Data Assimilation

Geir Evensen obtained his Ph.D. in applied mathematics at the University in Bergen in 1992. Thereafter he has worked as a Research Director at the Nansen Environmental and Remote Sensing Center/Mohn-Sverdrup Center, as Prof. II at the Department of Mathematics at the University in Bergen, and as a Principal Engineer at the Hydro Research Center in Bergen. He is author or coauthor of more that 40 refereed publications related to modelling and data assimilation, and he has been the coordinator of international research projects on the development of data assimilation methodologies and systems.

From the reviews of the second edition: "This is a well-written and interesting book addressed to students taking an introductory course in data assimilation and inverse methods ... . The material is presented with detail, and calculations are easy to follow. Many figures help the reader to assess the results. Several discussions and comments are provided in each chapter. In this sense, it is written in a pedagogical way. ... a reference book for researchers interested in the interpretation and implementation of advanced ensemble methods."--- (Jesus Marin-Solano, Mathematical Reviews, Issue 2011 c) "Data assimilation, as defined by Geir Evensen, refers to the computation of the conditional probability distribution function of the output of a numerical model describing a dynamical process, conditioned by observations. ... the book is subdivided into seventeen chapters, which progressively introduce different aspects of data assimilation with Kalman filters. ... The book primarily addresses researchers in the field of data assimilation, for whom it represents a basic reference text. The text is very carefully written and is intended to be self-contained."--- (Hans Wackernagel, Mathematical Geosciences, Vol. 42, 2010)

• Statistical definitions.- Analysis scheme.- Sequential data assimilation.- Variational inverse problems.- Nonlinear variational inverse problems.- Probabilistic formulation.- Generalized Inverse.- Ensemble methods.- Statistical optimization.- Sampling strategies for the EnKF.- Model errors.- Square Root Analysis schemes.- Rank issues.- Spurious correlations, localization, and inflation.- An ocean prediction system.- Estimation in an oil reservoir simulator.