23 citations as recorded by crossref.

1. 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
2. A review of operational methods of variational and ensemble‐variational data assimilation R. Bannister https://doi.org/10.1002/qj.2982
3. Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System M. Lindskog et al. https://doi.org/10.1007/s00376-021-0326-5
4. 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
5. All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event D. Xu et al. https://doi.org/10.1016/j.atmosres.2023.106898
6. A comparison between EDA-EnVar and ETKF-EnVar data assimilation techniques using radar observations at convective scales through a case study of Hurricane Ike (2008) F. Shen et al. https://doi.org/10.1007/s00703-017-0544-7
7. Methods for Observation Data Assimilation in Problems of Physics of Atmosphere and Ocean V. Shutyaev https://doi.org/10.1134/S0001433819010080
8. 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
9. 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
10. The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review Z. Sokol et al. https://doi.org/10.3390/rs13030351
11. Applying Time-Expended Sampling to Ensemble Assimilation of Remote-Sensing Data for Short-Term Predictions of Thunderstorms H. Zhang et al. https://doi.org/10.3390/rs15092358
12. 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
13. An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model L. Huang et al. https://doi.org/10.3390/app15126399
14. Numerical Simulation of Ocean Circulation with Ultrahigh Spatial Resolution G. Korotaev & V. Shutyaev https://doi.org/10.1134/S000143382003010X
15. Optimized Localization and Hybridization to Filter Ensemble-Based Covariances B. Ménétrier & T. Auligné https://doi.org/10.1175/MWR-D-15-0057.1
16. Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting S. Gao et al. https://doi.org/10.1007/s00376-020-0081-z
17. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. https://doi.org/10.1002/wcc.535
18. Improving Background Error Covariances in a 3D Ensemble–Variational Data Assimilation System for Regional NWP J. Caron et al. https://doi.org/10.1175/MWR-D-18-0248.1
19. Towards a Data-Derived Observation Error Covariance Matrix for Satellite Measurements Y. Liu et al. https://doi.org/10.3390/rs11151770
20. Efficient Regional Hybrid Ensemble-Variational Data Assimilation using the Global-Ensemble-Model-Augmented Error Covariance for Numerical Weather Prediction over Eastern China Y. Wang et al. https://doi.org/10.3390/atmos11040365
21. Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 19892013 L. Axell & Y. Liu https://doi.org/10.3402/tellusa.v68.24220
22. Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM) N. Gustafsson & J. Bojarova https://doi.org/10.5194/npg-21-745-2014
23. Regional Ensemble–Variational Data Assimilation Using Global Ensemble Forecasts W. Wu et al. https://doi.org/10.1175/WAF-D-16-0045.1