53 citations as recorded by crossref.

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2. Discrepancy in assimilated atmospheric CO over East Asia in 2015–2020 by assimilating satellite and surface CO measurements Z. Tang et al. 10.5194/acp-22-7815-2022
3. A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ C. Huang et al. 10.3390/rs15061711
4. Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation K. Hu et al. 10.3390/rs16183394
5. Stock Price Forecasting of IBEX35 Companies in the Petroleum, Electricity, and Gas Industries I. Todorov & F. Sánchez Lasheras 10.3390/en16093856
6. Dynamics-based estimates of decline trend with fine temporal variations in China's PM2.5 emissions Z. Peng et al. 10.5194/acp-23-14505-2023
7. Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. 10.1029/2019JD030421
8. Improving ozone simulations in Asia via multisource data assimilation: results from an observing system simulation experiment with GEMS geostationary satellite observations L. Shu et al. 10.5194/acp-23-3731-2023
9. Development and application of a hybrid long-short term memory – three dimensional variational technique for the improvement of PM2.5 forecasting X. Lu et al. 10.1016/j.scitotenv.2020.144221
10. Assessment of the Meteorological Impact on Improved PM2.5 Air Quality Over North China During 2016–2019 Based on a Regional Joint Atmospheric Composition Reanalysis Data‐Set X. Kou et al. 10.1029/2020JD034382
11. Improving PM2.5 predictions during COVID-19 lockdown by assimilating multi-source observations and adjusting emissions L. Chen et al. 10.1016/j.envpol.2021.118783
12. Human mortality attributable to outdoor air pollution in China during the period 2016–2020 G. Liu et al. 10.1088/2752-5309/acd3a0
13. Development of the CMA-ChemRA: China Regional Weakly Coupled Chemical-Weather Reanalysis System with product since 2007 T. Zhang et al. 10.1016/j.scitotenv.2024.177552
14. Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI) L. Kong et al. 10.5194/essd-16-4351-2024
15. Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
16. Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods U. Dash et al. 10.1016/j.envpol.2023.121099
17. Ozone variability and the impacts of associated synoptic patterns over China during summer 2016–2020 based on a regional atmospheric composition reanalysis dataset X. Kou et al. 10.1016/j.atmosenv.2024.120919
18. The optimization of SO2 emissions by the 4DVAR and EnKF methods and its application in WRF-Chem Y. Hu et al. 10.1016/j.scitotenv.2023.163796
19. Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States X. Chen et al. 10.5194/gmd-14-3969-2021
20. Aerosol optical depth assimilation for a modal aerosol model: Implementation and application in AOD forecasts over East Asia J. Pang et al. 10.1016/j.scitotenv.2020.137430
21. 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. 10.5194/acp-20-9311-2020
22. Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system Y. Kong et al. 10.1016/j.atmosres.2020.105366
23. Development of an integrated machine-learning and data assimilation framework for NOx emission inversion Y. Chen et al. 10.1016/j.scitotenv.2023.161951
24. Evaluation of air quality forecasting system FORAIR-IT over Europe and Italy at high resolution for year 2017 M. Adani et al. 10.1016/j.apr.2022.101456
25. Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China Y. Hu et al. 10.5194/acp-22-13183-2022
26. Assimilating Himawari-8 AHI aerosol observations with a rapid-update data assimilation system X. Xia et al. 10.1016/j.atmosenv.2019.116866
27. Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation L. Kong et al. 10.5194/acp-23-6217-2023
28. Improving PM2.5 and PM10 predictions in China from WRF_Chem through a deep learning method: Multiscale depth-separable UNet X. Ma et al. 10.1016/j.envpol.2024.125344
29. Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM2.5 and PM10 Forecasts in the Sichuan Basin Z. Zhang et al. 10.1029/2020EA001614
30. Improved ozone simulation in East Asia via assimilating observations from the first geostationary air-quality monitoring satellite: Insights from an Observing System Simulation Experiment L. Shu et al. 10.1016/j.atmosenv.2022.119003
31. A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. 10.5194/gmd-16-5949-2023
32. Simultaneous assimilation of Fengyun-4A and Himawari-8 aerosol optical depth retrieval to improve air quality simulations during one storm event over East Asia X. Xia et al. 10.3389/feart.2023.1057299
33. Assimilating a blended dataset of satellite-based estimations and in situ observations to improve WRF-Chem PM2.5 prediction X. Ma et al. 10.1016/j.atmosenv.2023.120284
34. The impacts of background error covariance on particulate matter assimilation and forecast: An ideal case study with a modal aerosol model over China J. Pang & X. Wang 10.1016/j.scitotenv.2021.147417
35. Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model H. Ye et al. 10.1016/j.atmosres.2021.105877
36. A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China W. Han et al. 10.5194/gmd-15-4225-2022
37. An Observing System Simulation Experiment Analysis of How Well Geostationary Satellite Trace‐Gas Observations Constrain NOx Emissions in the US C. Hsu et al. 10.1029/2023JD039323
38. 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. 10.5194/acp-21-4403-2021
39. Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
40. Improvement of PM2.5 Forecast in China by Ground-Based Multi-Pollutant Emission Source Inversion in 2022 L. Zhu et al. 10.3390/atmos15020181
41. Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM<sub>2.5</sub> S. Park et al. 10.5194/gmd-15-2773-2022
42. The carbon sink in China as seen from GOSAT with a regional inversion system based on the Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS) X. Kou et al. 10.5194/acp-23-6719-2023
43. Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme S. Ha 10.5194/gmd-15-1769-2022
44. Improvement of PM2.5 and O3 forecasting by integration of 3D numerical simulation with deep learning techniques H. Sun et al. 10.1016/j.scs.2021.103372
45. Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. 10.1021/acs.est.0c01680
46. The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system D. Chen et al. 10.5194/acp-19-8619-2019
47. Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations Z. Peng et al. 10.1029/2020GL089030
48. Anthropogenic emissions estimated using surface observations and their impacts on PM2.5 source apportionment over the Yangtze River Delta, China S. Feng et al. 10.1016/j.scitotenv.2022.154522
49. Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem T. Dai et al. 10.5194/acp-21-4357-2021
50. CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations Z. Peng et al. 10.1029/2022JD037154
51. Aerosol data assimilation using data from Fengyun-4A, a next-generation geostationary meteorological satellite X. Xia et al. 10.1016/j.atmosenv.2020.117695
52. China’s Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2 S. Feng et al. 10.1021/acs.est.3c07756
53. Impact of meteorological conditions and reductions in anthropogenic emissions on PM2.5 concentrations in China from 2016 to 2020 Z. Xu et al. 10.1016/j.atmosenv.2023.120265