and ocean are described by partial differential equations.
The Data Assimilation and Inverse Problems research group at the Department of Mathematics and Statistics is part of the Data Assimilation Research Centre (DARC), which involves researchers across the entire School of Mathematical, Physical and Computational Sciences. Go to the DARC website for general information on the research activities on data assimilation at Reading.
Our group is also closely linked to the Numerical Analysis and Computational Modelling group.