Data Assimilation

Mathematical background.- Discrete Time: Formulation.- Discrete Time: Smoothing Algorithms.- Discrete Time: Filtering Algorithms.- Discrete Time: MATLAB Programs.- Continuous Time: Formulation.- Continuous Time: Smoothing Algorithms.- Continuous Time: Filtering Algorithms.- Continuous Time: MATLAB Programs.- Index.

Kody J.H. Law is a Senior Research Scientist at the SRI - Center for Uncertainty Quantification in the Division of Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology. He earned an undergraduate B.S. degree in Mathematics, an M.S. in Applied Mathematics, and a Ph.D. in Mathematics at the University of Massachusetts in Amherst. His Ph.D. research focus was on computational studies of discrete and continuous Nonlinear Schrodinger Equation, usually in the presence of an external potential and with application to nonlinear optics and Bose-Einstein condensation.

Andrew Mark Stuart is Professor of Applied Mathematics at the Mathematics Institute at Warwick University. He received his PhD from Oxford University, Computing Laboratory and his major research interest is in the field of applied and computational mathematics. He has won numerous awards, including the Leslie Fox Prize for Numerical Analysis and the Monroe Martin Prize in Applied Mathematics. Another of his numerous published works includes Multiscale Methods: Averaging and Homogenization (Springer, 2008).

Dr. Konstantinos Zygalakis is Lecturer in Applied Mathematics at the University of Southampton. He received his Diploma in Applied Mathematics and Physics at the National Technical University of Athens, and his MSc and PhD in Mathematics from the University of Warwick. His research interests include theory and applications of multiscale stochastic differential equations, numerical methods and backward error analysis of the same and the mathematical modeling of plants.

"The mathematical style of the book is accessible to post-graduate students and combines formal mathematics with intuitive arguments and summaries of higher level results. ... the book is a good guide on dynamic data assimilation. ... the book suitable as a reference book for modelling on coordinates, whenever the sample space has a Euclidean vector space structure." (Vera Pawlowsky-Glahn, zbMATH 1353.60002, 2017)

"This book provides a Bayesian perspective of data assimilation, with a focus on smoothing and filtering problems with generic dynamical models. ... The authors also provide many numerical results, focusing on simple models that help the reader easily grasp the important properties of the underlying algorithms. In my opinion, this book is well suited to a graduate level course on data assimilation for applied mathematicians." (David T. B. Kelly, Mathematical Reviews, December, 2016) "The authors have used a collection of dynamical systems examples throughout the book. ... Exercises are also given at the end of each chapter. The first half of this book would be very suitable as a graduate level textbook and concise reference on discrete time approaches to the data assimilation problem from a Bayesian point of view. The second half of the book ... will primarily be of interest to researchers working in this area." (Brian Borchers, MAA Reviews, maa.org, May, 2016)