191 citations as recorded by crossref.

1. Application of direct regularization techniques and bounded–variable least squares for inverse modeling of an urban emissions inventory A. Vanoye & A. Mendoza 10.5094/APR.2014.027
2. 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
3. 基于WRF-Chem/DART的硫酸盐化学反应速率同化研究 丛. 黄 et al. 10.1360/SSTe-2023-0057
4. Thoughts on Earth System Modeling: From global to regional scale E. Canepa & P. Builtjes 10.1016/j.earscirev.2017.06.017
5. The Parameters Estimation for a PM2.5Transport Model with the Adjoint Method D. Wang et al. 10.1155/2016/9873815
6. Dynamically Constrained Interpolation of the Sparsely Observed Suspended Sediment Concentrations in Both Space and Time: A Case Study in the Bohai Sea X. Mao et al. 10.1175/JTECH-D-17-0149.1
7. The impact of multi-species surface chemical observation assimilation on air quality forecasts in China Z. Peng et al. 10.5194/acp-18-17387-2018
8. Estimation of volatile organic compound emissions for Europe using data assimilation M. Koohkan et al. 10.5194/acp-13-5887-2013
9. Spatial representativeness of PM2.5 monitoring stations and its implication for health assessment H. Bai et al. 10.1007/s11869-022-01202-2
10. MesSBAR—Multicopter and Instrumentation for Air Quality Research L. Bretschneider et al. 10.3390/atmos13040629
11. 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
12. Top‐down analysis of the elemental carbon emissions inventory in the United States by inverse modeling using Community Multiscale Air Quality model with decoupled direct method (CMAQ‐DDM) Y. Hu et al. 10.1029/2009JD011987
13. Evaluating the Impact of Emissions Regulations on the Emissions Reduction During the 2015 China Victory Day Parade With an Ensemble Square Root Filter K. Chu et al. 10.1002/2017JD027631
14. An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data H. Kim et al. 10.3390/rs14020389
15. Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. 10.1175/MWR-D-16-0289.1
16. Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant: Application to the reconstruction of the cesium‐137 and iodine‐131 source terms from the Fukushima Daiichi power plant V. Winiarek et al. 10.1029/2011JD016932
17. Performance of a local ensemble transform Kalman filter for the analysis of atmospheric circulation and distribution of long‐lived tracers under idealized conditions K. Miyazaki 10.1029/2009JD011892
18. A feature point scheme for improving estimation of the temporally varying bottom friction coefficient in tidal models using adjoint method D. Wang et al. 10.1016/j.oceaneng.2020.108481
19. Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects Y. Zhang et al. 10.1016/j.atmosenv.2012.02.041
20. Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
21. 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
22. On the observability of chemical and physical aerosol properties by optical observations: Inverse modelling with variational data assimilation M. Kahnert 10.1111/j.1600-0889.2009.00436.x
23. 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
24. 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
25. Toward the next generation of air quality monitoring: Ozone K. Bowman 10.1016/j.atmosenv.2013.07.007
26. Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima Daiichi accidental releases of radionuclides Y. Liu et al. 10.1002/qj.3138
27. CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations Z. Peng et al. 10.1029/2022JD037154
28. Estimation of the caesium-137 source term from the Fukushima Daiichi nuclear power plant using a consistent joint assimilation of air concentration and deposition observations V. Winiarek et al. 10.1016/j.atmosenv.2013.10.017
29. 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
30. Investigating a high ozone episode in a rural mountain site A. Monteiro et al. 10.1016/j.envpol.2011.11.008
31. A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC L. Kong et al. 10.5194/essd-13-529-2021
32. Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer P. Monks et al. 10.5194/acp-15-8889-2015
33. Improving ozone forecasts over Europe by synergistic use of the LOTOS-EUROS chemical transport model and in-situ measurements R. Curier et al. 10.1016/j.atmosenv.2012.06.017
34. Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM A. Monteiro et al. 10.1007/s10666-012-9344-0
35. Retrospective analysis of 2015–2017 wintertime PM<sub>2.5</sub> in China: response to emission regulations and the role of meteorology D. Chen et al. 10.5194/acp-19-7409-2019
36. Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal A. Monteiro et al. 10.1007/s10666-013-9358-2
37. Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method S. Zhang et al. 10.1016/j.atmosenv.2021.118896
38. Multi-species inversion of CH<sub>4</sub>, CO and H<sub>2</sub> emissions from surface measurements I. Pison et al. 10.5194/acp-9-5281-2009
39. 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
40. Adjoint inversion modeling of Asian dust emission using lidar observations K. Yumimoto et al. 10.5194/acp-8-2869-2008
41. Comparison of OMI NO<sub>2</sub> tropospheric columns with an ensemble of global and European regional air quality models V. Huijnen et al. 10.5194/acp-10-3273-2010
42. Three‐dimensional variational data assimilation of ozone and fine particulate matter observations: some results using the Weather Research and Forecasting—Chemistry model and Grid‐point Statistical Interpolation M. Pagowski et al. 10.1002/qj.700
43. Retrieving global aerosol sources from satellites using inverse modeling O. Dubovik et al. 10.5194/acp-8-209-2008
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. Tracing the origin of odour nuisance from citizens’ notifications with the SMART modelling system S. Trini Castelli et al. 10.1016/j.atmosenv.2023.119992
46. 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
47. Inversion of CO emissions over Beijing and its surrounding areas with ensemble Kalman filter X. Tang et al. 10.1016/j.atmosenv.2013.08.051
48. 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
49. Particle-filter-based volcanic ash emission inversion applied to a hypothetical sub-Plinian Eyjafjallajökull eruption using the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-chem) version 1.0 P. Franke et al. 10.5194/gmd-15-1037-2022
50. Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions X. Tang et al. 10.5194/acp-11-12901-2011
51. A regional air quality forecasting system over Europe: the MACC-II daily ensemble production V. Marécal et al. 10.5194/gmd-8-2777-2015
52. Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion J. Meirink et al. 10.5194/acp-8-6341-2008
53. Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. 10.3390/atmos14040758
54. Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China M. Li et al. 10.1016/j.atmosenv.2024.120848
55. An inversion technique for the retrieval of single-point emissions from atmospheric concentration measurements M. Sharan et al. 10.1098/rspa.2008.0402
56. 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
57. Data assimilation of satellite-retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF's Composition-IFS A. Inness et al. 10.5194/acp-15-5275-2015
58. Impact of assimilated observations on improving tropospheric ozone simulations P. Messina et al. 10.1016/j.atmosenv.2011.08.056
59. A Methodology for Estimating the Parameters in Three-Dimensional Cohesive Sediment Transport Models by Assimilating In Situ Observations with the Adjoint Method D. Wang et al. 10.1175/JTECH-D-16-0194.1
60. Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook M. Sofiev et al. 10.5194/acp-17-12341-2017
61. Application of data assimilation technology in source apportionment of PM2.5 during winter haze episodes in the Beijing-Tianjin-Hebei region in China T. Sun et al. 10.1016/j.apr.2022.101546
62. Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE-v2019 A. Fortems-Cheiney et al. 10.5194/gmd-14-2939-2021
63. 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
64. Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts P. Saide et al. 10.5194/acp-13-10425-2013
65. A review of operational, regional-scale, chemical weather forecasting models in Europe J. Kukkonen et al. 10.5194/acp-12-1-2012
66. Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
67. Kalman filter-based air quality forecast adjustment K. De Ridder et al. 10.1016/j.atmosenv.2012.01.032
68. NOx Emission Changes Over China During the COVID‐19 Epidemic Inferred From Surface NO2 Observations S. Feng et al. 10.1029/2020GL090080
69. Evaluating and optimizing PM2.5 stations in Yangtze River Delta from a spatial representativeness perspective H. Bai et al. 10.1016/j.apgeog.2023.102949
70. European air quality in view of the WHO 2021 guideline levels: Effect of emission reductions on air pollution exposure P. Franke et al. 10.1525/elementa.2023.00127
71. Observation and integrated Earth-system science: A roadmap for 2016–2025 A. Simmons et al. 10.1016/j.asr.2016.03.008
72. COST ES0602: towards a European network on chemical weather forecasting and information systems J. Kukkonen et al. 10.5194/asr-3-27-2009
73. Assimilation of OMI NO<sub>2</sub> retrievals into the limited-area chemistry-transport model DEHM (V2009.0) with a 3-D OI algorithm J. Silver et al. 10.5194/gmd-6-1-2013
74. 3-D evaluation of tropospheric ozone simulations by an ensemble of regional Chemistry Transport Model D. Zyryanov et al. 10.5194/acp-12-3219-2012
75. Status and future of numerical atmospheric aerosol prediction with a focus on data requirements A. Benedetti et al. 10.5194/acp-18-10615-2018
76. Air quality over Portugal in 2020 A. Monteiro et al. 10.5094/APR.2015.087
77. Bayesian design of control space for optimal assimilation of observations. Part I: Consistent multiscale formalism M. Bocquet et al. 10.1002/qj.837
78. Chemical state estimation for the middle atmosphere by four‐dimensional variational data assimilation: A posteriori validation of error statistics in observation space J. Schwinger & H. Elbern 10.1029/2009JD013115
79. Radioactive Contamination Control by Atmospheric Dispersion Assessment of Airborne Indicator Contaminants: Numerical Model Validation A. Dahia et al. 10.1007/s10666-018-9598-2
80. 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
81. PM<sub>10</sub> data assimilation over Europe with the optimal interpolation method M. Tombette et al. 10.5194/acp-9-57-2009
82. Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations Z. Peng et al. 10.1029/2020GL089030
83. Estimation of spatially varying parameters in three-dimensional cohesive sediment transport models by assimilating remote sensing data J. Zhang et al. 10.1007/s00773-017-0477-3
84. Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas X. Tang et al. 10.5194/acp-16-6395-2016
85. Simulation of Air Quality Over South Korea Using the WRF-Chem Model: Impacts of Chemical Initial and Lateral Boundary Conditions J. Lee et al. 10.14191/Atmos.2015.25.4.639
86. Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area F. Hennig et al. 10.3390/atmos7030048
87. Measuring atmospheric composition change P. Laj et al. 10.1016/j.atmosenv.2009.08.020
88. Constraining surface emissions of air pollutants using inverse modelling: method intercomparison and a new two-step two-scale regularization approach P. Saide et al. 10.1111/j.1600-0889.2011.00529.x
89. 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
90. Observational evidence reveals the significance of nocturnal chemistry in seasonal secondary organic aerosol formation L. Liu et al. 10.1038/s41612-024-00747-6
91. Assimilation of satellite NO<sub>2</sub> observations at high spatial resolution using OSSEs X. Liu et al. 10.5194/acp-17-7067-2017
92. Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region I. Konovalov et al. 10.5194/acp-11-10031-2011
93. Toward Optimal Choices of Control Space Representation for Geophysical Data Assimilation M. Bocquet 10.1175/2009MWR2789.1
94. Data assimilation of stratospheric constituents: a review W. Lahoz et al. 10.5194/acp-7-5745-2007
95. Updated aerosol module and its application to simulate secondary organic aerosols during IMPACT campaign May 2008 Y. Li et al. 10.5194/acp-13-6289-2013
96. 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
97. Four-dimensional variational inversion of black carbon emissions during ARCTAS-CARB with WRFDA-Chem J. Guerrette & D. Henze 10.5194/acp-17-7605-2017
98. 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
99. Simplified aerosol modeling for variational data assimilation N. Huneeus et al. 10.5194/gmd-2-213-2009
100. A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region X. Cheng et al. 10.5194/acp-21-13747-2021
101. 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
102. Evaluation of atmospheric aerosols in the metropolitan area of São Paulo simulated by the regional EURAD-IM model on high-resolution E. De Souza Fernandes Duarte et al. 10.1016/j.apr.2020.12.006
103. NO<sub>2</sub> photolysis frequencies in street canyons P. Koepke et al. 10.5194/acp-10-7457-2010
104. 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
105. 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
106. Inverse modeling of emissions and their time profiles J. Resler et al. 10.5094/APR.2010.036
107. Estimation of Volcanic Ash Emissions Using Trajectory-Based 4D-Var Data Assimilation S. Lu et al. 10.1175/MWR-D-15-0194.1
108. Combined effect of surface PM2.5 assimilation and aerosol-radiation interaction on winter severe haze prediction in central and eastern China Y. Peng et al. 10.1016/j.apr.2023.101802
109. Sequential data assimilation algorithms for air quality monitoring models based on a weak-constraint variational principle A. Penenko et al. 10.1134/S1995423916040054
110. Joint state and parameter estimation with an iterative ensemble Kalman smoother M. Bocquet & P. Sakov 10.5194/npg-20-803-2013
111. 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
112. Parameter‐field estimation for atmospheric dispersion: application to the Chernobyl accident using 4D‐Var M. Bocquet 10.1002/qj.961
113. 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
114. Identifying forecast uncertainties for biogenic gases in the Po Valley related to model configuration in EURAD-IM during PEGASOS 2012 A. Vogel & H. Elbern 10.5194/acp-21-4039-2021
115. Merging citizen science with epidemiology: design of a prospective feasibility study of health events and air pollution in Cologne, Germany S. Soja et al. 10.1186/s40814-023-01250-0
116. 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
117. 3DVar sectoral emission inversion based on source apportionment and machine learning C. Huang et al. 10.1016/j.envpol.2024.125140
118. Application of a chemical transport model and optimized data assimilation methods to improve air quality assessment C. Silibello et al. 10.1007/s11869-014-0235-1
119. Compound dry-hot-fire events connecting Central and Southeastern South America: an unapparent and deadly ripple effect D. Monteiro dos Santos et al. 10.1038/s44304-024-00031-w
120. Synergistic use of OMI NO2 tropospheric columns and LOTOS–EUROS to evaluate the NOx emission trends across Europe R. Curier et al. 10.1016/j.rse.2014.03.032
121. Modeling study of a severe aerosol pollution event in December 2013 over Shanghai China: An application of chemical data assimilation J. Wu et al. 10.1016/j.partic.2014.10.008
122. Evaluation of a regional air quality forecast model for tropospheric NO<sub>2</sub> columns using the OMI/Aura satellite tropospheric NO<sub>2</sub> product F. Herron-Thorpe et al. 10.5194/acp-10-8839-2010
123. 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. 10.5194/gmd-14-337-2021
124. Sensitivity of Latent Heat Fluxes to Initial Values and Parameters of a Land‐Surface Model J. Schwinger et al. 10.2136/vzj2009.0190
125. Using Geographically Referenced Data on Environmental Exposures for Public Health Research: A Feasibility Study Based on the German Socio-Economic Panel Study (SOEP) S. Voigtländer et al. 10.2139/ssrn.1884910
126. Challenges for a New Air Quality Directive: The role of monitoring and modelling techniques C. Borrego et al. 10.1016/j.uclim.2014.06.007
127. Singular vector decomposition for sensitivity analyses of tropospheric chemical scenarios N. Goris & H. Elbern 10.5194/acp-13-5063-2013
128. Parameter estimation for a cohesive sediment transport model by assimilating satellite observations in the Hangzhou Bay: Temporal variations and spatial distributions D. Wang et al. 10.1016/j.ocemod.2017.11.007
129. Seasonal trends of dry and bulk concentration of nitrogen compounds over a rain forest in Ghana F. Fattore et al. 10.5194/bg-11-3069-2014
130. 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
131. Ubiquity of organic nitrates from nighttime chemistry in the European submicron aerosol A. Kiendler‐Scharr et al. 10.1002/2016GL069239
132. The High Order Conservative Method for the Parameters Estimation in a PM2.5 Transport Adjoint Model N. Li et al. 10.1155/2017/4626585
133. 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
134. Online coupled regional meteorology chemistry models in Europe: current status and prospects A. Baklanov et al. 10.5194/acp-14-317-2014
135. Probing into the impact of 3DVAR assimilation of surface PM10 observations over China using process analysis Z. Jiang et al. 10.1002/jgrd.50495
136. Variational assimilation of IASI SO<sub>2</sub> plume height and total column retrievals in the 2010 eruption of Eyjafjallajökull using the SILAM v5.3 chemistry transport model J. Vira et al. 10.5194/gmd-10-1985-2017
137. Retrieval of desert dust and carbonaceous aerosol emissions over Africa from POLDER/PARASOL products generated by the GRASP algorithm C. Chen et al. 10.5194/acp-18-12551-2018
138. Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations S. Lee et al. 10.3390/rs14092123
139. Performance assessment of CHIMERE and EURAD-IM’ dust modules C. Gama et al. 10.1016/j.apr.2019.03.005
140. Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter Z. Peng et al. 10.5194/acp-17-4837-2017
141. Air quality observations onboard commercial and targeted Zeppelin flights in Germany – a platform for high-resolution trace-gas and aerosol measurements within the planetary boundary layer R. Tillmann et al. 10.5194/amt-15-3827-2022
142. Estimation of Bottom Friction Coefficient in Multi‐Constituent Tidal Models Using the Adjoint Method: Temporal Variations and Spatial Distributions D. Wang et al. 10.1029/2020JC016949
143. The assessment of potential observability for joint chemical states and emissions in atmospheric modelings X. Wu et al. 10.1007/s00477-021-02113-x
144. On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
145. Regional representativity of AERONET observation sites during the biomass burning season in South America determined by correlation studies with MODIS Aerosol Optical Depth J. Hoelzemann et al. 10.1029/2008JD010369
146. Intercomparison of Air Quality Models in a Megacity: Toward an Operational Ensemble Forecasting System for São Paulo A. Deroubaix et al. 10.1029/2022JD038179
147. 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
148. Regional NOx emission inversion through a four-dimensional variational approach using SCIAMACHY tropospheric NO2 column observations T. Chai et al. 10.1016/j.atmosenv.2009.06.052
149. Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model B. Gaubert et al. 10.5194/gmd-7-283-2014
150. MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe M. Sofiev et al. 10.5194/acp-15-8115-2015
151. A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al. 10.1155/2021/8970949
152. Application of Dynamically Constrained Interpolation Methodology to the Surface Nitrogen Concentration in the Bohai Sea Q. Zheng et al. 10.3390/ijerph16132400
153. Improving predictability of high-ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign S. Ma et al. 10.5194/acp-21-16531-2021
154. Study on the assimilation of the sulphate reaction rates based on WRF-Chem/DART C. Huang et al. 10.1007/s11430-023-1153-9
155. Variational methods of data assimilation and inverse problems for studying the atmosphere, ocean, and environment V. Penenko 10.1134/S1995423909040065
156. Potential effects of using biodiesel in road-traffic on air quality over the Porto urban area, Portugal I. Ribeiro et al. 10.1016/j.atmosenv.2015.11.006
157. 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
158. On the representation error in data assimilation T. Janjić et al. 10.1002/qj.3130
159. The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework K. Yumimoto & T. Takemura 10.5194/gmd-6-2005-2013
160. Simultaneous assimilation of satellite NO<sub>2</sub>, O<sub>3</sub>, CO, and HNO<sub>3</sub> data for the analysis of tropospheric chemical composition and emissions K. Miyazaki et al. 10.5194/acp-12-9545-2012
161. 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
162. European pollen reanalysis, 1980–2022, for alder, birch, and olive M. Sofiev et al. 10.1038/s41597-024-03686-2
163. How bias-correction can improve air quality forecasts over Portugal C. Borrego et al. 10.1016/j.atmosenv.2011.09.006
164. 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
165. Singular vector-based targeted observations of chemical constituents: description and first application of the EURAD-IM-SVA v1.0 N. Goris & H. Elbern 10.5194/gmd-8-3929-2015
166. A comparison study of data assimilation algorithms for ozone forecasts L. Wu et al. 10.1029/2008JD009991
167. Chemical state estimation for the middle atmosphere by four‐dimensional variational data assimilation: System configuration H. Elbern et al. 10.1029/2009JD011953
168. An Observing System Simulation Experiment (OSSE) for Aerosol Optical Depth from Satellites R. Timmermans et al. 10.1175/2009JTECHA1263.1
169. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models M. Bocquet et al. 10.5194/acp-15-5325-2015
170. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
171. Optimal Control and Observation Locations for Time-Varying Systems on a Finite-Time Horizon X. Wu et al. 10.1137/15M1014759
172. Estimation of Initial Field in the Bohai Sea with the Adjoint Method: A Comparative Study on Optimization Algorithms C. Wang et al. 10.4028/www.scientific.net/AMM.571-572.196
173. Assimilation of surface NO<sub>2</sub> and O<sub>3</sub> observations into the SILAM chemistry transport model J. Vira & M. Sofiev 10.5194/gmd-8-191-2015
174. Assimilating Fengyun-4A observations to improve WRF-Chem PM2.5 predictions in China J. Hong et al. 10.1016/j.atmosres.2021.105878
175. Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method W. Chen et al. 10.3390/rs15010148
176. A refinement of the emission data for Kola Peninsula based on inverse dispersion modelling M. Prank et al. 10.5194/acp-10-10849-2010
177. A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysis P. Siljamo et al. 10.1007/s00484-012-0539-5
178. 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
179. Constraining non-methane VOC emissions with TROPOMI HCHO observations: impact on summertime ozone simulation in August 2022 in China S. Feng et al. 10.5194/acp-24-7481-2024
180. Implementation of aerosol assimilation in Gridpoint Statistical Interpolation (v. 3.2) and WRF-Chem (v. 3.4.1) M. Pagowski et al. 10.5194/gmd-7-1621-2014
181. 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
182. 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
183. Observation operator for the assimilation of aerosol type resolving satellite measurements into a chemical transport model M. Schroedter-Homscheidt et al. 10.5194/acp-10-10435-2010
184. The impact of observing characteristics on the ability to predict ozone under varying polluted photochemical regimes P. Hamer et al. 10.5194/acp-15-10645-2015
185. China’s Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2 S. Feng et al. 10.1021/acs.est.3c07756
186. 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. 10.1007/s11356-019-04246-7
187. On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations J. Vira & M. Sofiev 10.1016/j.atmosenv.2011.09.066
188. Assimilation of OMI NO2 retrievals into a regional chemistry-transport model for improving air quality forecasts over Europe X. Wang et al. 10.1016/j.atmosenv.2010.09.028
189. Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission K. Raeder et al. 10.5194/acp-7-5695-2007
190. The Assimilation of Envisat data (ASSET) project W. Lahoz et al. 10.5194/acp-7-1773-2007
191. Algorithms for the inverse modelling of transport and transformation of atmospheric pollutants A. Penenko 10.1088/1755-1315/211/1/012052