180 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 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 NO2 observations from the Sentinel-4 and Sentinel-5P missions on tropospheric NO2 analyses R. Timmermans et al. 10.5194/acp-19-12811-2019
7. Optimizing air quality forecasting with OSSE: Assessing the assimilation of aerosol species data via multi-resolution observation network design in the WRF-Chem and 3DVAR frameworks X. Chen et al. 10.1016/j.atmosres.2025.108213
8. Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2 S. Silva & A. Arellano 10.3390/rs9070744
9. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
10. The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
11. Next Generation Air Quality Models: Dynamical Mesh, New Insights into Mechanism, Datasets and Applications J. Li et al. 10.1007/s40726-025-00355-9
12. Origin of Fine Particulate Carbon in the Rural United States B. Schichtel et al. 10.1021/acs.est.7b00645
13. 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
14. Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
15. 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
16. 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
17. Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics R. Ménard & M. Deshaies-Jacques 10.3390/atmos9030086
18. Using Chemical Transport Model and Climatology Data as Backgrounds for Aerosol Optical Depth Spatial–Temporal Optimal Interpolation N. Miatselskaya et al. 10.3390/atmos16050623
19. 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
20. 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
21. 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
22. 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
23. Variational approach to the study of processes of geophysical hydro-thermodynamics with assimilation of observation data V. Penenko et al. 10.1134/S0021894417050029
24. 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
25. 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
26. Scattering and absorbing aerosols in the climate system J. Li et al. 10.1038/s43017-022-00296-7
27. 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
28. The use of forecast gradients in 3DVar data assimilation Z. Zhu et al. 10.1016/j.apm.2019.04.038
29. Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
30. 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
31. Inverse problems for the study of climatic and ecological processes under anthropogenic influences V. Penenko & E. Tsvetova 10.1088/1755-1315/386/1/012036
32. 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
33. Inverse Modeling of Formaldehyde Emissions and Assessment of Associated Cumulative Ambient Air Exposures at Fine Scale E. Olaguer 10.3390/atmos14060931
34. 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
35. 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
36. 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
37. Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model J. Huang et al. 10.1016/j.srs.2024.100146
38. 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
39. Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter M. Pang et al. 10.1016/j.atmosenv.2023.119831
40. Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
41. Local search methods for the solution of implicit inverse problems E. Nino-Ruiz et al. 10.1007/s00500-017-2670-z
42. The impact of data assimilation into the meteorological WRF model on birch pollen modelling M. Werner et al. 10.1016/j.scitotenv.2021.151028
43. 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
44. 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
45. Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations D. Alvim et al. 10.3390/rs13112231
46. 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
47. 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
48. The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015 J. Flemming et al. 10.5194/acp-17-1945-2017
49. Spatio-temporal analysis with short- and long-memory dependence: a state-space approach G. Ferreira et al. 10.1007/s11749-017-0541-7
50. 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
51. 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
52. A review of numerical models to predict the atmospheric dispersion of radionuclides Á. Leelőssy et al. 10.1016/j.jenvrad.2017.11.009
53. 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
54. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) R. Gelaro et al. 10.1175/JCLI-D-16-0758.1
55. 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
56. Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
57. 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
58. Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. 10.1175/MWR-D-16-0289.1
59. 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
60. 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
61. Advances in air quality research – current and emerging challenges R. Sokhi et al. 10.5194/acp-22-4615-2022
62. Digital twins in process engineering: An overview on computational and numerical methods L. Peterson et al. 10.1016/j.compchemeng.2024.108917
63. 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
64. 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
65. 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
66. Sequential data assimilation algorithms for air quality monitoring models based on a weak-constraint variational principle A. Penenko et al. 10.1134/S1995423916040054
67. 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
68. Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
69. 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
70. Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations Z. Peng et al. 10.1029/2020GL089030
71. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
72. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
73. 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
74. 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
75. 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
76. 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
77. Variational data assimilation for the optimized ozone initial state and the short-time forecasting S. Park et al. 10.5194/acp-16-3631-2016
78. On the limit to the accuracy of regional-scale air quality models S. Rao et al. 10.5194/acp-20-1627-2020
79. 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
80. Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method X. Zhang et al. 10.1029/2020JD033599
81. 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
82. 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
83. Subregional inversion of North African dust sources J. Escribano et al. 10.1002/2016JD025020
84. 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
85. 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
86. Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. 10.3390/atmos10120798
87. Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. 10.3402/tellusb.v68.29812
88. 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
89. 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
90. Significant wintertime PM2.5 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
91. A CO2–Δ14CO2 inversion setup for estimating European fossil CO2 emissions C. Gómez-Ortiz et al. 10.5194/acp-25-397-2025
92. 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
93. 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
94. Thoughts on Earth System Modeling: From global to regional scale E. Canepa & P. Builtjes 10.1016/j.earscirev.2017.06.017
95. A review on methodology in O3-NOx-VOC sensitivity study C. Liu & K. Shi 10.1016/j.envpol.2021.118249
96. 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
97. 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
98. Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis K. Miyazaki et al. 10.5194/acp-20-931-2020
99. 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
100. Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method K. Chan et al. 10.3390/rs13050969
101. Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. 10.1021/acs.est.0c01680
102. 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
103. 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
104. 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
105. 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
106. Application of Data Fusion Techniques to Improve Air Quality Forecast: A Case Study in the Northern Italy C. Carnevale et al. 10.3390/atmos11030244
107. 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
108. 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
109. Improved O3 predictions in China by combining chemical transport model and multi-source data with machining learning techniques K. Xiong et al. 10.1016/j.atmosenv.2023.120269
110. 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
111. 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. 10.5194/gmd-15-2773-2022
112. 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
113. 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
114. Enhanced parallelization of the incremental 4D‐Var data assimilation algorithm using the Randomized Incremental Optimal Technique N. Bousserez et al. 10.1002/qj.3740
115. Use of Satellite Data for Air Pollution Modeling in Bulgaria E. Georgieva et al. 10.3390/earth2030034
116. Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network Y. Lops et al. 10.1007/s00521-023-08558-1
117. 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
118. Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders I. De Feis et al. 10.3390/s20082352
119. Variational methods for targeted monitoring of atmospheric quality by specified cost criteria V. Penenko 10.1088/1755-1315/211/1/012048
120. Influence of Northeast Monsoon cold surges on air quality in Southeast Asia M. Ashfold et al. 10.1016/j.atmosenv.2017.07.047
121. 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
122. 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
123. Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data S. Zhu et al. 10.1139/er-2022-0125
124. 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
125. 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
126. 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
127. A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al. 10.1155/2021/8970949
128. 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
129. Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees P. Ivatt & M. Evans 10.5194/acp-20-8063-2020
130. The potential for geostationary remote sensing of NO2 to improve weather prediction X. Liu et al. 10.5194/acp-21-9573-2021
131. 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
132. 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
133. 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
134. Advances in air quality modeling and forecasting A. Baklanov & Y. Zhang 10.1016/j.glt.2020.11.001
135. Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
136. An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
137. 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
138. 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
139. 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
140. Consistent Numerical Schemes for Solving Nonlinear Inverse Source Problems with Gradient-Type Algorithms and Newton–Kantorovich Methods A. Penenko 10.1134/S1995423918010081
141. 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
142. Spatial boundaries of Aerosol Robotic Network observations over the Mediterranean basin A. Mishra et al. 10.1002/2015GL067630
143. 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
144. 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
145. 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
146. Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula Y. Cho et al. 10.1007/s13351-024-3152-8
147. Source reconstruction of airborne toxics based on acute health effects information C. Argyropoulos et al. 10.1038/s41598-018-23767-8
148. Diurnal patterns in ambient PM2.5 exposure over India using MERRA-2 reanalysis data K. Bali et al. 10.1016/j.atmosenv.2020.118180
149. 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
150. 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
151. Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
152. 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
153. 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
154. Enhancing long-term trend simulation of the global tropospheric hydroxyl (TOH) and its drivers from 2005 to 2019: a synergistic integration of model simulations and satellite observations A. Souri et al. 10.5194/acp-24-8677-2024
155. Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
156. Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application Z. Zang et al. 10.1007/s11430-022-9974-4
157. Impact of Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth and AirNow PM2.5 assimilation on Community Multi‐scale Air Quality aerosol predictions over the contiguous United States T. Chai et al. 10.1002/2016JD026295
158. 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
159. Digital Twins in Process Engineering: An Overview on Computational and Numerical Methods L. Peterson et al. 10.2139/ssrn.4747265
160. 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
161. Anthropogenic NOx Emission Estimations over East China for 2015 and 2019 Using OMI Satellite Observations and the New Inverse Modeling System CIF-CHIMERE D. Savas et al. 10.3390/atmos14010154
162. An Evaluation of Ground-Level Concentrations of Aerosols and Criteria Pollutants Using the CAMS Reanalysis Dataset over the Himawari-8 Observational Area, Including China, Indonesia, and Australia (2016–2023) M. Sowden 10.3390/air2040024
163. EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06) S. Skachko et al. 10.5194/gmd-9-2893-2016
164. 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
165. A variational approach to environmental and climatic problems of urban agglomerations V. Penenko & E. Tsvetova 10.1088/1755-1315/48/1/012020
166. Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations F. Lemmouchi et al. 10.3390/rs15061510
167. 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
168. A novel statistical-dynamical method for a seasonal forecast of particular matter in South Korea J. Jeong et al. 10.1016/j.scitotenv.2022.157699
169. Improving WRF-Chem PM2.5 predictions by combining data assimilation and deep-learning-based bias correction X. Ma et al. 10.1016/j.envint.2024.109199
170. Lidar vertical observation network and data assimilation reveal key processes driving the 3-D dynamic evolution of PM2.5 concentrations over the North China Plain Y. Xiang et al. 10.5194/acp-21-7023-2021
171. 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
172. Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
173. Application of Ensemble Kalman Smoothing in Inverse Modeling of Advection and Diffusion E. Klimova 10.1134/S1995423924030030
174. The air quality forecast rote: Recent changes and future challenges W. Ryan 10.1080/10962247.2016.1151469
175. Numerical Investigation of the Direct Variational Algorithm of Data Assimilation in the Urban Scenario A. Penenko et al. 10.1134/S102485601806012X
176. Structure of an Information and Computing System for Solving the Problem of Data Assimilation in Environmental Modeling V. Kotler et al. 10.25205/1818-7900-2024-22-1-21-30
177. European pollen reanalysis, 1980–2022, for alder, birch, and olive M. Sofiev et al. 10.1038/s41597-024-03686-2
178. Algorithms for the inverse modelling of transport and transformation of atmospheric pollutants A. Penenko 10.1088/1755-1315/211/1/012052
179. Data Assimilation Algorithm Based on the Sensitivity Operator for a Three-Dimensional Model of Transport and Transformation of Atmospheric Contaminants A. Penenko et al. 10.1134/S1024856024701082
180. A Newton–Kantorovich Method in Inverse Source Problems for Production-Destruction Models with Time Series-Type Measurement Data A. Penenko 10.1134/S1995423919010051