104 citations as recorded by crossref.

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2. Impact of non‐stationarity on hybrid ensemble filters: A study with a doubly stochastic advection‐diffusion‐decay model M. Tsyrulnikov & A. Rakitko https://doi.org/10.1002/qj.3556
3. Hybrid Background Error Covariances for a Limited-Area Deterministic Weather Prediction System J. Bédard et al. https://doi.org/10.1175/WAF-D-19-0069.1
4. Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction S. Ahmed et al. https://doi.org/10.1103/PhysRevE.102.043302
5. Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1) S. Skachko et al. https://doi.org/10.5194/gmd-12-5097-2019
6. Scale-Dependent Background Error Covariance Localization: Evaluation in a Global Deterministic Weather Forecasting System J. Caron & M. Buehner https://doi.org/10.1175/MWR-D-17-0369.1
7. A review of operational methods of variational and ensemble‐variational data assimilation R. Bannister https://doi.org/10.1002/qj.2982
8. Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments R. Kong et al. https://doi.org/10.1175/MWR-D-17-0164.1
9. Using Icepack to reproduce ice mass balance buoy observations in landfast ice: improvements from the mushy-layer thermodynamics M. Plante et al. https://doi.org/10.5194/tc-18-1685-2024
10. Estimation of Systematic Errors in the GFS Using Analysis Increments K. Bhargava et al. https://doi.org/10.1002/2017JD027423
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12. Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment J. Bédard et al. https://doi.org/10.1175/MWR-D-16-0310.1
13. Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part II: The Regional System J. Caron et al. https://doi.org/10.1175/MWR-D-14-00353.1
14. The Impact of Incremental Analysis Update on Regional Simulations for Typhoons Y. Ge et al. https://doi.org/10.1029/2022MS003084
15. Assimilation of radar radial velocity data with the WRF hybrid 4DEnVar system for the prediction of hurricane Ike (2008) F. Shen et al. https://doi.org/10.1016/j.atmosres.2019.104771
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17. Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres N. Gustafsson et al. https://doi.org/10.1002/qj.3179
18. The benefits of a hybrid‐3DEnVar data assimilation scheme for the convection‐permitting NWP model AROME–Austria K. Jyoti et al. https://doi.org/10.1002/qj.70207
19. Developing a coastal hazard prediction system in ice-infested waters – Part 1: High-resolution regional wave modeling in the Estuary and Gulf of St. Lawrence J. Baudry et al. https://doi.org/10.5194/nhess-26-4231-2026
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21. A comparison of variational, ensemble-based, and hybrid data assimilation methods over East Asia for two one-month periods E. Yang & H. Kim https://doi.org/10.1016/j.atmosres.2020.105257
22. Tensor Formulation of Ensemble-Based Background Error Covariance Matrix Factorization T. Ishibashi https://doi.org/10.1175/MWR-D-15-0014.1
23. An Ensemble Kalman Filter for Numerical Weather Prediction Based on Variational Data Assimilation: VarEnKF M. Buehner et al. https://doi.org/10.1175/MWR-D-16-0106.1
24. Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere S. Shin et al. https://doi.org/10.1007/s13143-018-0022-2
25. Block Krylov methods for accelerating ensembles of variational data assimilations F. Mercier et al. https://doi.org/10.1002/qj.3329
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27. A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation M. Buehner et al. https://doi.org/10.1175/MWR-D-17-0252.1
28. 100 Years of Progress in Forecasting and NWP Applications S. Benjamin et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0020.1
29. Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part I: The Global System M. Buehner et al. https://doi.org/10.1175/MWR-D-14-00354.1
30. Precision and convergence speed of the ensemble Kalman filter-based parameter estimation: setting parameter uncertainty for reliable and efficient estimation K. Sueki et al. https://doi.org/10.1186/s40645-022-00504-4
31. The Local Ensemble Tangent Linear Model: an enabler for coupled model 4D‐Var C. Bishop et al. https://doi.org/10.1002/qj.2986
32. Dependence on initial conditions versus model formulations for medium‐range forecast error variations L. Magnusson et al. https://doi.org/10.1002/qj.3545
33. Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat J. Du et al. https://doi.org/10.1109/JSTARS.2021.3092340
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35. Evaluating the Forecast Impact of Assimilating ATOVS Radiance With the Regional System of Multigrid NLS‐4DVar Data Assimilation for Numerical Weather Prediction (SNAP) H. Zhang & X. Tian https://doi.org/10.1029/2020MS002407
36. Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada C. Whaley et al. https://doi.org/10.5194/acp-18-2011-2018
37. Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China D. Xu et al. https://doi.org/10.1007/s00376-020-0219-z
38. A geo‐statistical observation operator for the assimilation of near‐surface wind data J. Bédard et al. https://doi.org/10.1002/qj.2569
39. Development of an Operational Hybrid Data Assimilation System at KIAPS I. Kwon et al. https://doi.org/10.1007/s13143-018-0029-8
40. The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions H. Song et al. https://doi.org/10.1002/2017JD027598
41. Ensemble analysis and forecast of ecosystem indicators in the North Atlantic using ocean colour observations and prior statistics from a stochastic NEMO–PISCES simulator M. Popov et al. https://doi.org/10.5194/os-20-155-2024
42. The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1) M. Buehner et al. https://doi.org/10.5194/gmd-18-1-2025
43. Development of an Early Warning and Incident Response System for the Protection of Visitors from Natural Hazards in Important Outdoor Sites in Greece C. Psaroudakis et al. https://doi.org/10.3390/su13095143
44. An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part I: System Description and 3D-Hybrid Results D. Kleist & K. Ide https://doi.org/10.1175/MWR-D-13-00351.1
45. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. https://doi.org/10.1002/wcc.535
46. A 3D ensemble variational data assimilation scheme for the limited‐area AROME model: Formulation and preliminary results T. Montmerle et al. https://doi.org/10.1002/qj.3334
47. Relationships among Four-Dimensional Hybrid Ensemble–Variational Data Assimilation Algorithms with Full and Approximate Ensemble Covariance Localization C. Liu & M. Xue https://doi.org/10.1175/MWR-D-15-0203.1
48. On the Use of Cost-Effective Valid-Time-Shifting (VTS) Method to Increase Ensemble Size in the GFS Hybrid 4DEnVar System B. Huang & X. Wang https://doi.org/10.1175/MWR-D-18-0009.1
49. Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts S. Zhang & Z. Pu https://doi.org/10.3390/atmos11091007
50. On process-oriented conditional targeted covariance inflation (TCI) for 3D-volume radar data assimilation K. Vobig et al. https://doi.org/10.5194/npg-32-471-2025
51. A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary Tests S. Zhu et al. https://doi.org/10.1029/2021MS002737
52. Estimation of the Added Value of the Absolute Calibration of GPS Radio Occultation Data for Numerical Weather Prediction J. Aparicio & S. Laroche https://doi.org/10.1175/MWR-D-14-00153.1
53. Comparison between Four-Dimensional LETKF and Ensemble-Based Variational Data Assimilation with Observation Localization S. Yokota et al. https://doi.org/10.2151/sola.2016-019
54. A parallel implementation of a 4DEnVar ensemble É. Arbogast et al. https://doi.org/10.1002/qj.3061
55. Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. https://doi.org/10.1002/qj.3551
56. Unraveling the Mechanism of Extreme (More than 30 Sigma) Precipitation during August 2018 and 2019 over Kerala, India P. Mukhopadhyay et al. https://doi.org/10.1175/WAF-D-20-0162.1
57. Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China Y. Du et al. https://doi.org/10.3390/atmos13050838
58. Formulation and use of 3D‐hybrid and 4D‐hybrid ensemble covariances in the Météo‐France global data assimilation system L. Berre & E. Arbogast https://doi.org/10.1002/qj.4603
59. Handling non-linearity in radar data assimilation using the non-linear least squares enhanced POD-4DVar B. Zhang et al. https://doi.org/10.1007/s11430-015-0271-4
60. A study on harmonizing total ozone assimilation with multiple sensors Y. Rochon et al. https://doi.org/10.5194/acp-19-9431-2019
61. Assimilation of Infrared Radiance Observations with Sensitivity to Land Surfaces in the Canadian Ensemble–Variational System S. Dutta et al. https://doi.org/10.1175/JAMC-D-15-0208.1
62. West African operational daily solar forecast errors and their link with meteorological conditions L. Clauzel et al. https://doi.org/10.1016/j.renene.2024.120101
63. Development of Methods and Technologies for Operational Assimilation of Meteorological Observations at the Hydrometcenter of Russia M. Tsyrulnikov et al. https://doi.org/10.3103/S1068373924070082
64. Comparison of hybrid gain data assimilation update scenarios in an operational global forecast system C. Chang et al. https://doi.org/10.1007/s44394-026-00022-7
65. Unraveling the socio-environmental drivers during the early COVID-19 pandemic in China A. Alaniz et al. https://doi.org/10.1007/s11356-023-27969-0
66. Was the extreme rainfall that caused the August 2022 flood in Pakistan predictable? I. Malik et al. https://doi.org/10.1088/2752-5295/acfa1a
67. Optimized Localization and Hybridization to Filter Ensemble-Based Covariances B. Ménétrier & T. Auligné https://doi.org/10.1175/MWR-D-15-0057.1
68. Improving spatial localization in 4DEnVar G. Desroziers et al. https://doi.org/10.1002/qj.2898
69. Hybrid 4DVAR with a Local Ensemble Tangent Linear Model: Application to the Shallow-Water Model D. Allen et al. https://doi.org/10.1175/MWR-D-16-0184.1
70. PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python S. Ahmed et al. https://doi.org/10.3390/fluids5040225
71. The Near-Real-Time SCALE-LETKF System: A Case of the September 2015 Kanto-Tohoku Heavy Rainfall G. Lien et al. https://doi.org/10.2151/sola.2017-001
72. To what extent is your data assimilation scheme designed to find the posterior mean, the posterior mode or something else? D. Hodyss et al. https://doi.org/10.3402/tellusa.v68.30625
73. A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean C. Aldebert et al. https://doi.org/10.1029/2020MS002245
74. Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique W. Zhang et al. https://doi.org/10.1007/s00190-020-01386-4
75. Regional Ensemble–Variational Data Assimilation Using Global Ensemble Forecasts W. Wu et al. https://doi.org/10.1175/WAF-D-16-0045.1
76. An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble‐Based Assimilation Methods H. Zhang & X. Tian https://doi.org/10.1002/2017JD027999
77. An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part II: 4DEnVar and Hybrid Variants D. Kleist & K. Ide https://doi.org/10.1175/MWR-D-13-00350.1
78. Impact of Ensemble Data Assimilation Perturbation and Singular Vector Perturbation on Ensemble Forecasts Across Spatial Scales Z. Huo et al. https://doi.org/10.1007/s13351-026-5173-y
79. Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta) B. Jung et al. https://doi.org/10.5194/gmd-17-3879-2024
80. Comparison of Hybrid-4DEnVar and Hybrid-4DVar Data Assimilation Methods for Global NWP A. Lorenc et al. https://doi.org/10.1175/MWR-D-14-00195.1
81. Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model M. Tsyrulnikov & A. Sotskiy https://doi.org/10.1016/j.spasta.2024.100870
82. Integrated Hybrid Data Assimilation for an Ensemble Kalman Filter https://doi.org/10.1175/MWR-D-21-0002.1
83. A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation J. Tödter & B. Ahrens https://doi.org/10.1175/MWR-D-14-00108.1
84. International challenge to model the long-range transport of radioxenon released from medical isotope production to six Comprehensive Nuclear-Test-Ban Treaty monitoring stations C. Maurer et al. https://doi.org/10.1016/j.jenvrad.2018.01.030
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86. High-Resolution NWP Forecast Precipitation Comparison over Complex Terrain of the Sierras de Córdoba during RELAMPAGO-CACTI G. Casaretto et al. https://doi.org/10.1175/WAF-D-21-0006.1
87. Particle-filter-enhanced reinforcement learning under noisy measurements W. Song et al. https://doi.org/10.3934/acse.2026002
88. Sensitivities of the NCEP Global Forecast System J. Wang et al. https://doi.org/10.1175/MWR-D-18-0239.1
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90. Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill G. Smith et al. https://doi.org/10.1175/MWR-D-17-0157.1
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92. An ensemble-variational inversion system for the estimation of ammonia emissions using CrIS satellite ammonia retrievals M. Sitwell et al. https://doi.org/10.5194/acp-22-6595-2022
93. Data Assimilation of Compressible Flows With Discontinuities: Evaluating Algorithms on Sod's Shock Tube A. Edoh et al. https://doi.org/10.1002/fld.70054
94. Impacts of satellite data assimilation with different model vertical levels on QPFs downstream of the Tibetan Plateau Z. Qin & X. Zou https://doi.org/10.1007/s00703-020-00765-y
95. On Oceanic Initial State Errors in the Ensemble Data Assimilation for a Coupled General Circulation Model Y. Chen et al. https://doi.org/10.1029/2022MS003106
96. Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study Y. Zhang et al. https://doi.org/10.3390/rs14010065
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98. Grand ensemble forecasts verification based on two high resolution (∼12 km) global ensemble prediction systems A. Mamgain et al. https://doi.org/10.1016/j.atmosres.2024.107585
99. A hybrid approach to generating search subspaces in dynamically constrained 4-dimensional data assimilation M. Yaremchuk et al. https://doi.org/10.1016/j.ocemod.2017.08.003
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102. JMA Operational Hourly Hybrid 3DVar with Singular Vector-Based Mesoscale Ensemble Prediction System S. YOKOTA et al. https://doi.org/10.2151/jmsj.2024-006
103. Improvement of the Snow Depth in the Common Land Model by Coupling a Two-Dimensional Deterministic Ensemble Model with a Variational Hybrid Snow Cover Fraction Data Assimilation Scheme and a New Observation Operator J. Xu et al. https://doi.org/10.1175/JHM-D-16-0149.1
104. Estimation of regional-scale near real time reference evapotranspiration using remote sensing and weather data to improve agriculture advisory A. Soni et al. https://doi.org/10.1007/s12145-023-01197-z