On the merits of using a 3D-FGAT assimilation scheme with an outer loop for atmospheric situations governed by transport

Monthly Weather Review

Volumn 138, Issue 12, 2010, Pages 4509-4522

  Massart, Sébastien ^a   Pajot, Benjamin ^a   Piacentini, Andrea ^a   Pannekoucke, Olivier ^a  

^a CNRS   (France)

Author keywords

Data assimilation; Dvection; Ozone; Transport; Variational analysis

Indexed keywords

ATMOSPHERIC EVOLUTION; ATMOSPHERIC SITUATIONS; CHEMICAL TRANSPORT MODELS; COMPUTING TIME; DATA ASSIMILATION; DVECTION; GREAT CIRCLE; ITERATIVE PROCESS; OUTER LOOP; OZONE MEASUREMENTS; PASSIVE TRACERS; PROS AND CONS; TIME LENGTH; TRANSPORT; VARIATIONAL ANALYSIS; VARIATIONAL DATA ASSIMILATION; WIND SPEED; ZONAL WIND;

ATMOSPHERIC CHEMISTRY; ATMOSPHERIC MOVEMENTS; DATA PROCESSING; OZONE; THREE DIMENSIONAL COMPUTER GRAPHICS; VALUE ENGINEERING;

THREE DIMENSIONAL;

ADVECTION; ATMOSPHERIC CHEMISTRY; CHEMICAL COMPOSITION; DATA ASSIMILATION; ESTIMATION METHOD; OBSERVATIONAL METHOD; OZONE; THREE-DIMENSIONAL MODELING; ZONAL WIND;

EID: 78651251780     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010MWR3237.1     Document Type: Article

References (38)

1. Bennett, A. F., 2002: Inverse Modelling of the Ocean and Atmosphere. Cambridge University Press, 260 pp.
2. Bloom, S. C., L. L. Takacs, A. M. Da Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256-1271.
3. Bousserez, N., and Coauthors, 2007: Evaluation of the MOCAGE chemistry transport model during the ICARTT/ITOP experiment. J. Geophys. Res., 112, D10S42, doi:10.1029/2006JD007595.
4. Buis, S., A. Piacentini, and D. Déclat, 2006: PALM: A computational framework for assembling high performance computing applications. Concurr. Comput., 18, 247-262.
5. Cariolle, D., and H. Teyssèdre, 2007: A revised linear ozone photochemistry parameterization for use in transport and general circulation models: Multi-annual simulations. Atmos. Chem. Phys., 7, 2183-2196.
6. Courtier, P., and O. Talagrand, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. II: Numerical results. Quart. J. Roy. Meteor. Soc., 113, 1329-1347.
7. Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367-1387.
8. Courtier, P., and Coauthors, 1998: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). I: Formulation. Quart. J. Roy. Meteor. Soc., 124, 1783-1807.
9. Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 3385-3396.
10. Dufour, A., M. Amodei, G. Ancellet, and V.-H. Peuch, 2004: Observed and modelled "chemical weather" during ESCOMPTE. Atmos. Res., 74, 161-189.
11. Elbern, H., H. Schmidt, and A. Ebel, 1997: Variational data assimilation for tropospheric chemistry modeling. J. Geophys. Res., 102, 15 967-15 985.
12. Fillion, L., H. L. Mitchell, H. R. Ritchie, and A. N. Staniforth, 1995: The impact of a digital filter finalization technique in a global data assimilation system. Tellus, 47A, 304-323.
13. Fisher, M., and D. J. Lary, 1995: Lagrangian 4-dimensional variational data assimilation of chemical-species. Quart. J. Roy. Meteor. Soc., 121, 1681-1704.
14. Fisher, M., and E. Andersson, 2001: Developmentsin4D-VarandKalman filtering. Tech. Memo. 347, ECMWF, 36 pp.
15. Gandin, L. S., 1963: Objective Analysis of Meteorological Fields. Israel Program for Scientific Translation, 242 pp.
16. Gauthier, P., and J.-N. Thépaut, 2001: Impact of the digital filter as a weak constraint in the preoperational 4DVAR assimilation system of Météo-France. Mon. Wea. Rev., 129, 2089-2102.
17. Gauthier, P., M. Tanguay, S. Laroche, S. Pellerin, and J. Morneau, 2007: Extension of 3DVAR to 4DVAR: Implementationof4DVAR at the Meteorological Service of Canada. Mon. Wea. Rev., 135, 2339-2354.
18. Geer, A., and Coauthors, 2006: The ASSET intercomparison of ozone analyses: Method and first results. Atmos. Chem. Phys., 6, 5445-5474.
19. Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., 82, 35-45.
20. Lahoz, W. A., and Coauthors, 2007: The assimilation of Envisat data (ASSET) project. Atmos. Chem. Phys., 7, 1773-1796.
21. Laroche, S., P. Gauthier, M. Tanguay, S. Pellerin, and J. Morneau, 2007: Impact of the different components of 4DVAR on the global forecast system of the Meteorological Service of Canada. Mon. Wea. Rev., 135, 2355-2364.
22. Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 1177-1194.
23. Lorenc, A. C., and F. Rawlins, 2005: Why does 4D-Var beat 3D-Var? Quart. J. Roy. Meteor. Soc., 131, 3247-3257.
24. Mallat, S., 2009: A Wavelet Tour of Signal Processing: The Sparse Way. 3rd ed. Vol. 1, Academic Press, 832 pp.
25. Massart, S., D. Cariolle, and V.-H. Peuch, 2005: Vers une meilleure représentation de la distribution et de la variabilité de l'ozone atmosphérique par l'assimilation des données satellitaires. C. R. Geosci., 337, 1305-1310.
26. Massart, S., D., A. Piacentini, D. Cariolle, L. El Amraoui, and N. Semane, 2007: Assessment of the quality of the ozone measurements from the Odin/SMR instrument using data assimilation. Can. J. Phys., 85, 1209-1223.
27. Massart, S., C. Clerbaux, D. Cariolle, A. Piacentini, S. Turquety, and J. Hadji-Lazaro, 2009: First steps towards the assimilation of IASI ozone data into the MOCAGE-PALM system. Atmos. Chem. Phys., 9, 5073-5091.
28. Maybeck, P. S., 1979: Stochastic Models, Estimation, and Control. Vol. 1. Academic Press, 423 pp.
29. Pannekoucke, O., and S. Massart, 2008: Estimation of the local diffusion tensor and normalization for heterogeneous correlation modelling using a diffusion equation. Quart. J. Roy. Meteor. Soc., 134, 1425-1438.
30. Sasaki, Y. K., 1958: An objective analysis based on the variational method. J. Meteor. Soc. Japan, 36, 77-88.
31. Sasaki, Y. K., 1970: Some basic formalisms in numerical variational analysis. Mon. Wea. Rev., 98, 875-883.
32. Talagrand, O., 1997: Assimilation of observations: An introduction. J. Meteor. Soc. Japan, 75 (1B), 191-209.
33. Talagrand, O., and P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Quart. J. Roy. Meteor. Soc., 113, 1311-1328.
34. Teyssèdre, H., and Coauthors, 2007: A new tropospheric and stratospheric chemistry and transport model MOCAGE-Climat for multi-year studies: Evaluation of the present-day climatology and sensitivity to surface processes. Atmos. Chem. Phys., 7, 5815-5860.
35. Thacker, W., and R. B. Long, 1988: Fitting dynamics to data. J. Geophys. Res., 93, 1227-1240.
36. Tremolet, Y., 2004: Diagnostics of linear and incremental approximations in 4D-Var. Quart. J. Roy. Meteor. Soc., 130, 2233-2251.
37. Waters, J. W., and Coauthors, 2006: The Earth Observing System Microwave Limb Sounder (EOS MLS) on the Aura satellite. IEEE Trans. Geosci. Remote Sens., 44, 1075-1092.
38. Weaver, A., and P. Courtier, 2001: Correlation modelling on the sphere using a generalized diffusion equation. Quart. J. Roy. Meteor. Soc., 127, 1815-1846.