Data Assimilation for the Geosciences

Data Assimilation for the Geosciences
Author :
Publisher : Elsevier
Total Pages : 1130
Release :
ISBN-10 : 9780323972536
ISBN-13 : 0323972535
Rating : 4/5 (36 Downloads)

Book Synopsis Data Assimilation for the Geosciences by : Steven J. Fletcher

Download or read book Data Assimilation for the Geosciences written by Steven J. Fletcher and published by Elsevier. This book was released on 2022-11-16 with total page 1130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Assimilation for the Geosciences: From Theory to Application, Second Edition brings together all of the mathematical and statistical background knowledge needed to formulate data assimilation systems into one place. It includes practical exercises enabling readers to apply theory in both a theoretical formulation as well as teach them how to code the theory with toy problems to verify their understanding. It also demonstrates how data assimilation systems are implemented in larger scale fluid dynamical problems related to land surface, the atmosphere, ocean and other geophysical situations. The second edition of Data Assimilation for the Geosciences has been revised with up to date research that is going on in data assimilation, as well as how to apply the techniques. The new edition features an introduction of how machine learning and artificial intelligence are interfacing and aiding data assimilation. In addition to appealing to students and researchers across the geosciences, this now also appeals to new students and scientists in the field of data assimilation as it will now have even more information on the techniques, research, and applications, consolidated into one source. Includes practical exercises and solutions enabling readers to apply theory in both a theoretical formulation as well as enabling them to code theory Provides the mathematical and statistical background knowledge needed to formulate data assimilation systems into one place New to this edition: covers new topics such as Observing System Experiments (OSE) and Observing System Simulation Experiments; and expanded approaches for machine learning and artificial intelligence


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