67 citations as recorded by crossref.

1. Comparison of Two Kinds of Momentum Control Variables in 3DVAR During Assimilating Low-resolution Observations in a Convective-scale Model: a Case Study of Torrential Rainfall in North China Q. Dong et al. https://doi.org/10.1007/s13143-022-00290-5
2. Assimilating chemical observations in the Mexico City Metropolitan Area using WRFDA-Chem G. Maldonado Pacheco et al. https://doi.org/10.22201/igeof.2954436xe.2026.65.3.1886
3. Impact of all‐sky water vapour channel radiance from INSAT‐3D/3DR satellite over South Asia region using WRF model P. Kumar et al. https://doi.org/10.1002/qj.4323
4. Impact of Feature-Dependent Static Background Error Covariances for Satellite-Derived Humidity Assimilation on Analyses and Forecasts of Multiple Sea Fog Cases over the Yellow Sea Y. Yang et al. https://doi.org/10.3390/rs14184537
5. Inferring and evaluating satellite-based constraints on NOx emissions estimates in air quality simulations J. East et al. https://doi.org/10.5194/acp-22-15981-2022
6. Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting S. Cheng et al. https://doi.org/10.1016/j.jcp.2022.111302
7. Impact of Skin Temperature Control Variable on the Assimilation of Microwave Temperature-sounding Channels in Regional Numerical Weather Prediction Y. Chen et al. https://doi.org/10.1007/s00376-024-4070-5
8. Review of methods for online power-distribution reconstruction H. Yang et al. https://doi.org/10.1016/j.anucene.2025.111531
9. Radar Reflectivity Assimilation Based on Hydrometeor Control Variables and Its Impact on Short-Term Precipitation Forecasting H. Zheng et al. https://doi.org/10.3390/rs15030672
10. Direct Assimilation of Radar Reflectivity without Tangent Linear and Adjoint of the Nonlinear Observation Operator in the GSI-Based EnVar System: Methodology and Experiment with the 8 May 2003 Oklahoma City Tornadic Supercell Y. Wang & X. Wang https://doi.org/10.1175/MWR-D-16-0231.1
11. Evaluation of the 3DVAR Operational Implementation of the Colombian Air Force for Aircraft Operations: A Case Study J. Hinestroza-Ramirez et al. https://doi.org/10.3390/cli11070153
12. A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results Y. Pan et al. https://doi.org/10.1007/s00376-017-7108-0
13. Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0) S. Ha et al. https://doi.org/10.5194/gmd-17-4199-2024
14. Role of background error statistics in the prediction of severe cyclonic storm over the Bay of Bengal using the WRF-3DVAR data assimilation system A. Thankachan & K. Singh https://doi.org/10.1016/j.asr.2025.07.095
15. Three‐dimensional spatial interpolation of 2 m temperature over Norway C. Lussana et al. https://doi.org/10.1002/qj.3208
16. Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin X. Pan et al. https://doi.org/10.3390/rs9090963
17. Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System T. Campos et al. https://doi.org/10.3390/atmos9040123
18. Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: aiming to improve air quality forecasting and diagnose model deficiencies W. Sun et al. https://doi.org/10.5194/acp-20-9311-2020
19. 3DVar data assimilation for inversion optimization of main steam system parameters in nuclear power plants using forecast-gradient and residual FCN X. Luo et al. https://doi.org/10.1016/j.nucengdes.2025.114170
20. A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data S. Wang & Z. Liu https://doi.org/10.5194/gmd-12-4031-2019
21. Use of the WRF-DA 3D-Var Data Assimilation System to Obtain Wind Speed Estimates in Regular Grids from Measurements at Wind Farms in Uruguay G. Cazes Boezio & S. Ortelli https://doi.org/10.3390/data4040142
22. Assimilation of Doppler Weather Radar data with a regional WRF-3DVAR system: Impact of control variables on forecasts of a heavy rainfall case P. Thiruvengadam et al. https://doi.org/10.1016/j.advwatres.2019.02.004
23. 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
24. Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia S. Lee et al. https://doi.org/10.1016/j.atmosenv.2021.118921
25. Towards a Better Design of Convection-Allowing Ensembles for Precipitation Forecasts over Ensenada, Baja California, Mexico Y. Mayor et al. https://doi.org/10.3390/atmos11090973
26. Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China W. Chang et al. https://doi.org/10.5194/acp-21-4403-2021
27. Improved Streamflow Forecast in a Small-Medium Sized River Basin with Coupled WRF and WRF-Hydro: Effects of Radar Data Assimilation T. Gu et al. https://doi.org/10.3390/rs13163251
28. A Comparison between 3DVAR and EnKF for Data Assimilation Effects on the Yellow Sea Fog Forecast X. Gao et al. https://doi.org/10.3390/atmos9090346
29. Comparison of different momentum control variables on assimilating radar observations for the forecasts of a dispersive convective event W. Song et al. https://doi.org/10.3389/feart.2023.1148921
30. Dynamically weighted hybrid gain data assimilation: perfect model testing H. De Azevedo et al. https://doi.org/10.1080/16000870.2020.1835310
31. The Weather On-Demand Framework Ó. Rögnvaldsson et al. https://doi.org/10.3390/atmos16010091
32. UFS-RAQMS global atmospheric composition model: TROPOMI CO column assimilation M. Bruckner et al. https://doi.org/10.5194/gmd-18-8109-2025
33. Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry X. Xu & A. Stoffelen https://doi.org/10.5194/amt-14-7435-2021
34. Data assimilation for the burnup distribution applying the three-dimensional variational and artificial neutral network algorithm L. Guo et al. https://doi.org/10.1016/j.anucene.2022.109419
35. A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods Y. Tang et al. https://doi.org/10.5194/gmd-10-4743-2017
36. An inverse-distance-based fitting term for 3D-Var data assimilation in nuclear core simulation H. Gong et al. https://doi.org/10.1016/j.anucene.2020.107346
37. Assimilation of FY-3D and FY-3E Hyperspectral Infrared Atmospheric Sounding Observation and Its Impact on Numerical Weather Prediction during Spring Season over the Continental United States Q. Zhang & M. Shao https://doi.org/10.3390/atmos14060967
38. All-sky radiance assimilation of INSAT-3DS imager water vapour channel in the weather research and forecasting model P. Kumar et al. https://doi.org/10.1016/j.atmosres.2025.108674
39. Effect of Adding Hydrometeor Mixing Ratios Control Variables on Assimilating Radar Observations for the Analysis and Forecast of a Typhoon D. Xu et al. https://doi.org/10.3390/atmos10070415
40. Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. https://doi.org/10.3390/atmos13020160
41. Direct assimilation of ground-based microwave radiometer observations with machine learning bias correction based on developments of RTTOV-gb v1.0 and WRFDA v4.5 Q. Zheng et al. https://doi.org/10.5194/gmd-19-731-2026
42. Constraining NOx emissions with satellite NO2 data to improve modeling of 2023 wildfire air quality J. Kumm & Z. Qu https://doi.org/10.1016/j.atmosenv.2026.121985
43. The Regularized WSM6 Microphysical Scheme and Its Validation in WRF 4D-Var S. Yang et al. https://doi.org/10.1007/s00376-022-2058-6
44. Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction Q. Zhang & M. Shao https://doi.org/10.3390/atmos14030580
45. Direct Assimilation of Dual-polarization Radar Using the Hydrometeor Background Error Covariance in the CMA-MESO Model and Its Sensitivity to the Microphysics Scheme J. Chen et al. https://doi.org/10.1007/s00376-025-4294-z
46. 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
47. Impact of model resolution and its representativeness consistency with observations on operational prediction of PM2.5 with 3D-VAR data assimilation Y. Wei et al. https://doi.org/10.1016/j.apr.2024.102141
48. A Review of Data Assimilation on Aerosol Optical, Radiative, and Climatic Effects Study Y. Cheng et al. https://doi.org/10.1007/s41810-022-00142-9
49. Effects of chemical and meteorological data assimilation on air-quality and meteorological forecasts in the Korean Peninsula Y. Cho et al. https://doi.org/10.1016/j.scitotenv.2025.179842
50. Multi‐GNSS Airborne Radio Occultation Observations as a Complement to Dropsondes in Atmospheric River Reconnaissance J. Haase et al. https://doi.org/10.1029/2021JD034865
51. The Potential of Monitoring Carbon Dioxide Emission in a Geostationary View with the GIIRS Meteorological Hyperspectral Infrared Sounder Q. Zhang et al. https://doi.org/10.3390/rs15040886
52. Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks S. Cheng & M. Qiu https://doi.org/10.1007/s00521-021-06739-4
53. Impacts of adding hydrometeor control variables on the radar reflectivity data assimilation for the 6–8 August 2018 mesoscale convective system case F. Shen et al. https://doi.org/10.1016/j.atmosres.2023.107012
54. A pragmatic strategy for implementing spatially correlated observation errors in an operational system: An application to Doppler radial winds D. Simonin et al. https://doi.org/10.1002/qj.3592
55. Surface temperature assimilation improving geostationary meteorological satellite surface-sensitive brightness temperature simulations over land X. Li et al. https://doi.org/10.1016/j.atmosres.2024.107706
56. Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast M. Hung et al. https://doi.org/10.3390/atmos14081201
57. Physically consistent global atmospheric data assimilation with machine learning in latent space H. Fan et al. https://doi.org/10.1126/sciadv.aea4248
58. Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction D. Meng et al. https://doi.org/10.1175/MWR-D-20-0419.1
59. Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme S. Ha https://doi.org/10.5194/gmd-15-1769-2022
60. Research on Data Assimilation Methods Based on the Lorenz-96 Model 大. 张 https://doi.org/10.12677/nst.2024.123014
61. Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem T. Dai et al. https://doi.org/10.5194/acp-21-4357-2021
62. Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008) F. Shen et al. https://doi.org/10.3390/atmos12070853
63. WRF data assimilation of weather stations and lightning data for a convective event in northern Italy E. Maggioni et al. https://doi.org/10.1007/s42865-023-00061-8
64. Reactor power distribution detection and estimation via a stabilized gappy proper orthogonal decomposition method H. Gong et al. https://doi.org/10.1016/j.nucengdes.2020.110833
65. Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. https://doi.org/10.1029/2018MS001475
66. Improving Clear-Sky Solar Power Prediction over China by Assimilating Himawari-8 Aerosol Optical Depth with WRF-Chem-Solar S. Wang et al. https://doi.org/10.3390/rs14194990
67. An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties A. Bell et al. https://doi.org/10.5194/amt-15-5415-2022