Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation

Aksoy, A., 2006, Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model, 134, 2951

Aksoy, A., 2009, A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses, 137, 1805

Aksoy, A., 2010, A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: Short-range ensemble forecasts, 138, 1273

Aksoy, A., 2013, Assimilation of high-resolution tropical cyclone observations with an ensemble Kalman filter using NOAA/AOML/HRD’s HEDAS: Evaluation of the 2008–11 vortex-scale analyses, 141, 1842

Albers, S. C., 1996, The Local Analysis and Prediction System (LAPS): Analysis of clouds, precipitation, and temperature, 11, 273

Amezcua, J., 2014, Ensemble transform Kalman–Bucy filters, 140, 995

Anderson, J. L., 2001, An ensemble adjustment Kalman filter for data assimilation, 129, 2884

Anderson, J. L., 2003, A local least squares framework for ensemble filtering, 131, 634

Anderson, J. L., 2007, Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter, 230, 99

Anderson, J. L., 2009, Spatially and temporally varying adaptive covariance inflation for ensemble filters, 61A, 72

Anderson, J. L., 2012, Localization and sampling error correction in ensemble Kalman filter data assimilation, 140, 2359

Anderson, J. L., 2016, Reducing correlation sampling error in ensemble Kalman filter data assimilation, 144, 913

Anderson, J. L., 1999, A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, 127, 2741

Anderson, J. L., 2009, The Data Assimilation Research Testbed: A community facility, 90, 1283

Annan, J. D., 2004, On the orthogonality of bred vectors, 132, 843

Annan, J. D., 2005, Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter, 8, 135

Aravéquia, J. A., 2011, Evaluation of a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter, 139, 1932

Ballish, B., 1992, Incremental nonlinear normal-mode initialization, 120, 1723

Barker, D. M., 2005, Southern high-latitude ensemble data assimilation in the Antartic mesoscale prediction system, 133, 3431

Barker, D. M., 2012, The Weather Research and Forecasting Model’s Community Variational/Ensemble Data Assimilation System: WRFDA, 93, 831

Bergemann, K., 2010, A mollified ensemble Kalman filter, 136, 1636

Berner, J., 2009, A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system, 66, 603

Bishop, C. H., 1999, Ensemble transformation and adaptive observations, 56, 1748

Bishop, C. H., 2009, Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models, 61A, 84

Bishop, C. H., 2001, Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects, 129, 420

Bloom, S. C., 1996, Data assimilation using incremental analysis updates, 124, 1256

Bonavita, M., 2011, 303

Bonavita, M., 2010, Ensemble data assimilation with the CNMCA regional forecasting system, 136, 132

Bonavita, M., 2012, On the use of EDA background error variances in the ECMWF 4D-Var, 138, 1540

Bonavita, M., 2015, EnKF and hybrid gain ensemble data assimilation. Part II: EnKF and hybrid gain results, 143, 4865

Bonavita, M., 2016, The evolution of the ECMWF hybrid data assimilation system, 142, 287

Bormann, N., 2010, Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data, 136, 1036

Bowler, N. E., 2008, The MOGREPS short-range ensemble prediction system, 134, 703

Brankart, J.-M., 2009, Efficient parameterization of the observation error covariance matrix for square root or ensemble Kalman filters: Application to ocean altimetry, 137, 1908

Buehner, M., 2012, Evaluation of a spatial/spectral covariance localization approach for atmospheric data assimilation, 140, 617

Buehner, M., 2010a, Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description and single-observation experiments, 138, 1550

Buehner, M., 2010b, Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations, 138, 1567

Buehner, M., 2015, Implementation of deterministic weather forecasting systems based on ensemble–variational data assimilation at Environment Canada. Part I: The global system, 143, 2532

Buizza, R., 1999, Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, 125, 2887

Burgers, G., 1998, Analysis scheme in the ensemble Kalman filter, 126, 1719

Campbell, W. F., 2010, Vertical covariance localization for satellite radiances in ensemble Kalman filters, 138, 282

Candille, G., 2009, The multiensemble approach: The NAEFS example, 137, 1655

Candille, G., 2005, Evaluation of probabilistic prediction systems for a scalar variable, 131, 2131

Candille, G., 2008, Impact of observational error on the validation of ensemble prediction systems, 134, 959

Cardinali, C., 2009, Monitoring the observation impact on the short-range forecast, 135, 239

Caron, J.-F., 2015, Implementation of deterministic weather forecasting systems based on ensemble-variational data assimilation at Environment Canada. Part II: The regional system, 143, 2560

Caya, A., 2005, A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation, 133, 3081

Chang, C.-C., 2014a, Applications of the mean recentering scheme to improve typhoon track prediction: A case study of typhoon Nanmadol (2011), 92, 559

Chang, W., 2014b, Radar data assimilation in the Canadian high-resolution ensemble Kalman filter system: Performance and verification with real summer cases, 142, 2118

Charron, M., 2010, Toward random sampling of model error in the Canadian Ensemble Prediction System, 138, 1877

Clayton, A. M., 2013, Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office, 139, 1445

Cohn, S. E., 1991, The behavior of forecast error covariances for a Kalman filter in two dimensions, 119, 1757

Cohn, S. E., 1998, Assessing the effects of data selection with the DAO physical-space statistical analysis system, 126, 2913

Compo, G. P., 2006, Feasibility of a 100-year reanalysis using only surface pressure data, 87, 175

Daley, R., 1991

Daley, R., 1995, Estimating the wind field from chemical constituent observations: Experiments with a one-dimensional extended Kalman filter, 123, 181

Daley, R., 1986, Estimates of global analysis error from the global weather experiment observational network, 114, 1642

Dee, D. P., 1995, On-line estimation of error covariance parameters for atmospheric data assimilation, 123, 1128

Dee, D. P., 2005, Bias and data assimilation, 131, 3323

Derber, J. C., 1998, The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system, 126, 2287

Derber, J. C., 1999, A reformulation of the background error covariance in the ECMWF global data assimilation system, 51A, 195

Desroziers, G., 2005, Diagnosis of observation, background and analysis-error statistics in observation space, 131, 3385

Dillon, M. E., 2016, Application of the WRF-LETKF data assimilation system over southern South America: Sensitivity to model physics, 31, 217

Dong, J., 2013, Assimilation of radial velocity and reflectivity data from coastal WSR-88D radars using an ensemble Kalman filter for the analysis and forecast of landfalling hurricane Ike (2008), 139, 467

Dowell, D. C., 2004, Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments, 132, 1982

Dowell, D. C., 2011, Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on storm-scale analyses, 139, 272

2011

English, S. J., 2000, A comparison of the impact of TOVS and ATOVS satellite sounding data on the accuracy of numerical weather forecasts, 126, 2911

Errico, R. M., 2013, Development and validation of observing-system simulation experiments at NASA’s Global Modeling and Assimilation Office, 139, 1162

Evensen, G., 1994, Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, 99, 10 143

Evensen, G., 2003, The ensemble Kalman filter: Theoretical formulation and practical implementation, 53, 343

Evensen, G., 2004, Sampling strategies and square root analysis schemes for the EnKF, 54, 539

Evensen, G., 2009, The ensemble Kalman filter for combined state and parameter estimation, 29, 83

Fertig, E. J., 2007, A comparative study of 4D-VAR and a 4D ensemble Kalman filter: Perfect model simulations with Lorenz-96, 59A, 96

Fillion, L., 1995, The impact of a digital filter finalization technique in a global data assimilation system, 47A, 304

Fisher, M., 2004, 45

Fisher, M., 2012, 189

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Flowerdew, J., 2015, Towards a theory of optimal localisation, 67A, 25257

Frehlich, R., 2006, Adaptive data assimilation including the effect of spatial variations in observation error, 132, 1225

Fujita, T., 2007, Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties, 135, 1846

Gaspari, G., 1999, Construction of correlation functions in two and three dimensions, 125, 723

Gauthier, P., 2001, Impact of the digital filter as a weak constraint in the preoperational 4DVAR assimilation system of Météo-France, 129, 2089

Gauthier, P., 2007, Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada, 135, 2339

Geer, A. J., 2011, Observation errors in all-sky data assimilation, 137, 2024

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Gilleland, E., 2010, Verifying forecasts spatially, 91, 1365

Golub, G. H., 1996

Gorin, V. E., 2011, Estimation of multivariate observation-error statistics for AMSU-A data, 139, 3765

Grell, G. A., 2002, A generalized approach to parameterizing convection combining ensemble and data assimilation techniques, 29

Greybush, S. J., 2011, Balance and ensemble Kalman filter localization techniques, 139, 511

Ha, S., 2015, A comparison of model error representations in mesoscale ensemble data assimilation, 143, 3893

Hager, G., 2011

Hamill, T. M., 2001, Interpretation of rank histograms for verifying ensemble forecasts, 129, 550

Hamill, T. M., 2006

Hamill, T. M., 2000, A hybrid ensemble Kalman filter-3D variational analysis scheme, 128, 2905

Hamill, T. M., 2011, What constrains spread growth in forecasts initialized from ensemble Kalman filters?, 139, 117

Hamill, T. M., 2001, Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter, 129, 2776

Hamrud, M., 2015, EnKF and hybrid gain ensemble data assimilation. Part I: EnKF implementation, 143, 4847

Hersbach, H., 2000, Decomposition of the continuous ranked probability score for ensemble prediction systems, 15, 559

Houtekamer, P. L., 1993, Global and local skill forecasts, 121, 1834

Houtekamer, P. L., 2011, 163

Houtekamer, P. L., 1997, Using ensemble forecasts for model validation, 125, 2416

Houtekamer, P. L., 1998, Data assimilation using an ensemble Kalman filter technique, 126, 796

Houtekamer, P. L., 2001, A sequential ensemble Kalman filter for atmospheric data assimilation, 129, 123

Houtekamer, P. L., 2005, Ensemble Kalman filtering, 131, 3269

Houtekamer, P. L., 1996, A system simulation approach to ensemble prediction, 124, 1225

Houtekamer, P. L., 2005, Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations, 133, 604

Houtekamer, P. L., 2009, Model error representation in an operational ensemble Kalman filter, 137, 2126

Houtekamer, P. L., 2014a, Higher resolution in an operational ensemble Kalman filter, 142, 1143

Houtekamer, P. L., 2014b, Parallel implementation of an ensemble Kalman filter, 142, 1163

Hu, M., 2006, 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact, 134, 675

Hu, X.-M., 2010, Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error: A real-data study, 37, L08802

Huang, X.-Y., 1993, Diabetic digital-filtering initialization: Application to the HIRLAM model, 121, 589

Hunt, B. R., 2004, Four-dimensional ensemble Kalman filtering, 56A, 273

Hunt, B. R., 2007, Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, 230, 112

Isaksen, L., 2012, 301

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Jung, Y., 2010a, Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme, 49, 146

Jung, Y., 2010b, Simultaneous estimation of microphysical parameters and the atmospheric state using simulated polarimetric radar data and an ensemble Kalman filter in the presence of an observation operator error, 138, 539

Kalman, R. E., 1960, A new approach to linear filtering and prediction problems, 82, 35

Kalman, R. E., 1961, New results in linear filtering and prediction theory, 83, 95

Kalnay, E., 1987, Forecasting forecast skill, 115, 349

Kalnay, E., 2010, Accelerating the spin-up of ensemble Kalman filtering, 136, 1644

Kang, J.-S., 2011, “Variable localization�? in an ensemble Kalman filter: Application to the carbon cycle data assimilation, 116, D09110

Kang, J.-S., 2012, Estimation of surface carbon fluxes with an advanced data assimilation methodology, 117, D24101

Kelly, D., 2015, Concrete ensemble Kalman filters with rigorous catastrophic filter divergence, 112, 10 589

Kepert, J. D., 2009, Covariance localisation and balance in an ensemble Kalman filter, 135, 1157

Kleist, D. T., 2015a, An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-hybrid results, 143, 433

Kleist, D. T., 2015b, An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants, 143, 452

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Krishnamurti, T. N., 1999, Improved weather and seasonal climate forecasts from multimodel superensemble, 285, 1548

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Kunii, M., 2014, The 1000-member ensemble Kalman filtering with the JMA nonhydrostatic mesoscale model on the K computer, 92, 623

Lang, S. T. K., 2015, On the impact of re-centering initial conditions for ensemble forecasts, 141, 2571

Lange, H., 2014, The impact of data assimilation length scales on analysis and prediction of convective storms, 142, 3781

Lavaysse, C., 2013, Impact of surface parameter uncertainties within the Canadian regional ensemble prediction system, 141, 1506

Lawson, W. G., 2004, Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth, 132, 1966

Lei, L., 2014, Empirical localization of observations for serial ensemble Kalman filter data assimilation in an atmospheric general circulation model, 142, 1835

Lei, L., 2015, Model space localization is not always better than observation space localization for assimilation of satellite radiances, 143, 3948

Lei, L., 2012, A hybrid nudging-ensemble Kalman filter approach to data assimilation in WRF/DART, 138, 2066

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Li, X., 2008, A regional ensemble prediction system based on moist targeted singular vectors and stochastic parameter perturbations, 136, 443

Lien, G.-Y., 2013, Effective assimilation of global precipitation: Simulation experiments, 65A, 19915

Lien, G.-Y., 2016a, Statistical properties of global precipitation in the NCEP GFS model and TMPA observations for data assimilation, 144, 663

Lien, G.-Y., 2016b, Assimilation of TRMM multisatellite precipitation analysis with a low-resolution NCEP global forecast system, 144, 643

Lindskog, M., 2004, Doppler radar wind data assimilation with HIRLAM 3DVAR, 132, 1081

Liu, C., 2008, An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test, 136, 3363

Liu, Z.-Q., 2003, The potential of high-density observations for numerical weather prediction: A study with simulated observations, 129, 3013

Liu, Z.-Q., 2012, Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter, 140, 4017

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Lorenc, A. C., 2003, The potential of the ensemble Kalman filter for NWP—A comparison with 4D-Var, 129, 3183

Lorenc, A. C., 2015, Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP, 143, 212

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Mass, C. F., 2002, Does increasing horizontal resolution produce more skillful forecasts?, 83, 407

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McTaggart-Cowan, R., 2011, The utility of upper-boundary nesting in NWP, 139, 2117

Meng, Z., 2007, Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments, 135, 1403

Meng, Z., 2008a, Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study, 136, 522

Meng, Z., 2008b, Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Comparison with 3DVAR in a month-long experiment, 136, 3671

Meng, Z., 2011, Limited-area ensemble-based data assimilation, 139, 2025

Mitchell, H. L., 2000, An adaptive ensemble Kalman filter, 128, 416

Mitchell, H. L., 2009, Ensemble Kalman filter configurations and their performance with the logistic map, 137, 4325

Mitchell, H. L., 2002, Ensemble size, balance, and model-error representation in an ensemble Kalman filter, 130, 2791

Miyoshi, T., 2011, The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter, 139, 1519

Miyoshi, T., 2013, A multi-scale localization approach to an ensemble Kalman filter, 9, 170

Miyoshi, T., 2010, Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system, 138, 2846

Miyoshi, T., 2013, Estimating and including observation-error correlations in data assimilation, 21, 387

Miyoshi, T., 2014, The 10,240-member ensemble Kalman filtering with an intermediate AGCM, 41, 5264

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Morrison, H., 2015, Concurrent sensitivities of an idealized deep convective storm to parameterization of microphysics, horizontal grid resolution, and environmental static stability, 143, 2082

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Ying, Y., 2015, An adaptive covariance relaxation method for ensemble data assimilation, 141, 2898

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