Data Assimilation in the Geosciences - An overview on methods, issues and perspectives

Kalnay, E., H. Li, T. Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 4-D-Var or ensemble Kalman filter?, Tellus A

2007

Cited alongside, same era.

Lorenc, A. C., and T. Payne, 4D-Var and the butterfly effect: Statistical four-dimensional data assimilation for a wide range of scales, Q J Roy. Meteor. Soc

2007

Cited alongside, same era.

Smith, D. M., and J. M. Murphy, An objective ocean temperature and salinity analysis using covariances from a global climate model, J. Geophys. Res. Oceans

2007

Cited alongside, same era.

Thacker, C., Data assimilation with inequality constraints, Ocean Modell

2007

Cited alongside, same era.

Trémolet, Y., Model-error estimation in 4D-Var, Q J Roy. Meteor. Soc

2007

Cited alongside, same era.

Wang, X., C. Snyder, and T. M. Hamill, On the theoretical equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes, Mon. Weather Rev

2007

Cited alongside, same era.

Wikle, C. K., and L. M. Berliner, A Bayesian tutorial for data assimilation, Physica D

2007

Cited alongside, same era.

Amezcua, J., and P. J. Van Leeuwen, Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach, Tellus A

2014

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Bocquet, M., and P. Sakov, An iterative ensemble Kalman smoother, Q J Roy. Meteor. Soc

2014

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Carrassi, A., R. Weber, V. Guemas, F. Doblas-Reyes, M. Asif, and D. Volpi, Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations, Nonlinear Proc. Geoph

2014

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Counillon, F., I. Bethke, N. Keenlyside, M. Bentsen, L. Bertino, and F. Zheng, Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: A twin experiment, Tellus A

2014

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de Rosnay, P., G. Balsamo, C. Albergel, J. Muñoz-Sabater, and L. Isaksen, Initialisation of land surface variables for numerical weather prediction, Surveys in Geophysics

2014

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Desroziers, G., J.-T. Camino, and L. Berre, 4DEnVar: Link with 4D state formulation of variational assimilation and different possible implementations, Q J Roy. Meteor. Soc

2014

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Fairbairn, D., S. R. Pring, A. C. Lorenc, and I. Roulstone, A comparison of 4DVar with ensemble data assimilation methods, Q J Roy. Meteor. Soc

2014

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Giffin, A., and R. Urniezius, The kalman filter revisited using maximum relative entropy, Entropy

2014

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Gustafsson, N., J. Bojarova, and O. Vignes, A hybrid variational ensemble data assimilation for the HIgh Resolution Limited Area Model (HIRLAM), Nonlinear Proc. Geoph

2014

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Janjić, T., D. McLaughlin, S. E. Cohn, and M. Verlaan, Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms, Mon. Weather Rev

2014

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Ngodock, H., and M. Carrier, A 4DVAR system for the Navy Coastal Ocean Model. Part II: Strong and weak constraint assimilation experiments with real observations in Monterey Bay, Mon. Weather Rev

2014

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Tardif, R., G. J. Hakim, and C. Snyder, Coupled atmosphere–ocean data assimilation experiments with a low-order climate model, Clim. Dyn

2014

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Ueno, G., and N. Nakamura, Iterative algorithm for maximum-likelihood estimation of the observation-error covariance matrix for ensemble-based filters, Q J Roy. Meteor. Soc

2014

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Ades, M., and P. J. van Leeuwen, The equivalent-weights particle filter in a high-dimensional system, Q J Roy. Meteor. Soc

2015

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Barth, A., M. Canter, B. van Schaeybroeck, S. Vannitsem, F. Massonnet, V. Zunz, P. Mathiot, A. Alvera-Azcarate, and J.-M. Beckers, Assimilation of sea surface temperature , ice concentration and ice drift in a model of the Southern Ocean, Ocean Modell

2015

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Bauer, P., A. Thorpe, and G. Brunet, The quiet revolution of numerical weather prediction, Nature

2015

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Berre, L., H. Varella, and G. Desroziers, Modelling of flow-dependent ensemble-based background-error correlations using a wavelet formulation in 4D-Var at Météo-France, Q J Roy. Meteor. Soc

2015

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Hoteit, I., D.-T. Pham, M. Gharamti, and X. Luo, Mitigating observation perturbation sampling errors in the stochastic EnKF, Mon. Weather Rev

2015

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Kleist, D. T., and K. Ide, An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-hybrid results, Mon. Weather Rev

2015

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Law, K., A. Stuart, and K. Zygalakis, Data assimilation: A mathematical introduction

2015

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Lorenc, A. C., N. E. Bowler, A. M. Clayton, S. R. Pring, and D. Fairbairn, Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP, Mon. Weather Rev

2015

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Lu, F., Z. Liu, S. Zhang, and Y. Liu, Strongly coupled data assimilation using leading averaged coupled covariance (LACC). Part I: Simple model study, Mon. Weather Rev

2015

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Palatella, L., and A. Trevisan, Interaction of Lyapunov vectors in the formulation of the nonlinear extension of the Kalman filter, Phys. Rev. E

2015

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Poterjoy, J., and F. Zhang, Systematic comparison of four-dimensional data assimilation methods with and without the tangent linear model using hybrid background error covariance: E4DVar versus 4DEnVar, Mon. Weather Rev

2015

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Raanes, P. N., A. Carrassi, and L. Bertino, Extending the square root method to account for additive forecast noise in ensemble methods, Mon. Weather Rev

2015

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Reich, S., and C. Cotter, Probabilistic Forecasting and Bayesian Data Assimilation

2015

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Santitissadeekorn, N., and C. Jones, Two-stage filtering for joint state-parameter estimation, Mon. Weather Rev

2015

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Simon, E., A. Samuelsen, L. Bertino, and S. Mouysset, Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter, J. Marine Systems

2015

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Slivinski, L., E. Spiller, A. Apte, and B. Sandstede, A hybrid particle-ensemble Kalman filter for Lagrangian data assimilation, Mon. Weather Rev

2015

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Smith, P. J., A. M. Fowler, and A. S. Lawless, Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere–ocean model, Tellus A

2015

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Snyder, C., T. Bengtsson, and T. Morzfeld, Performance bounds for particle filters using the optimal proposal, Mon. Weather Rev

2015

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Tandeo, P., M. Pulido, and F. Lott, Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: Application to a subgrid-scale orography parametrization, Q J Roy. Meteor. Soc

2015

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Tardif, R., G. J. Hakim, and C. Snyder, Coupled atmosphere–ocean data assimilation experiments with a low-order model and CMIP5 model data, Clim. Dyn

2015

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van Leeuwen, P. J., Y. Cheng, and S. Reich, Nonlinear data assimilation

2015

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Weber, R. J., A. Carrassi, and F. J. Doblas-Reyes, Linking the anomaly initialization approach to the mapping paradigm: a proof-of-concept study, Mon. Weather Rev

2015

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Ye, J., N. Kadakia, P. Rozdeba, H. Abarbanel, and J. Quinn, Improved variational methods in statistical data assimilation, Nonlinear Proc. Geoph

2015

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Ying, M., and F. Zhang, An adaptive covariance relaxation method for ensemble data assimilation, Q J Roy. Meteor. Soc

2015

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Asch, M., M. Bocquet, and M. Nodet, Data Assimilation: Methods, Algorithms, and Applications

2016

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Auligné, T., B. Ménétrier, A. C. Lorenc, and M. Buehner, Ensemble-variational integrated localized data assimilation, Mon. Weather Rev

2016

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Bocquet, M., Localization and the iterative ensemble Kalman smoother, Q J Roy. Meteor. Soc

2016

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Carrassi, A., and S. Vannitsem, Deterministic treatment of model error in geophysical data assimilation, in Mathematical Paradigms of Climate Science

2016

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Carrassi, A., V. Guemas, F. Doblas-Reyes, D. Volpi, and M. Asif, Sources of skill in near-term climate prediction: generating initial conditions, Clim. Dyn

2016

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Chustagulprom, N., S. Reich, and M. Reinhardt, A hybrid ensemble transform particle filter for nonlinear and spatially extended dynamical systems, SIAM/ASA J. Uncertainty Quantification

2016

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Counillon, F., N. Keenlyside, I. Bethke, Y. Wang, S. Billeau, M. L. Shen, and M. Bentsen, Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model, Tellus A

2016

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Desroziers, G., E. Arbogast, and L. Berre, Improving spatial localization in 4DEnVar, Q J Roy. Meteor. Soc

2016

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Emili, E., S. Gurol, and D. Cariolle, Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0, Geosci. Model Dev

2016

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Hannart, A., A. Carrassi, M. Bocquet, M. Ghil, P. Naveau, M. Pulido, J. Ruiz, and P. Tandeo, DADA: data assimilation for the detection and attribution of weather and climate-related events, Climatic Change

2016

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Haussaire, J.-M., and M. Bocquet, A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0), Geosci. Model Dev

2016

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Houtekamer, P., and F. Zhang, Review of the ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev

2016

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Kadakia, N., E. Armstrong, D. Breen, U. Morone, A. Daou, D. Margoliash, and H. D. Abarbanel, Nonlinear statistical data assimilation for hvc-RA neurons in the avian song system, Biological cybernetics

2016

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Laloyaux, P., M. Balmaseda, D. Dee, K. Mogensen, and P. Janssen, A coupled data assimilation system for climate reanalysis, Q J Roy. Meteor. Soc

2016

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Mandel, J., E. Bergou, S. Gürol, S. Gratton, and I. Kasanickỳ, Hybrid Levenberg-Marquardt and weak-constraint ensemble Kalman smoother method, Nonlinear Proc. Geoph

2016

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Ni, B., and Q. Zhang, Stability of the Kalman filter for continuous time output error systems, Syst. Control. Lett

2016

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Nino Ruiz, E. D., and A. Sandu, A derivative-free trust region framework for variational data assimilation, J. Comput Appl Math

2016

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Pazo, D., A. Carrassi, and J. Lopez, Data assimilation by delay-coordinate nudging, Q J Roy. Meteor. Soc

2016

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Penny, S. G., and T. Miyoshi, A local particle filter for high dimensional geophysical systems, Nonlinear Proc. Geoph

2016

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Poli, P., H. Hersbach, D. P. Dee, P. Berrisford, A. J. Simmons, F. Vitart, P. Laloyaux, D. G. Tan, C. Peubey, J.-N. Thépaut, et al., Era-20c: An atmospheric reanalysis of the twentieth century, J. Climate

2016

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Poterjoy, J., A localized particle filter for high-dimensional nonlinear systems, Mon. Weather Rev

2016

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Raanes, P. N., On the ensemble rauch-tung-striebel smoother and its equivalence to the ensemble Kalman smoother, Q J Roy. Meteor. Soc

2016

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Rampal, P., S. Bouillon, E. Ólason, and M. Morlighem, neXtSIM: a new Lagrangian sea ice model, Cryosphere

2016

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Slivinski, L., and C. Snyder, Exploring practical estimates of the ensemble size necessary for particle filters, Mon. Weather Rev

2016

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Sluka, T. C., S. G. Penny, E. Kalnay, and T. Miyoshi, Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation, Geophys. Res. Let

2016

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Ueno, G., and N. Nakamura, Bayesian estimation of the observation-error covariance matrix in ensemble-based filters, Q J Roy. Meteor. Soc

2016

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Wang, Y., F. Counillon, and L. Bertino, Alleviating the bias induced by the linear analysis update with an isopycnal ocean model, Q J Roy. Meteor. Soc

2016

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Zhu, M., P.-J. van Leeuwen, and J. Amezcua, Implicit equal-weights particle filter, Q J Roy. Meteor. Soc

2016

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Amezcua, J., M. Goodliff, and P. J. van Leeuwen, A weak-constraint 4DEnsembleVar. Part I: formulation and simple model experiments, Tellus A

2017

Closest in time.

Bannister, R. N., A review of operational methods of variational and ensemble-variational data assimilation, Q J Roy. Meteor. Soc

2017

Closest in time.

Beskos, A., D. Crisan, A. Jasra, K. Kamatani, and Y. Zhou, A stable particle filter for a class of high-dimensional state-space models, Advances in Applied Probability

2017

Closest in time.

Bocquet, M., and A. Carrassi, Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A

2017

Closest in time.

Bocquet, M., K. S. Gurumoorthy, A. Apte, A. Carrassi, C. Grudzien, and C. K. Jones, Degenerate Kalman filter error covariances and their convergence onto the unstable subspace, SIAM/ASA J. Uncertainty Quantification

2017

Closest in time.

Bonan, B., N. K. Nichols, M. J. Baines, and D. Partridge, Data assimilation for moving mesh methods with an application to ice sheet modelling, Nonlinear Proc. Geoph

2017

Closest in time.

Bowler, N. E., A. M. Clayton, M. Jardak, E. Lee, A. C. Lorenc, C. Piccolo, S. R. Pring, M. A. Wlasak, D. M. Barker, G. W. Inverarity, and R. Swinbank, Inflation and localization tests in the development of an ensemble of 4D-ensemble variational assimilations, Q J Roy. Meteor. Soc

2017

Closest in time.

Carrassi, A., M. Bocquet, A. Hannart, and M. Ghil, Estimating model evidence using data assimilation, Q J Roy. Meteor. Soc

2017

Closest in time.

Carson, J., M. Crucifix, S. Preston, and R. D. Wilkinson, Bayesian model selection for the glacial–interglacial cycle, J. R. Stat. Soc. C-Appl

2017

Closest in time.

Dreano, D., P. Tandeo, M. Pulido, B. Ait-El-Fquih, T. Chonavel, and I. Hoteit, Estimating model error covariances in nonlinear state-space models using Kalman smoothing and the expectation-maximisation algorithm, Q J Roy. Meteor. Soc

2017

Closest in time.

Fisher, M., and S. Gürol, Parallelization in the time dimension of four-dimensional variational data assimilation, Q J Roy. Meteor. Soc

2017

Closest in time.

Fletcher, S. J., Data Assimilation for the Geosciences: From Theory to Application

2017

Closest in time.

Gurumoorthy, K. S., C. Grudzien, A. Apte, A. Carrassi, and C. K. Jones, Rank deficiency of Kalman error covariance matrices in linear time-varying system with deterministic evolution, SIAM J. Control Optim

2017

Closest in time.

Janjić, T., N. Bormann, M. Bocquet, J. A. Carton, S. E. Cohn, S. L. Dance, S. N. Losa, N. K. Nichols, R. Potthast, J. A. Waller, and P. Weston, On the representation error in data assimilation, Q J Roy. Meteor. Soc

2017

Closest in time.

Kotsuki, S., T. Miyoshi, K. Terasaki, G. Y. Lien, and E. Kalnay, Assimilating the global satellite mapping of precipitation data with the nonhydrostatic icosahedral atmospheric model (NICAM), Journal of Geophysical Research

2017

Closest in time.

Liu, Y., J.-M. Haussaire, M. Bocquet, Y. Roustan, O. Saunier, and A. Mathieu, Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima-Daiichi accidental releases of radionuclides, Q J Roy. Meteor. Soc

2017

Closest in time.

Penny, S. G., and T. M. Hamill, Coupled data assimilation for integrated earth system analysis and prediction, Bull. Amer. Meteor. Soc

2017

Closest in time.

Robert, S., and H. R. Künsch, Localizing the ensemble Kalman particle filter, Tellus A

2017

Closest in time.

Vannitsem, S., Predictability of large-scale atmospheric motions: Lyapunov exponents and error dynamics, Chaos

2017

Closest in time.

Xie, J., L. Bertino, F. Counillon, K. A. Lisæter, and P. Sakov, Quality assessment of the topaz4 reanalysis in the arctic over the period 1991-2013, Ocean Sci

2017

Closest in time.

Yano, J.-I., M. Z. Ziemiański, M. Cullen, P. Termonia, J. Onvlee, L. Bengtsson, A. Carrassi, R. Davy, A. Deluca, S. L. Gray, et al., Scientific challenges of convective-scale numerical weather prediction, Bull. Amer. Meteor. Soc

2017

Closest in time.

Aalstad, K., S. Westermann, T. Chuler, J. Boike, and L. Bertino, Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere

2018

Closest in time.

Evensen, G., Analysis of iterative ensemble smoothers for solving inverse problems, Accepted for publication in Computat. Geosci., 2018

2018

Closest in time.

Farchi, A., and M. Bocquet, Review article: Comparison of local particle filters and new implementations, Nonlinear Proc. Geoph. Disc

2018

Closest in time.

Grudzien, C., A. Carrassi, and M. Bocquet, Chaotic dynamics and the role of covariance inflation for reduced rank kalman filters with model error, Nonlinear Proc. Geoph. Disc

2018

Closest in time.

Jardak, M., and O. Talagrand, Ensemble variational assimilation as a probabilistic estimator. part i: The linear and weak non-linear case, Nonlinear Proc. Geoph. Disc

2018

Closest in time.

Pulido, M., P. Tandeo, M. Bocquet, A. Carrassi, and M. Lucini, Stochastic parameterization identification using ensemble kalman filtering combined with maximum likelihood methods, Tellus A

2018

Closest in time.