内容紹介
This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.
レビュー
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.” (Jess Marn-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)
