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Principles of data assimilation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Principles of data assimilationSeon Ki Park, Ewha Womans University, Milija Zupanski, Colorado State University.
作者:	Park, Seon Ki.
其他作者:	Zupanski, Milija.
出版者:	Cambridge :Cambridge University Press,2022.
面頁冊數:	xiii, 398 p. :ill., digital ;25 cm.
附註:	Title from publisher's bibliographic system (viewed on 22 Sep 2022).
標題:	MeteorologyData processing.
電子資源:	https://doi.org/10.1017/9781108924238
ISBN:	9781108924238$q(electronic bk.)

Principles of data assimilation
Park, Seon Ki.

Principles of data assimilation[electronic resource] /Seon Ki Park, Ewha Womans University, Milija Zupanski, Colorado State University. - Cambridge :Cambridge University Press,2022. - xiii, 398 p. :ill., digital ;25 cm.

Title from publisher's bibliographic system (viewed on 22 Sep 2022).

Data assimilation is theoretically founded on probability, statistics, control theory, information theory, linear algebra, and functional analysis. At the same time, data assimilation is a very practical subject, given its goal of estimating the posterior probability density function in realistic high-dimensional applications. This puts data assimilation at the intersection between the contrasting requirements of theory and practice. Based on over twenty years of teaching courses in data assimilation, Principles of Data Assimilation introduces a unique perspective that is firmly based on mathematical theories, but also acknowledges practical limitations of the theory. With the inclusion of numerous examples and practical case studies throughout, this new perspective will help students and researchers to competently interpret data assimilation results and to identify critical challenges of developing data assimilation algorithms. The benefit of information theory also introduces new pathways for further development, understanding, and improvement of data assimilation methods.

ISBN: 9781108924238$q(electronic bk.)Subjects--Topical Terms:

611471
Meteorology
--Data processing.

LC Class. No.: QC874.3 / .P37 2022

Dewey Class. No.: 551.63028563

Principles of data assimilation
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