Home
  >  
Section 63
  >  
Chapter 62,169

Four-dimensional variational data assimilation using the adjoint of a multilevel primitive-equation model

Jean-Noéul Thepaut; Philippe Courtier

Quarterly Journal of the Royal Meteorological Society 117(502): 1225-1254

1991

ISSN/ISBN: 0035-9009
DOI: 10.1002/qj.49711750206
Accession: 062168761
Download citation:  
Text
  |  
BibTeX
  |  
RIS
The aim of the paper is to demonstrate the numerical feasibility of 4-D variational assimilation using a multilevel primitive-equation model. The experiments consist in minimizing the distance between the model solution and the observations. The gradient of the cost function thus defined is computed by integrating the adjoint of the model. Here, assimilations are performed using model-generated observations.

PDF emailed within 0-6 h: $19.90




Related References

Devenon, J.L.; Dekeyser, I.; Leredde, Y.; Lellouche, J.M. 2001: Assimilation de données par méthode variationnelle utilisant l'adjoint d'un code de circulation côtière tridimensionnel - Data assimilation method by a variational methodology using the adjoint of a 3-D coastal circulation primitive equation model Mathocean. Conference (Marseille 1999-12) / Mathocean. Colloque (Marseille 1999-12) 24(5): 395-407
Jean-Noël Thépart; Drasko Vasiljevic; Philippe Courtier; Jean Pailleux 1993: Variational assimilation of conventional meteorological observations with a multilevel primitive-equation model Quarterly Journal of the Royal Meteorological Society 119(509): 153-186
Xiangyang Zheng,Roberto Mayerle,Qianguo Xing… 2016: Adjoint free four-dimensional variational data assimilation for a storm surge model of the German North Sea Ocean Dynamics 66(8): 1037-1050
Stéphane Louvel 2001: Implementation of a dual variational algorithm for assimilation of synthetic altimeter data in the oceanic primitive equation model MICOM Journal of Geophysical Research: Oceans 106(C5): 9199-9212
Li, Y.; Navon, I.M.; Courtier, P.; Gauthier, P. 1993: Variational data assimilation with a semi-Lagrangian semi-implicit global shallow-water equation model and its adjoint Monthly Weather Review 121(6): 1759-1769
Ezer, T; Mellor, G L. 1994: Continuous Assimilation of Geosat Altimeter Data into a Three-Dimensional Primitive Equation Gulf Stream Model Journal of Physical Oceanography 24(4): 832-847
Zou, X. 1997: Tangent linear and adjoint of on-off processes and their feasibility for use in 4-dimensional variational data assimilation Tellus A 49(1): 3-31
Yumimoto, K.; Uno, I. 2006: Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation Atmospheric Environment 40(35): 6836-6845
Yumimoto, K.; Uno, I. 2006: Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation Atmospheric Environment (1994) 40(35): 6836-6845
Yong, L.I.; Navon, I.M.; Weiyu, Y.A.N.G.; Xiaolei, Z.O.U.; Bates, J.R.; Moorthi, S.; Higgins, R.W. 1994: Four-dimensional variational data assimilation experiments with a multilevel semi-Lagrangian semi-implicit general circulation model Monthly Weather Review 122(5): 966-983
Elbern, H.; Schmidt, H.; Talagrand, O.; Ebel, A. 2000: 4D-variational data assimilation with an adjoint air quality model for emission analysis Environmental Modelling-Software 15(6-7): 539-548
Panteleev, G.; Yaremchuk, M.; Rogers, W.E. 2015: Adjoint-Free Variational Data Assimilation into a Regional Wave Model Journal of Atmospheric and Oceanic Technology 32(7): 1386-1399
Sujit, B.A.S.U.; Subramanian, V.; Pandey, P.C. 1993: The variational technique of data assimilation using adjoint equations in a shallow water model Proceedings of the Indian Academy of Sciences. Earth and Planetary Sciences 102(4): 521-536
Song, H.; Edwards, C.A.; Moore, A.M.; Fiechter, J. 2016: Data assimilation in a coupled physical- “biogeochemical model of the California Current System using an incremental lognormal 4-dimensional variational approach: Part 1- Model formulation and biological data assimilation twin experiments Ocean Modelling 106: 131-145
Nino-Ruiz, E.D.; Guzman-Reyes, L.G.; Beltran-Arrieta, R. 2019: An adjoint-free four-dimensional variational data assimilation method via a modified Cholesky decomposition and an iterative Woodbury matrix formula Nonlinear Dynamics 99(3): 2441-2457
Lindskog, M.; Gustafsson, N.; Navascues, B.; Mogensen, K.S.; Huang, X.Y.; Yang, X.; Andræ, U.; Berre, L.; Thorsteinsson, S.; Rantakokko, J. 2001: Three-dimensional variational data assimilation for a limited area model : Part II: Observation handling and assimilation experiments Tellus A 53(4): 447-468
Olivier Talagrand; Philippe Courtier 1987: Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory Quarterly Journal of the Royal Meteorological Society 113(478): 1311-1328
Mattern, J.P.; Edwards, C.A.; Hill, C.N. 2019: Dual number-based variational data assimilation: Constructing exact tangent linear and adjoint code from nonlinear model evaluations Plos one 14(10): E0223131
Bonekamp, H.; Van Oldenborgh, G.J.; Burgers, G. 2001: Variational assimilation of Tropical Atmosphere-Ocean and expendable bathythermograph data in the Hamburg Ocean Primitive Equation ocean general circulation model, adjusting the surface fluxes in the tropical ocean Journal of Geophysical Research 106(C 8): 16693-16709
Philippe Courtier; Olivier Talagrand 1987: Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. Ii: Numerical Results Quarterly Journal of the Royal Meteorological Society 113(478): 1329-1347