12 citations as recorded by crossref.

1. What Can We Expect from Data Assimilation for Air Quality Forecast? Part II: Analysis with a Semi-Real Case B. Bessagnet et al. https://doi.org/10.1175/JTECH-D-18-0117.1
2. A multiphase CMAQ version 5.0 adjoint S. Zhao et al. https://doi.org/10.5194/gmd-13-2925-2020
3. Development of four-dimensional variational assimilation system based on the GRAPES–CUACE adjoint model (GRAPES–CUACE-4D-Var V1.0) and its application in emission inversion C. Wang et al. https://doi.org/10.5194/gmd-14-337-2021
4. Machine Learning-Augmented 3D-Variational Data Assimilation for Wintertime Diurnal Variability of Surface Ozone Over China J. Fan et al. https://doi.org/10.1109/JSTARS.2026.3720234
5. Better prediction of surface ozone by a superensemble method using emission sensitivity runs in Japan M. Kajino et al. https://doi.org/10.1016/j.aeaoa.2021.100120
6. Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. https://doi.org/10.5194/acp-19-13911-2019
7. Confronting the boundary layer data gap: evaluating new and existing methodologies of probing the lower atmosphere T. Bell et al. https://doi.org/10.5194/amt-13-3855-2020
8. Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5 S. Park et al. https://doi.org/10.5194/gmd-15-2773-2022
9. Hybrid IFDMB/4D-Var inverse modeling to constrain the spatiotemporal distribution of CO and NO2 emissions using the CMAQ adjoint model J. Moon et al. https://doi.org/10.1016/j.atmosenv.2024.120490
10. A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations—a case study of Xuzhou, China L. Lu et al. https://doi.org/10.1007/s11356-019-04246-7
11. An evaluation of digital filtering and 4DVar data assimilation in the WRF model towards the simulation of tropical cyclones G. Tiwari et al. https://doi.org/10.1016/j.asr.2024.02.004
12. Multisource Observation-Constrained Tuning for Air Quality Forecasting in a Machine Learning-Enhanced NOAA Unified Forecast System J. Xing et al. https://doi.org/10.1021/acs.est.6c04600