175 citations as recorded by crossref.

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2. Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 1. Bias Correction With Surface Observations in Nonrural Areas H. Zhang et al. 10.1029/2019JD032293
3. Development of the WRF-CO2 4D-Var assimilation system v1.0 T. Zheng et al. 10.5194/gmd-11-1725-2018
4. Urban-scale variational flux inversion for CO Using TROPOMI total-column retrievals: A case study of Tehran N. Shahrokhi et al. 10.1016/j.atmosenv.2023.120009
5. Multivariate Kalman filtering for spatio-temporal processes G. Ferreira et al. 10.1007/s00477-022-02266-3
6. Impact of synthetic space-borne NO<sub>2</sub> observations from the Sentinel-4 and Sentinel-5P missions on tropospheric NO<sub>2</sub> analyses R. Timmermans et al. 10.5194/acp-19-12811-2019
7. Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2 S. Silva & A. Arellano 10.3390/rs9070744
8. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
9. The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
10. Origin of Fine Particulate Carbon in the Rural United States B. Schichtel et al. 10.1021/acs.est.7b00645
11. 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
12. Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
13. 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. 10.1016/j.apr.2024.102141
14. Evaluation of ACCMIP ozone simulations and ozonesonde sampling biases using a satellite-based multi-constituent chemical reanalysis K. Miyazaki & K. Bowman 10.5194/acp-17-8285-2017
15. Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics R. Ménard & M. Deshaies-Jacques 10.3390/atmos9030086
16. 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
17. Evaluation of Two Low-Cost Optical Particle Counters for the Measurement of Ambient Aerosol Scattering Coefficient and Ångström Exponent K. Markowicz & M. Chiliński 10.3390/s20092617
18. Application of satellite observations in conjunction with aerosol reanalysis to characterize long-range transport of African and Asian dust on air quality in the contiguous U.S. S. Chen et al. 10.1016/j.atmosenv.2018.05.038
19. Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption G. Fu et al. 10.5194/acp-17-1187-2017
20. Variational approach to the study of processes of geophysical hydro-thermodynamics with assimilation of observation data V. Penenko et al. 10.1134/S0021894417050029
21. Dust effects on mixed-phase clouds and precipitation during a super dust storm over northern China R. Luo et al. 10.1016/j.atmosenv.2023.120081
22. A comprehensive study on ozone pollution in a megacity in North China Plain during summertime: Observations, source attributions and ozone sensitivity J. Sun et al. 10.1016/j.envint.2020.106279
23. Scattering and absorbing aerosols in the climate system J. Li et al. 10.1038/s43017-022-00296-7
24. Responses of the Optical Properties and Distribution of Aerosols to the Summer Monsoon in the Main Climate Zones of China B. Bai et al. 10.3390/atmos12040482
25. The use of forecast gradients in 3DVar data assimilation Z. Zhu et al. 10.1016/j.apm.2019.04.038
26. Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
27. Optimizing the Numerical Simulation of the Dust Event of March 2021: Integrating Aerosol Observations through Multi-Scale 3D Variational Assimilation in the WRF-Chem Model S. Mei et al. 10.3390/rs16111852
28. Inverse problems for the study of climatic and ecological processes under anthropogenic influences V. Penenko & E. Tsvetova 10.1088/1755-1315/386/1/012036
29. Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China J. Pang et al. 10.1016/j.atmosenv.2018.02.011
30. Inverse Modeling of Formaldehyde Emissions and Assessment of Associated Cumulative Ambient Air Exposures at Fine Scale E. Olaguer 10.3390/atmos14060931
31. Evaluating simplified chemical mechanisms within present-day simulations of the Community Earth System Model version 1.2 with CAM4 (CESM1.2 CAM-chem): MOZART-4 vs. Reduced Hydrocarbon vs. Super-Fast chemistry B. Brown-Steiner et al. 10.5194/gmd-11-4155-2018
32. CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model D. Pendergrass et al. 10.5194/gmd-16-4793-2023
33. Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system J. Guerrette & D. Henze 10.5194/gmd-8-1857-2015
34. Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model J. Huang et al. 10.1016/j.srs.2024.100146
35. Re-framing the Gaussian dispersion model as a nonlinear regression scheme for retrospective air quality assessment at a high spatial and temporal resolution S. Chen et al. 10.1016/j.envsoft.2019.104620
36. Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter M. Pang et al. 10.1016/j.atmosenv.2023.119831
37. Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
38. Local search methods for the solution of implicit inverse problems E. Nino-Ruiz et al. 10.1007/s00500-017-2670-z
39. The impact of data assimilation into the meteorological WRF model on birch pollen modelling M. Werner et al. 10.1016/j.scitotenv.2021.151028
40. Tropospheric ozone data assimilation in the NASA GEOS Composition Forecast modeling system (GEOS-CF v2.0) using satellite data for ozone vertical profiles (MLS), total ozone columns (OMI), and thermal infrared radiances (AIRS, IASI) M. Kelp et al. 10.1088/1748-9326/acf0b7
41. Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period S. Ha et al. 10.5194/acp-20-6015-2020
42. Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations D. Alvim et al. 10.3390/rs13112231
43. A benchmark for testing the accuracy and computational cost of shortwave top-of-atmosphere reflectance calculations in clear-sky aerosol-laden atmospheres J. Escribano et al. 10.5194/gmd-12-805-2019
44. 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
45. The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015 J. Flemming et al. 10.5194/acp-17-1945-2017
46. Spatio-temporal analysis with short- and long-memory dependence: a state-space approach G. Ferreira et al. 10.1007/s11749-017-0541-7
47. A Knowledge-Aided Robust Ensemble Kalman Filter Algorithm for Non-Linear and Non-Gaussian Large Systems S. Lopez-Restrepo et al. 10.3389/fams.2022.830116
48. Sources, variability, long-term trends, and radiative forcing of aerosols in the Arctic: implications for Arctic amplification J. Kuttippurath et al. 10.1007/s11356-023-31245-6
49. A review of numerical models to predict the atmospheric dispersion of radionuclides Á. Leelőssy et al. 10.1016/j.jenvrad.2017.11.009
50. What Can We Expect from Data Assimilation for Air Quality Forecast? Part I: Quantification with Academic Test Cases L. Menut & B. Bessagnet 10.1175/JTECH-D-18-0002.1
51. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) R. Gelaro et al. 10.1175/JCLI-D-16-0758.1
52. Opinion: Beyond global means – novel space-based approaches to indirectly constrain the concentrations of and trends and variations in the tropospheric hydroxyl radical (OH) B. Duncan et al. 10.5194/acp-24-13001-2024
53. Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
54. Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields M. Sofiev et al. 10.1016/j.scitotenv.2023.165800
55. Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. 10.1175/MWR-D-16-0289.1
56. Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0 E. Emili et al. 10.5194/gmd-9-3933-2016
57. Impact of intercontinental pollution transport on North American ozone air pollution: an HTAP phase 2 multi-model study M. Huang et al. 10.5194/acp-17-5721-2017
58. Advances in air quality research – current and emerging challenges R. Sokhi et al. 10.5194/acp-22-4615-2022
59. Digital twins in process engineering: An overview on computational and numerical methods L. Peterson et al. 10.1016/j.compchemeng.2024.108917
60. Switching to electric vehicles can lead to significant reductions of PM2.5 and NO2 across China L. Wang et al. 10.1016/j.oneear.2021.06.008
61. 基于高分辨率气溶胶观测资料的多尺度三维变分同化及预报 增. 臧 et al. 10.1360/SSTe-2022-0026
62. 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. 10.5194/gmd-10-4743-2017
63. Sequential data assimilation algorithms for air quality monitoring models based on a weak-constraint variational principle A. Penenko et al. 10.1134/S1995423916040054
64. The remote sensing of radiative forcing by light-absorbing particles (LAPs) in seasonal snow over northeastern China W. Pu et al. 10.5194/acp-19-9949-2019
65. Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
66. Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM2.5 Predictions M. Gao et al. 10.1021/acs.est.6b03745
67. Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations Z. Peng et al. 10.1029/2020GL089030
68. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
69. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
70. 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
71. 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
72. Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980–2021) Y. Ouma et al. 10.1016/j.scitotenv.2023.169027
73. Development of emissions inventory and identification of sources for priority control in the middle reaches of Yangtze River Urban Agglomerations X. Sun et al. 10.1016/j.scitotenv.2017.12.103
74. Variational data assimilation for the optimized ozone initial state and the short-time forecasting S. Park et al. 10.5194/acp-16-3631-2016
75. On the limit to the accuracy of regional-scale air quality models S. Rao et al. 10.5194/acp-20-1627-2020
76. 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
77. Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method X. Zhang et al. 10.1029/2020JD033599
78. Numerical study of variational data assimilation algorithms based on decomposition methods in atmospheric chemistry models A. Penenko & P. Antokhin 10.1088/1755-1315/48/1/012021
79. Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe M. Chrit & M. Majdi 10.3390/atmos13050763
80. Subregional inversion of North African dust sources J. Escribano et al. 10.1002/2016JD025020
81. Classifying aerosol type using in situ and satellite observations over a semi-arid station, Anantapur, from southern peninsular India S. Vadde et al. 10.1016/j.asr.2023.03.046
82. 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
83. Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. 10.3390/atmos10120798
84. Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. 10.3402/tellusb.v68.29812
85. Assessing the effect of long-range pollutant transportation on air quality in Seoul using the conditional potential source contribution function method U. Jeong et al. 10.1016/j.atmosenv.2016.11.017
86. Variability of satellite-based total aerosols and the relationship with emission, meteorology and landscape in North China during 2000–2016 Y. Feng et al. 10.1007/s12665-018-7685-y
87. Significant wintertime PM<sub>2.5</sub> mitigation in the Yangtze River Delta, China, from 2016 to 2019: observational constraints on anthropogenic emission controls L. Wang et al. 10.5194/acp-20-14787-2020
88. Bayesian Inference Approach to Quantify Primary and Secondary Organic Carbon in Fine Particulate Matter Using Major Species Measurements K. Liao et al. 10.1021/acs.est.2c09412
89. Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions S. Li et al. 10.5194/gmd-16-4171-2023
90. Thoughts on Earth System Modeling: From global to regional scale E. Canepa & P. Builtjes 10.1016/j.earscirev.2017.06.017
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95. Comparison between the assimilation of IASI Level 2 ozone retrievals and Level 1 radiances in a chemical transport model E. Emili et al. 10.5194/amt-12-3963-2019
96. Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method K. Chan et al. 10.3390/rs13050969
97. Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. 10.1021/acs.est.0c01680
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99. Multi-species chemical data assimilation with the Danish Eulerian hemispheric model: system description and verification J. Silver et al. 10.1007/s10874-015-9326-0
100. Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions R. Žabkar et al. 10.5194/gmd-8-2119-2015
101. Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest A. Varga-Balogh et al. 10.3390/atmos11060669
102. Application of Data Fusion Techniques to Improve Air Quality Forecast: A Case Study in the Northern Italy C. Carnevale et al. 10.3390/atmos11030244
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106. Improved method for characterising temporal variability in urban air quality part I: Traffic emissions in central Poland A. Podstawczyńska & S. Chambers 10.1016/j.atmosenv.2019.117038
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108. Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements G. Fu et al. 10.5194/acp-16-9189-2016
109. Retrieving Vertical Cloud Radar Reflectivity from MODIS Cloud Products with CGAN: An Evaluation for Different Cloud Types and Latitudes F. Wang et al. 10.3390/rs15030816
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114. Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders I. De Feis et al. 10.3390/s20082352
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117. Evaluation of NASA's high-resolution global composition simulations: Understanding a pollution event in the Chesapeake Bay during the summer 2017 OWLETS campaign N. Dacic et al. 10.1016/j.atmosenv.2019.117133
118. An intercomparison of tropospheric ozone reanalysis products from CAMS, CAMS interim, TCR-1, and TCR-2 V. Huijnen et al. 10.5194/gmd-13-1513-2020
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121. 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
122. A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0) J. Haussaire & M. Bocquet 10.5194/gmd-9-393-2016
123. A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al. 10.1155/2021/8970949
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125. Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees P. Ivatt & M. Evans 10.5194/acp-20-8063-2020
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128. Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene C. Ling et al. 10.5194/acp-23-3311-2023
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130. Advances in air quality modeling and forecasting A. Baklanov & Y. Zhang 10.1016/j.glt.2020.11.001
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142. Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula Y. Cho et al. 10.1007/s13351-024-3152-8
143. Source reconstruction of airborne toxics based on acute health effects information C. Argyropoulos et al. 10.1038/s41598-018-23767-8
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146. Balance of Emission and Dynamical Controls on Ozone During the Korea‐United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation K. Miyazaki et al. 10.1029/2018JD028912
147. Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
148. Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts C. Ma et al. 10.1007/s00376-017-7179-y
149. Improving the accuracy of O3 prediction from a chemical transport model with a random forest model in the Yangtze River Delta region, China K. Xiong et al. 10.1016/j.envpol.2022.120926
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