Jump to ContentJump to Main Navigation
Advanced Data Assimilation for GeosciencesLecture Notes of the Les Houches School of Physics: Special Issue, June 2012$
Users without a subscription are not able to see the full content.

Éric Blayo, Marc Bocquet, Emmanuel Cosme, and Leticia F. Cugliandolo

Print publication date: 2014

Print ISBN-13: 9780198723844

Published to Oxford Scholarship Online: March 2015

DOI: 10.1093/acprof:oso/9780198723844.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see http://www.oxfordscholarship.com/page/privacy-policy).date: 19 July 2018

Four-dimensional variational data assimilation

Four-dimensional variational data assimilation

Chapter:
(p.31) 2 Four-dimensional variational data assimilation
Source:
Advanced Data Assimilation for Geosciences
Author(s):

A. C. Lorenc

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198723844.003.0002

In this chapter, four-dimensional variational data assimilation (4D-VAR) is discussed in the context of numerical weather prediction (NWP). The analysis step in an NWP data assimilation cycle combines observations with a background forecast. Plausible models of error distributions involve transforms and statistics to describe the structure of errors at one time, plus a forecast model constraining the time evolution. They allow a Bayesian derivation of equations for the optimal analysis, by minimizing a 4D-Var penalty function using an adjoint model. Difficulties with the deterministic best fit of a nonlinear NWP model are discussed and a statistical approach to 4D-VAR based on the extended Kalman filter is presented. Advanced extensions to 4D-VAR can allow for nonlinearities and non-Gaussian distributions, arising from the physical limits to humidity, and from the possibility of erroneous observations. Ensembles provide useful information about likely background errors, which can be used in hybrid ensemble–variational data assimilation.

Keywords:   four-dimensional variational data assimilation, 4D-VAR, numerical weather prediction, Bayesian, adjoint model, nonlinearities, hybrid ensemble–variational

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .