130 citations as recorded by crossref.

1. Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime S. Feng et al. 10.1016/j.atmosenv.2018.05.049
2. Improving Surface PM 2.5 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. 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
5. Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2 S. Silva & A. Arellano 10.3390/rs9070744
6. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
7. The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
8. Origin of Fine Particulate Carbon in the Rural United States B. Schichtel et al. 10.1021/acs.est.7b00645
9. Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM 2.5 and PM 10 Forecasts in the Sichuan Basin Z. Zhang et al. 10.1029/2020EA001614
10. Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
11. 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
12. Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics R. Ménard & M. Deshaies-Jacques 10.3390/atmos9030086
13. 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
14. 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
15. 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
16. Variational approach to the study of processes of geophysical hydro-thermodynamics with assimilation of observation data V. Penenko et al. 10.1134/S0021894417050029
17. 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
18. 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
19. The use of forecast gradients in 3DVar data assimilation Z. Zhu et al. 10.1016/j.apm.2019.04.038
20. Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
21. Inverse problems for the study of climatic and ecological processes under anthropogenic influences V. Penenko & E. Tsvetova 10.1088/1755-1315/386/1/012036
22. 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
23. 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
24. 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
25. 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
26. Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
27. Local search methods for the solution of implicit inverse problems E. Nino-Ruiz et al. 10.1007/s00500-017-2670-z
28. The impact of data assimilation into the meteorological WRF model on birch pollen modelling M. Werner et al. 10.1016/j.scitotenv.2021.151028
29. 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
30. Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations D. Alvim et al. 10.3390/rs13112231
31. 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
32. The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015 J. Flemming et al. 10.5194/acp-17-1945-2017
33. Spatio-temporal analysis with short- and long-memory dependence: a state-space approach G. Ferreira et al. 10.1007/s11749-017-0541-7
34. The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) G. Pfister et al. 10.1175/BAMS-D-19-0331.1
35. 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
36. A review of numerical models to predict the atmospheric dispersion of radionuclides Á. Leelőssy et al. 10.1016/j.jenvrad.2017.11.009
37. 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
38. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) R. Gelaro et al. 10.1175/JCLI-D-16-0758.1
39. Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. 10.1175/MWR-D-16-0289.1
40. 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
41. 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
42. 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
43. 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
44. Sequential data assimilation algorithms for air quality monitoring models based on a weak-constraint variational principle A. Penenko et al. 10.1134/S1995423916040054
45. 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
46. Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
47. 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
48. Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations Z. Peng et al. 10.1029/2020GL089030
49. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
50. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
51. Assessment of the Meteorological Impact on Improved PM 2.5 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
52. 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
53. 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
54. Variational data assimilation for the optimized ozone initial state and the short-time forecasting S. Park et al. 10.5194/acp-16-3631-2016
55. On the limit to the accuracy of regional-scale air quality models S. Rao et al. 10.5194/acp-20-1627-2020
56. 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
57. Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method X. Zhang et al. 10.1029/2020JD033599
58. 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
59. Subregional inversion of North African dust sources J. Escribano et al. 10.1002/2016JD025020
60. Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. 10.3390/atmos10120798
61. Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. 10.3402/tellusb.v68.29812
62. 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
63. 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
64. 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
65. Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems A. Penenko et al. 10.3390/atmos12121697
66. Thoughts on Earth System Modeling: From global to regional scale E. Canepa & P. Builtjes 10.1016/j.earscirev.2017.06.017
67. A review on methodology in O3-NOx-VOC sensitivity study C. Liu & K. Shi 10.1016/j.envpol.2021.118249
68. Investigating sources of variability and error in simulations of carbon dioxide in an urban region C. Martin et al. 10.1016/j.atmosenv.2018.11.013
69. A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
70. Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis K. Miyazaki et al. 10.5194/acp-20-931-2020
71. 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
72. Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method K. Chan et al. 10.3390/rs13050969
73. Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. 10.1021/acs.est.0c01680
74. Estimating NOx LOTOS-EUROS CTM Emission Parameters over the Northwest of South America through 4DEnVar TROPOMI NO2 Assimilation A. Yarce Botero et al. 10.3390/atmos12121633
75. 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
76. 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
77. 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
78. Application of Data Fusion Techniques to Improve Air Quality Forecast: A Case Study in the Northern Italy C. Carnevale et al. 10.3390/atmos11030244
79. Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks A. Sayeed et al. 10.1016/j.atmosenv.2021.118376
80. Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models: a case study for biogenic emissions from EURAD-IM version 5 A. Vogel & H. Elbern 10.5194/gmd-14-5583-2021
81. 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
82. 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
83. Enhanced parallelization of the incremental 4D‐Var data assimilation algorithm using the Randomized Incremental Optimal Technique N. Bousserez et al. 10.1002/qj.3740
84. Use of Satellite Data for Air Pollution Modeling in Bulgaria E. Georgieva et al. 10.3390/earth2030034
85. Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide E. Ryan & O. Wild 10.5194/gmd-14-5373-2021
86. Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders I. De Feis et al. 10.3390/s20082352
87. Variational methods for targeted monitoring of atmospheric quality by specified cost criteria V. Penenko 10.1088/1755-1315/211/1/012048
88. Influence of Northeast Monsoon cold surges on air quality in Southeast Asia M. Ashfold et al. 10.1016/j.atmosenv.2017.07.047
89. 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
90. 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
91. Key Issues for Seamless Integrated Chemistry–Meteorology Modeling A. Baklanov et al. 10.1175/BAMS-D-15-00166.1
92. Impact of Infrared Atmospheric Sounding Interferometer (IASI) thermal infrared measurements on global ozone reanalyses E. Emili & M. El Aabaribaoune 10.5194/gmd-14-6291-2021
93. 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
94. 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
95. A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al. 10.1155/2021/8970949
96. Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland M. Werner et al. 10.3390/rs11202364
97. Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees P. Ivatt & M. Evans 10.5194/acp-20-8063-2020
98. The potential for geostationary remote sensing of NO<sub>2</sub> to improve weather prediction X. Liu et al. 10.5194/acp-21-9573-2021
99. High concentrations of ice crystals in upper-tropospheric tropical clouds: is there a link to biomass and fossil fuel combustion? G. Raga et al. 10.5194/acp-22-2269-2022
100. High-spatiotemporal-resolution inverse estimation of CO and NOx emission reductions during emission control periods with a modified ensemble Kalman filter H. Wu et al. 10.1016/j.atmosenv.2020.117631
101. Advances in air quality modeling and forecasting A. Baklanov & Y. Zhang 10.1016/j.glt.2020.11.001
102. Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
103. Toward Improving Short‐Term Predictions of Fine Particulate Matter Over the United States Via Assimilation of Satellite Aerosol Optical Depth Retrievals R. Kumar et al. 10.1029/2018JD029009
104. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation C. Randles et al. 10.1175/JCLI-D-16-0609.1
105. COVID-19 incidence and mortality in Lombardy, Italy: An ecological study on the role of air pollution, meteorological factors, demographic and socioeconomic variables E. De Angelis et al. 10.1016/j.envres.2021.110777
106. Consistent Numerical Schemes for Solving Nonlinear Inverse Source Problems with Gradient-Type Algorithms and Newton–Kantorovich Methods A. Penenko 10.1134/S1995423918010081
107. Analyzing highly uncertain source regions in the Ex-UTLS and their effects on small-scale atmospheric composition using probabilistic retroplume calculations A. Vogel et al. 10.1016/j.atmosenv.2019.117063
108. Spatial boundaries of Aerosol Robotic Network observations over the Mediterranean basin A. Mishra et al. 10.1002/2015GL067630
109. Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10 C. Keller & M. Evans 10.5194/gmd-12-1209-2019
110. A comparison of correlation-length estimation methods for the objective analysis of surface pollutants at Environment and Climate Change Canada R. Ménard et al. 10.1080/10962247.2016.1177620
111. Source reconstruction of airborne toxics based on acute health effects information C. Argyropoulos et al. 10.1038/s41598-018-23767-8
112. Diurnal patterns in ambient PM2.5 exposure over India using MERRA-2 reanalysis data K. Bali et al. 10.1016/j.atmosenv.2020.118180
113. Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory R. Dumitrache et al. 10.1016/j.atmosenv.2016.08.063
114. 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
115. Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
116. 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
117. Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
118. Impact of Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth and AirNow PM 2.5 assimilation on Community Multi‐scale Air Quality aerosol predictions over the contiguous United States T. Chai et al. 10.1002/2016JD026295
119. 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
120. Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0 E. Di Tomaso et al. 10.5194/gmd-10-1107-2017
121. EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06) S. Skachko et al. 10.5194/gmd-9-2893-2016
122. Assimilation of PM2.5 ground base observations to two chemical schemes in WRF-Chem – The results for the winter and summer period M. Werner et al. 10.1016/j.atmosenv.2018.12.016
123. A variational approach to environmental and climatic problems of urban agglomerations V. Penenko & E. Tsvetova 10.1088/1755-1315/48/1/012020
124. Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations J. Hong et al. 10.1016/j.envpol.2020.114451
125. Lidar vertical observation network and data assimilation reveal key processes driving the 3-D dynamic evolution of PM<sub>2.5</sub> concentrations over the North China Plain Y. Xiang et al. 10.5194/acp-21-7023-2021
126. Evaluation of ECMWF IFS-AER (CAMS) operational forecasts during cycle 41r1–46r1 with calibrated ceilometer profiles over Germany H. Flentje et al. 10.5194/gmd-14-1721-2021
127. The air quality forecast rote: Recent changes and future challenges W. Ryan 10.1080/10962247.2016.1151469
128. Numerical Investigation of the Direct Variational Algorithm of Data Assimilation in the Urban Scenario A. Penenko et al. 10.1134/S102485601806012X
129. Algorithms for the inverse modelling of transport and transformation of atmospheric pollutants A. Penenko 10.1088/1755-1315/211/1/012052
130. A Newton–Kantorovich Method in Inverse Source Problems for Production-Destruction Models with Time Series-Type Measurement Data A. Penenko 10.1134/S1995423919010051