J. L. Anderson and S. L. Anderson, A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts, Monthly Weather Review, vol.127, issue.12, pp.2741-2758, 1999.
DOI : 10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2

T. W. Anderson and D. A. Darling, Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes, The Annals of Mathematical Statistics, vol.23, issue.2, pp.193-212, 1952.
DOI : 10.1214/aoms/1177729437

E. Andersson and H. Jä-rvinen, Variational quality control, Quarterly Journal of the Royal Meteorological Society, vol.122, issue.554, pp.697-722, 1999.
DOI : 10.1002/qj.49712555416

?. , M. Fisher, E. Hó-lm, L. Isaksen, G. Radnó et al., Will the 4D-Var approach be defeated by nonlinearity?, 2005.

D. Auroux, Generalization of the dual variational data assimilation algorithm to a nonlinear layered quasi-geostrophic ocean model, Inverse Problems, vol.23, issue.6, pp.2485-2503
DOI : 10.1088/0266-5611/23/6/013
URL : https://hal.archives-ouvertes.fr/inria-00189641

O. E. Barndorff-nielsen and D. R. Cox, Asymptotic Techniques for Use in Statistics, Meteor. Monogr, vol.252, issue.31, p.pp, 1989.
DOI : 10.1007/978-1-4899-3424-6

R. Bellman, Adaptive Control Processes: A Guided Tour, 1961.
DOI : 10.1515/9781400874668

T. Bengtsson, C. Snyder, and D. Nychka, Toward a nonlinear ensemble filter for high-dimensional systems, Journal of Geophysical Research: Atmospheres, vol.104, issue.C5, pp.10-1029, 2003.
DOI : 10.1029/2002JD002900

M. L. Berliner and C. K. Wikle, Approximate importance sampling Monte Carlo for data assimilation, Physica D: Nonlinear Phenomena, vol.230, issue.1-2, pp.37-49, 2007.
DOI : 10.1016/j.physd.2006.07.031

L. Bertino, G. Evensen, and H. Wackernagel, Sequential Data Assimilation Techniques in Oceanography, International Statistical Review, vol.12, issue.4, pp.223-241, 2003.
DOI : 10.1111/j.1751-5823.2003.tb00194.x

C. H. Bishop, B. J. Etherton, and S. J. Majumdar, Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Monthly Weather Review, vol.129, issue.3, pp.420-436, 2001.
DOI : 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2

M. Bocquet, Grid resolution dependence in the reconstruction of an atmospheric tracer source, Nonlinear Processes in Geophysics, vol.12, issue.2, pp.219-234, 2005.
DOI : 10.5194/npg-12-219-2005
URL : https://hal.archives-ouvertes.fr/hal-00331081

J. M. Borwein and A. S. Lewis, Convex Analysis and Nonlinear Optimization: Theory and Examples, 2000.

M. Buehner, P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part II: One-Month Experiments with Real Observations, Monthly Weather Review, vol.138, issue.5, pp.1567-1586, 2010.
DOI : 10.1175/2009MWR3158.1

R. Buizza and A. Montani, Targeting Observations Using Singular Vectors, Journal of the Atmospheric Sciences, vol.56, issue.17, pp.2965-2985, 1999.
DOI : 10.1175/1520-0469(1999)056<2965:TOUSV>2.0.CO;2

G. Burgers, P. J. Van-leeuwen, and G. Evensen, Analysis Scheme in the Ensemble Kalman Filter, Monthly Weather Review, vol.126, issue.6, pp.1719-1724, 1998.
DOI : 10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2

M. A. Cane, A. Kaplan, R. N. Miller, B. Y. Tang, E. C. Hackert et al., Mapping tropical Pacific sea level: Data assimilation via a reduced state space Kalman filter, Journal of Geophysical Research: Oceans, vol.46, issue.C10, pp.599-621, 1996.
DOI : 10.1029/96JC01684

A. Carrassi, A. Trevisan, and F. Uboldi, Adaptive observations and assimilation in the unstable subspace by breeding on the data-assimilation system, pp.101-113, 2007.

?. , M. Ghil, A. Trevisan, and F. Uboldi, Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system, Chaos, vol.18, p.23112, 2008.

B. Chapnik, G. Desroziers, F. Rabier, and O. Talagrand, Diagnosis and tuning of observational error in a quasi-operational data assimilation setting, Quarterly Journal of the Royal Meteorological Society, vol.123, issue.615, pp.543-565, 2006.
DOI : 10.1256/qj.04.102

S. E. Cohn, An introduction to estimation theory, J. Meteor. Soc. Japan, vol.75, pp.257-288, 1997.

?. , A. Da-silva, J. Guo, M. Sienkiewicz, and D. Lamich, Assessing the effects of data selection with the DAO physicalspace statistical analysis system, Mon. Wea. Rev, vol.126, pp.2913-2926, 1998.

P. Courtier, Dual formulation of four-dimensional variational assimilation, Quarterly Journal of the Royal Meteorological Society, vol.121, issue.544, pp.2449-2461, 1997.
DOI : 10.1002/qj.49712354414

?. and O. Talagrand, Variational assimilation of meteorological observation with the adjoint vorticity equation. II: Numerical results, Quart. J. Roy. Meteor. Soc, vol.113, pp.1329-1347, 1987.

T. M. Cover and J. A. Thomas, Elements of Information Theory, 1991.

D. N. Daescu and I. M. Navon, Adaptive observations in the context of 4D-Var data assimilation, Meteorology and Atmospheric Physics, vol.85, issue.4, pp.205-226, 2004.
DOI : 10.1007/s00703-003-0011-5

X. Davoine and M. Bocquet, Inverse modelling-based reconstruction of the Chernobyl source term available for long-range transport, Atmospheric Chemistry and Physics, vol.7, issue.6, pp.1549-1564, 2007.
DOI : 10.5194/acp-7-1549-2007
URL : https://hal.archives-ouvertes.fr/hal-00328489

D. Moral and P. , Feynman?Kac Formulae: Genealogical and Interacting Particle Systems with Applications, 2004.

G. Desroziers, L. Berre, B. Chapnik, and P. Poli, Diagnosis of observation, background and analysis-error statistics in observation space, Quarterly Journal of the Royal Meteorological Society, vol.75, issue.613, pp.3385-3396, 2005.
DOI : 10.1256/qj.05.108

A. Doucet, S. Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, vol.10, issue.3, pp.197-208, 2000.
DOI : 10.1023/A:1008935410038

?. , N. De-freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice, 2001.

G. Evensen, Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, Journal of Geophysical Research, vol.109, issue.Part 4, pp.143-153, 1994.
DOI : 10.1029/94JC00572

G. L. Eyink and S. Kim, A Maximum Entropy Method for Particle Filtering, Journal of Statistical Physics, vol.108, issue.5, pp.1071-1128, 2006.
DOI : 10.1007/s10955-006-9124-9

M. Fisher, M. Leutbecher, and G. A. Kelly, On the equivalence between Kalman smoothing and weak-constraint four-dimensional variational data assimilation, Quarterly Journal of the Royal Meteorological Society, vol.131, issue.613, pp.3235-3246, 2005.
DOI : 10.1256/qj.04.142

S. J. Fletcher and M. Zupanski, A data assimilation method for log-normally distributed observational errors, Quarterly Journal of the Royal Meteorological Society, vol.58, issue.621, pp.2505-2519, 2006.
DOI : 10.1256/qj.05.222

T. Fujita, D. J. Stensrud, and D. C. Dowell, Surface Data Assimilation Using an Ensemble Kalman Filter Approach with Initial Condition and Model Physics Uncertainties, Monthly Weather Review, vol.135, issue.5, pp.1846-1868, 2007.
DOI : 10.1175/MWR3391.1

C. W. Gardiner, Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences, p.415, 2004.

G. Gaspari and S. E. Cohn, Construction of correlation functions in two and three dimensions, Quarterly Journal of the Royal Meteorological Society, vol.4, issue.2, pp.723-757, 1999.
DOI : 10.1002/qj.49712555417

P. Gauthier, Chaos and quadri-dimensional data assimilation: a study based on the Lorenz model, Tellus A: Dynamic Meteorology and Oceanography, vol.8, issue.1, pp.2-17, 1992.
DOI : 10.3402/tellusa.v44i1.14938

W. R. Gilks and C. Berzuini, Following a moving target-Monte Carlo inference for dynamic Bayesian models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.1, pp.127-146, 2001.
DOI : 10.1111/1467-9868.00280

N. J. Gordon, D. J. Salmond, and A. F. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F Radar and Signal Processing, vol.140, issue.2, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

T. M. Hamill, J. S. Whitaker, and C. Snyder, Distance-Dependent Filtering of Background Error Covariance Estimates in an Ensemble Kalman Filter, Monthly Weather Review, vol.129, issue.11, pp.2776-2790, 2001.
DOI : 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2

J. Handschin and D. Mayne, Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering???, International Journal of Control, vol.83, issue.5, pp.547-559, 1969.
DOI : 10.1016/0022-0396(67)90023-X

J. Harlim and B. R. Hunt, A non-Gaussian ensemble filter for assimilating infrequent noisy observations, pp.225-237, 2007.

K. Haven, A. Majda, and R. Abramov, Quantifying predictability through information theory: small sample estimation in a non-Gaussian framework, Journal of Computational Physics, vol.206, issue.1, pp.334-362, 2005.
DOI : 10.1016/j.jcp.2004.12.008

A. W. Heemink, M. J. Verlaan, and . Segers, Variance Reduced Ensemble Kalman Filtering, Monthly Weather Review, vol.129, issue.7, pp.1718-1728, 2001.
DOI : 10.1175/1520-0493(2001)129<1718:VREKF>2.0.CO;2

E. Hó-lm, Humidity control variable and supersaturation, Proc. Workshop on Flow-Dependent Aspects of Data Assimilation, pp.143-150, 2007.

?. , E. Andersson, A. Beljaars, P. Lopez, J. Mahfouf et al., Assimilation and modelling of the hydrological cycle: ECMWF's status and plans, 2002.

I. Hoteit, A reduced-order simulated annealing approach for four-dimensional variational data assimilation in meteorology and oceanography, International Journal for Numerical Methods in Fluids, vol.14, issue.4, pp.1181-1199, 2008.
DOI : 10.1002/fld.1794

?. , D. Pham, G. Triantafyllou, and G. Korres, A new approximate solution of the optimal nonlinear filter for data assimilation in meteorology and oceanography, Mon. Wea. Rev, vol.136, pp.317-334, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00853121

P. L. Houtekamer and H. L. Mitchell, Data Assimilation Using an Ensemble Kalman Filter Technique, Monthly Weather Review, vol.126, issue.3, pp.796-811, 1998.
DOI : 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2

?. , ?. , and X. Deng, Model error representation in an operational ensemble Kalman filter, Mon. Wea. Rev, vol.137, pp.2126-2143, 2009.

P. J. Huber, Robust Regression: Asymptotics, Conjectures and Monte Carlo, The Annals of Statistics, vol.1, issue.5, pp.799-821, 1973.
DOI : 10.1214/aos/1176342503

B. R. Hunt and C. , Four-dimensional ensemble Kalman filtering, pp.273-277, 2004.

A. Hyvä-rinen and E. Oja, Independent component analysis: algorithms and applications, Neural Networks, vol.13, issue.4-5, pp.411-430, 2000.
DOI : 10.1016/S0893-6080(00)00026-5

K. Ide, P. Courtier, M. Ghil, and A. Lorenc, Unified notation for data assimilation: Operational, sequential and variational, J. Meteor. Soc. Japan, vol.75, pp.181-189, 1999.

A. H. Jazwinski, Stochastic Processes and Filtering Theory, 1970.

G. Kitagawa, Non-Gaussian state-space modeling of nonstationary time series, J. Amer. Stat. Assoc, vol.82, pp.1032-1063, 1987.

R. Kleeman, Measuring Dynamical Prediction Utility Using Relative Entropy, Journal of the Atmospheric Sciences, vol.59, issue.13, pp.2057-2072, 2002.
DOI : 10.1175/1520-0469(2002)059<2057:MDPUUR>2.0.CO;2

J. Krü-ger, Simulated Annealing: A Tool for Data Assimilation into an Almost Steady Model State, Journal of Physical Oceanography, vol.23, issue.4, pp.679-688, 1993.
DOI : 10.1175/1520-0485(1993)023<0679:SAATFD>2.0.CO;2

M. Krysta and M. Bocquet, Source reconstruction of an accidental radionuclide release at European scale, Quarterly Journal of the Royal Meteorological Society, vol.33, issue.623, pp.529-544, 2007.
DOI : 10.1002/qj.3
URL : https://hal.archives-ouvertes.fr/inria-00633475

S. Kullback, Information Theory and Statistics, p.pp, 1959.

S. Laroche and P. Gauthier, A validation of the incremental formulation of 4D variational data assimilation in a nonlinear barotropic flow, pp.557-572, 1998.

C. Lauvernet, J. Brankart, F. Castruccio, G. Broquet, P. Braseur et al., A truncated Gaussian filter for data assimilation with inequality constraints: Application to the hydrostatic stability condition in ocean models, Ocean Modelling, vol.27, issue.1-2, pp.1-17, 2009.
DOI : 10.1016/j.ocemod.2008.10.007

W. G. Lawson and J. A. Hansen, Implications of Stochastic and Deterministic Filters as Ensemble-Based Data Assimilation Methods in Varying Regimes of Error Growth, Monthly Weather Review, vol.132, issue.8, pp.1966-1981, 2004.
DOI : 10.1175/1520-0493(2004)132<1966:IOSADF>2.0.CO;2

L. Dimet, F. , and O. Talagrand, Variational algotrithms for analysis and assimilation of meteorological observations: Theoretical aspects, pp.97-110, 1986.

?. , H. Ngodock, B. Luong, and J. Verron, Sensitivity analysis in variational data assimilation, J. Meteor. Soc. Japan, vol.75, pp.245-255, 1997.

C. E. Leith, Theoretical Skill of Monte Carlo Forecasts, Monthly Weather Review, vol.102, issue.6, pp.409-418, 1974.
DOI : 10.1175/1520-0493(1974)102<0409:TSOMCF>2.0.CO;2

P. F. Lermusiaux and A. R. Robinson, Data Assimilation via Error Subspace Statistical Estimation.Part I: Theory and Schemes, Monthly Weather Review, vol.127, issue.7, pp.1385-1407, 1999.
DOI : 10.1175/1520-0493(1999)127<1385:DAVESS>2.0.CO;2

H. W. Lilliefors, On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown, Journal of the American Statistical Association, vol.35, issue.318, pp.399-402, 1967.
DOI : 10.1214/aoms/1177728726

J. Lions, O. P. Manley, R. Temam, and S. Wang, Physical Interpretation of the Attractor Dimension for the Primitive Equations of Atmospheric Circulation, Journal of the Atmospheric Sciences, vol.54, issue.9, pp.1137-1143, 1997.
DOI : 10.1175/1520-0469(1997)054<1137:PIOTAD>2.0.CO;2

A. C. Lorenc, Analysis methods for numerical weather prediction, Quarterly Journal of the Royal Meteorological Society, vol.108, issue.474, pp.1177-1194, 1986.
DOI : 10.1002/qj.49711247414

?. and T. Payne, 4D-Var and the butterfly effect: Statistical four-dimensional data assimilation for a wide range of scales, Quart. J. Roy. Meteor. Soc, vol.133, pp.607-614, 2007.

E. N. Lorenz, Deterministic Nonperiodic Flow, Journal of the Atmospheric Sciences, vol.20, issue.2, pp.130-141, 1963.
DOI : 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

D. J. Mackay, Information Theory, Inference and Learning Algorithms, 2003.

Z. Meng and F. Zhang, Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part II: Imperfect Model Experiments, Monthly Weather Review, vol.135, issue.4, pp.1403-1423, 2007.
DOI : 10.1175/MWR3352.1

R. N. Miller, M. Ghil, and F. Gauthiez, Advanced Data Assimilation in Strongly Nonlinear Dynamical Systems, Journal of the Atmospheric Sciences, vol.51, issue.8, pp.1037-1056, 1994.
DOI : 10.1175/1520-0469(1994)051<1037:ADAISN>2.0.CO;2

?. , E. F. Carter, and S. T. Blue, Data assimilation into nonlinear stochastic models, pp.167-194, 1999.

H. L. Mitchell and P. L. Houtekamer, Ensemble Kalman Filter Configurations and Their Performance with the Logistic Map, Monthly Weather Review, vol.137, issue.12, pp.4325-4343, 2009.
DOI : 10.1175/2009MWR2823.1

S. Nakano, G. Ueno, and T. Higuchi, Merging particle filter for sequential data assimilation, Nonlinear Processes in Geophysics, vol.14, issue.4, pp.395-408, 2007.
DOI : 10.5194/npg-14-395-2007
URL : https://hal.archives-ouvertes.fr/hal-00302882

L. Nerger, W. Hiller, and J. Schrö-ter, A comparison of error subspace Kalman filters, pp.715-735, 2005.

K. Nodop, R. Connolly, and F. Girardi, The field campaigns of the European Tracer Experiment (ETEX), Atmospheric Environment, vol.32, issue.24, pp.4095-4108, 1998.
DOI : 10.1016/S1352-2310(98)00190-3

N. Papadakis, Assimilation de donné es images: Application au suivi de courbes et de champs de vecteurs (Image data assimilation: Application to curve and vector fields tracking), 2007.

D. Patil, R. Hunt, E. Kalnay, J. A. Yorke, and E. Ott, Local Low Dimensionality of Atmospheric Dynamics, Physical Review Letters, vol.86, issue.26, pp.5878-5881, 2001.
DOI : 10.1103/PhysRevLett.86.5878

D. T. Pham, Stochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems, Monthly Weather Review, vol.129, issue.5, pp.1194-1207, 2001.
DOI : 10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2
URL : https://hal.archives-ouvertes.fr/inria-00073082

?. , J. Verron, and M. Roubaud, A singular evolutive extended Kalman filter for data assimilation in oceanography, J. Mar. Syst, vol.16, pp.323-340, 1998.

C. A. Pires and R. Perdigã-o, Non-Gaussianity and Asymmetry of the Winter Monthly Precipitation Estimation from the NAO, Monthly Weather Review, vol.135, issue.2, pp.430-448, 2007.
DOI : 10.1175/MWR3407.1

?. , R. Vautard, and O. Talagrand, On extending the limits of variational assimilation in nonlinear chaotic systems, pp.96-121, 1996.

?. , O. Talagrand, and M. Bocquet, Diagnosis and impacts of non-Gaussianity of innovations in data assimilation, Physica D, vol.239, pp.1701-1717, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00538662

F. Rabier, H. Jä-rvinen, E. Klinker, J. Mahfouf, and A. Simmons, The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics, Quarterly Journal of the Royal Meteorological Society, vol.121, issue.564, pp.1143-1170, 2000.
DOI : 10.1002/qj.49712656415

C. D. Rodgers, Inverse Methods for Atmospheric Sounding, Series on Atmospheric, Oceanic and Planetary Physics, 2000.
DOI : 10.1142/3171

P. Sakov and P. R. Oke, Implications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters, Monthly Weather Review, vol.136, issue.3, pp.1042-1053, 2008.
DOI : 10.1175/2007MWR2021.1

S. S. Shapiro and M. B. Wilk, An analysis of variance test for normality (complete samples), Biometrika, vol.52, issue.3-4, pp.591-611, 1965.
DOI : 10.1093/biomet/52.3-4.591

B. W. Silverman, Density Estimation for Statistics and Data Analysis, 1986.
DOI : 10.1007/978-1-4899-3324-9

E. Simon and L. Bertino, Application of the Gaussian anamorphosis to assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic with the EnKF: a twin experiment, Ocean Science, vol.5, issue.4, pp.495-510, 2009.
DOI : 10.5194/os-5-495-2009

C. Snyder, T. Bengtsson, P. Bickel, and J. L. Anderson, Obstacles to High-Dimensional Particle Filtering, Monthly Weather Review, vol.136, issue.12, pp.4629-4640, 2008.
DOI : 10.1175/2008MWR2529.1

E. T. Spiller, A. Budhiraja, K. Ide, and C. K. Jones, Modified particle filter methods for assimilating Lagrangian data into a point-vortex model, Physica D: Nonlinear Phenomena, vol.237, issue.10-12, pp.1498-1506, 2008.
DOI : 10.1016/j.physd.2008.03.023

O. Talagrand and P. Courtier, Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory, Quarterly Journal of the Royal Meteorological Society, vol.8, issue.10, pp.1311-1328, 1987.
DOI : 10.1002/qj.49711347812

M. K. Tippett, J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, Ensemble Square Root Filters*, Monthly Weather Review, vol.131, issue.7, pp.1485-1490, 2003.
DOI : 10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2

Y. Trémolet, Diagnostics of linear and incremental approximations in 4D-Var, Quarterly Journal of the Royal Meteorological Society, vol.45, issue.601, pp.2233-2251, 2004.
DOI : 10.1256/qj.03.33

J. J. Tribbia and D. P. Baumhefner, Scale Interactions and Atmospheric Predictability: An Updated Perspective, Monthly Weather Review, vol.132, issue.3, pp.703-713, 2004.
DOI : 10.1175/1520-0493(2004)132<0703:SIAAPA>2.0.CO;2

B. Uzunoglu, Adaptive observations in ensemble data assimilation, Computer Methods in Applied Mechanics and Engineering, vol.196, issue.41-44, pp.4207-4221, 2007.
DOI : 10.1016/j.cma.2007.04.004

R. Van-der-merwe, A. Doucet, N. De-freitas, E. Wan, and P. J. , The unscented particle filter Particle filtering in geophysical systems, Mon. Wea. Rev, vol.137, pp.4089-4114, 2000.

M. Verlaan and A. W. Heemink, Nonlinearity in Data Assimilation Applications: A Practical Method for Analysis, Monthly Weather Review, vol.129, issue.6, pp.1578-1589, 2001.
DOI : 10.1175/1520-0493(2001)129<1578:NIDAAA>2.0.CO;2

J. S. Whitaker and T. M. Hamill, Ensemble Data Assimilation without Perturbed Observations, Monthly Weather Review, vol.130, issue.7, pp.1913-1924, 2002.
DOI : 10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2

L. Wu, V. Mallet, M. Bocquet, and B. Sportisse, A comparison study of data assimilation algorithms for ozone forecasts, Journal of Geophysical Research, vol.3, issue.D24, pp.10-1029, 2008.
DOI : 10.1029/2008JD009991
URL : https://hal.archives-ouvertes.fr/inria-00582376

X. Xiong, I. M. Navon, and B. Uzunoglu, A note on the particle filter with posterior Gaussian resampling, pp.456-460, 2006.

M. Zakai, On the optimal filtering of diffusion processes, Zeitschrift f???r Wahrscheinlichkeitstheorie und Verwandte Gebiete, vol.2, issue.3, pp.230-243, 1969.
DOI : 10.1007/BF00536382

M. Zupanski, Maximum Likelihood Ensemble Filter: Theoretical Aspects, Monthly Weather Review, vol.133, issue.6, pp.1710-1726, 2005.
DOI : 10.1175/MWR2946.1