20 citations as recorded by crossref.

1. Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives J. Li et al. https://doi.org/10.1007/s00376-021-1088-9
2. Impacts of Assimilating GOES‐16 ABI Channels 9 and 10 Clear Air and Cloudy Radiance Observations With Additive Inflation and Adaptive Observation Error in GSI‐EnKF for a Case of Rapidly Evolving Severe Supercells A. Johnson et al. https://doi.org/10.1029/2021JD036157
3. Assimilating satellite clear‐sky infrared radiances in the CMA‐MESO model using variational quality control J. He et al. https://doi.org/10.1002/qj.70142
4. Improving forecasts of a record-breaking rainstorm in Guangzhou by assimilating every 10-min AHI radiances with WRF 4DVAR Y. Wu et al. https://doi.org/10.1016/j.atmosres.2020.104912
5. A Bias Correction Scheme with the Symmetric Cloud Proxy Variable and Its Influence on Assimilating All-Sky GOES-16 Brightness Temperatures C. Feng & Z. Pu https://doi.org/10.1175/MWR-D-21-0333.1
6. Improving the prediction of heavy rainfall with rapid-update dual-resolution hybrid En3DVar assimilation of all-sky AHI infrared water vapor radiances Y. Wang et al. https://doi.org/10.1016/j.atmosres.2022.106352
7. Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast B. Shi et al. https://doi.org/10.1029/2023JD039063
8. Generalized Variational Retrieval of Full Field-of-View Cloud Fraction and Precipitable Water Vapor from FY-4A/GIIRS Observations G. Wang et al. https://doi.org/10.3390/rs17223687
9. Assimilation of Low-Peaking Satellite Observations Using the Coupled Interface Framework S. Frolov et al. https://doi.org/10.1175/MWR-D-19-0029.1
10. All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud A. Geer et al. https://doi.org/10.5194/amt-12-4903-2019
11. Surface‐dependent correlated infrared observation errors and quality control in the FV3 framework K. Bathmann & A. Collard https://doi.org/10.1002/qj.3925
12. Assimilating visible satellite images for convective‐scale numerical weather prediction: A case‐study L. Scheck et al. https://doi.org/10.1002/qj.3840
13. The Potential Value of Combining Assimilation of Water Vapor Radiances and Cloud Liquid/Ice Water Path From ABI on Analyses and Forecasts for Hurricane Irma (2017) D. Meng et al. https://doi.org/10.1029/2022JD036910
14. On the impact of observation‐error correlations in data assimilation, with application to along‐track altimeter data O. Goux et al. https://doi.org/10.1002/qj.5026
15. An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures G. Wang et al. https://doi.org/10.3390/rs18050748
16. Estimation of the error covariance matrix for IASI radiances and its impact on the assimilation of ozone in a chemistry transport model M. El Aabaribaoune et al. https://doi.org/10.5194/amt-14-2841-2021
17. Synergistic Effects of Infrared and Microwave Radiance in All-Sky Data Assimilation for a Heavy Precipitation Case Over the Korean Peninsula J. Hwang et al. https://doi.org/10.1109/JSTARS.2025.3642947
18. Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection J. Schröttle et al. https://doi.org/10.1175/MWR-D-20-0002.1
19. All-Sky Microwave Radiances Assimilated with an Ensemble Kalman Filter M. Bonavita et al. https://doi.org/10.1175/MWR-D-19-0413.1
20. Pilot EnKF assimilation of FY-4A AGRI All-sky upper-tropospheric water vapor radiances for a warm-sector heavy rainfall case in South China J. He et al. https://doi.org/10.1016/j.atmosres.2026.108934