Advanced Data Assimilation for Geosciences: Lecture Notes of the Les Houches School of Physics…

Hardcover | November 10, 2014

EditorEric Blayo, Marc Bocquet, Emmanuel Cosme

not yet rated|write a review
Data assimilation aims at determining as accurately as possible the state of a dynamical system by combining heterogeneous sources of information in an optimal way. Generally speaking, the mathematical methods of data assimilation describe algorithms for forming optimal combinations ofobservations of a system, a numerical model that describes its evolution, and appropriate prior information. Data assimilation has a long history of application to high-dimensional geophysical systems dating back to the 1960s, with application to the estimation of initial conditions for weatherforecasts. It has become a major component of numerical forecasting systems in geophysics, and an intensive field of research, with numerous additional applications in oceanography, atmospheric chemistry, and extensions to other geophysical sciences. The physical complexity and the highdimensionality of geophysical systems have led the community of geophysics to make significant contributions to the fundamental theory of data assimilation. This book gathers notes from lectures and seminars given by internationally recognized scientists during a three-week school held in the Les Houches School of physics in 2012, on theoretical and applied data assimilation. It is composed of (i) a series of main lectures, presenting the fundamentalsof the most commonly used methods, and the information theory background required to understand and evaluate the role of observations; (ii) a series of specialized lectures, addressing various aspects of data assimilation in detail, from the most recent developments of the theory to thespecificities of various thematic applications.

Pricing and Purchase Info

$44.75 online
$89.50 list price (save 50%)
Ships within 1-3 weeks
Ships free on orders over $25

From the Publisher

Data assimilation aims at determining as accurately as possible the state of a dynamical system by combining heterogeneous sources of information in an optimal way. Generally speaking, the mathematical methods of data assimilation describe algorithms for forming optimal combinations ofobservations of a system, a numerical model that de...

Eric Blayo is a Professor in applied mathematics at the University of Grenoble. He is leading a research team working on the development of mathematical and numerical methods for environmental applications. Marc Bocquet is a Professor and Senior Researcher at the environment research centre (CEREA), a joint laboratory of Ecole des Pon...

other books by Eric Blayo

Underwater Seascapes: From geographical to ecological perspectives
Underwater Seascapes: From geographical to ecological p...

Kobo ebook|Mar 24 2014

$101.39 online$131.64list price(save 22%)
Format:HardcoverDimensions:608 pages, 9.69 × 6.73 × 0.98 inPublished:November 10, 2014Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198723849

ISBN - 13:9780198723844

Look for similar items by category:

Customer Reviews of Advanced Data Assimilation for Geosciences: Lecture Notes of the Les Houches School of Physics: Special Issue, June 2012

Reviews

Extra Content

Table of Contents

PART I - KEY LECTURES1. Olivier Talagrand: 4D-VAR: four dimensional variational assimilation2. Andrew Lorenc: Four-dimensional variational data assimilation3. Chris Snyder: Introduction to the Kalman filter4. Emmanuel Cosme: Smoothers5. Carla Cardinali: Observation influence diagnostic of a data assimilation system6. Carla Cardinali: Observation impact on the short range forecastPART II - SPECIALIZED LECTURES7. Loik Berre: Background error covariances: estimation and specification8. Gerald Desroziers: Observation error specifications9. Olivier Talagrand: Errors. A posteriori diagnostics10. Peter Houtekamer: Error dynamics in ensemble Kalman filter systems: Localization11. Peter Houtekamer: Short-range error statistics in an ensemble Kalman filter12. Peter Houtekamer: Error dynamics in ensemble Kalman filter systems: System error13. Peter Jan Van Leeuwen: Particle filters for the geosciences14. Francois-Xavier Le Dimet, Igor Gejadze, and Victor Shutyaev: Second order methods for error propagation in variational data assimilation15. Laurent Hascoet: Adjoints by automatic differentiation16. Arthur Vidard: Assimilation of images17. Laurent Debreu, Emilie Neveu, Ehouarn Simon, and Francois-Xavier Le Dimet: Multigrid algorithms and local mesh refinement methods in the context of variational data assimilation18. Marc Bocquet, Lin Wu, Frederic Chevallier, and Mohammad Reza Koohkan: Selected topics in multiscale data assimilation19. Florence Rabier and Mike Fisher: Data assimilation in meteorology20. Marc Bocquet: An introduction to inverse modelling and parameter estimation for atmospheric and oceanic sciences21. Frederic Chevallier: Greenhouse gas fluxes inversion22. Hendrik Elbern, Elmar Friese, Lars Nieradzik, and Jorg Schwinger: Data assimilation in atmospheric chemistry and air quality23. Ichiro Fukumori: Combining models and data in large-scale oceanography: examples from the consortium for Estimating the Circulation and Climate of the Ocean (ECCO)24. Javier Zavala-Garay, John Wilkin, and Julia Levin: Data assimilation in coastal oceanography: IS4DVAR in the Regional Ocean Modeling System (ROMS)25. Bertrand Bonan, Maelle Nodet, Olivier Ozenda, and Catherine Ritz: Data assimilation in glaciology