103 citations as recorded by crossref.

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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
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23. 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
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27. 100 Years of Progress in Forecasting and NWP Applications S. Benjamin et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0020.1
28. 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
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35. 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
36. 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
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38. Development of an Operational Hybrid Data Assimilation System at KIAPS I. Kwon et al. https://doi.org/10.1007/s13143-018-0029-8
39. 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
40. 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
41. 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
42. 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
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44. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. https://doi.org/10.1002/wcc.535
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46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. A parallel implementation of a 4DEnVar ensemble É. Arbogast et al. https://doi.org/10.1002/qj.3061
54. Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. https://doi.org/10.1002/qj.3551
55. 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
56. Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China Y. Du et al. https://doi.org/10.3390/atmos13050838
57. 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
58. 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
59. A study on harmonizing total ozone assimilation with multiple sensors Y. Rochon et al. https://doi.org/10.5194/acp-19-9431-2019
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. Optimized Localization and Hybridization to Filter Ensemble-Based Covariances B. Ménétrier & T. Auligné https://doi.org/10.1175/MWR-D-15-0057.1
67. Improving spatial localization in 4DEnVar G. Desroziers et al. https://doi.org/10.1002/qj.2898
68. 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
69. PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python S. Ahmed et al. https://doi.org/10.3390/fluids5040225
70. 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
71. 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
72. 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
73. Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique W. Zhang et al. https://doi.org/10.1007/s00190-020-01386-4
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75. 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
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77. 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
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79. 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
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81. Integrated Hybrid Data Assimilation for an Ensemble Kalman Filter https://doi.org/10.1175/MWR-D-21-0002.1
82. 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
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102. 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
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