Articles | Volume 15, issue 10
https://doi.org/10.5194/acp-15-5325-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/acp-15-5325-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models
M. Bocquet
CEREA, Joint Laboratory École des Ponts ParisTech/EDF R&D, Université Paris-Est, Marne-la-Vallée, France
INRIA, Paris Rocquencourt Research Center, Rocquencourt, France
H. Elbern
Institute for Physics and Meteorology, University of Cologne, Cologne, Germany
H. Eskes
KNMI, De Bilt, The Netherlands
M. Hirtl
Central Institute for Meteorology and Geodynamics, Vienna, Austria
R. Žabkar
Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
G. R. Carmichael
Center for Global and Regional Environmental Research, University of Iowa, Iowa City, USA
J. Flemming
European Centre for Medium-range Weather Forecasts, Reading, UK
A. Inness
European Centre for Medium-range Weather Forecasts, Reading, UK
M. Pagowski
NOAA/ESRL, Boulder, Colorado, USA
J. L. Pérez Camaño
Technical University of Madrid (UPM), Madrid, Spain
P. E. Saide
Center for Global and Regional Environmental Research, University of Iowa, Iowa City, USA
R. San Jose
Technical University of Madrid (UPM), Madrid, Spain
M. Sofiev
Finnish Meteorological Institute, Helsinki, Finland
Finnish Meteorological Institute, Helsinki, Finland
A. Baklanov
World Meteorological Organization (WMO), Geneva, Switzerland and Danish Meteorological Institute (DMI), Copenhagen, Denmark
C. Carnevale
Department of Mechanical and Industrial Engineering, University of Brescia, Brescia, Italy
G. Grell
NOAA/ESRL, Boulder, Colorado, USA
C. Seigneur
CORRESPONDING AUTHOR
CEREA, Joint Laboratory École des Ponts ParisTech/EDF R&D, Université Paris-Est, Marne-la-Vallée, France
Viewed
Total article views: 8,803 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 20 Dec 2014)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
4,352 | 4,076 | 375 | 8,803 | 183 | 193 |
- HTML: 4,352
- PDF: 4,076
- XML: 375
- Total: 8,803
- BibTeX: 183
- EndNote: 193
Total article views: 7,406 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 May 2015)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
3,679 | 3,510 | 217 | 7,406 | 150 | 163 |
- HTML: 3,679
- PDF: 3,510
- XML: 217
- Total: 7,406
- BibTeX: 150
- EndNote: 163
Total article views: 1,397 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 20 Dec 2014)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
673 | 566 | 158 | 1,397 | 33 | 30 |
- HTML: 673
- PDF: 566
- XML: 158
- Total: 1,397
- BibTeX: 33
- EndNote: 30
Cited
172 citations as recorded by crossref.
- 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
- 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
- Development of the WRF-CO2 4D-Var assimilation system v1.0 T. Zheng et al. 10.5194/gmd-11-1725-2018
- 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
- Multivariate Kalman filtering for spatio-temporal processes G. Ferreira et al. 10.1007/s00477-022-02266-3
- 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
- Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2 S. Silva & A. Arellano 10.3390/rs9070744
- On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- Origin of Fine Particulate Carbon in the Rural United States B. Schichtel et al. 10.1021/acs.est.7b00645
- 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
- Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
- 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
- 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
- Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics R. Ménard & M. Deshaies-Jacques 10.3390/atmos9030086
- 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
- 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
- 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
- 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
- Variational approach to the study of processes of geophysical hydro-thermodynamics with assimilation of observation data V. Penenko et al. 10.1134/S0021894417050029
- 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
- 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
- Scattering and absorbing aerosols in the climate system J. Li et al. 10.1038/s43017-022-00296-7
- 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
- The use of forecast gradients in 3DVar data assimilation Z. Zhu et al. 10.1016/j.apm.2019.04.038
- Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
- 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
- Inverse problems for the study of climatic and ecological processes under anthropogenic influences V. Penenko & E. Tsvetova 10.1088/1755-1315/386/1/012036
- 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
- Inverse Modeling of Formaldehyde Emissions and Assessment of Associated Cumulative Ambient Air Exposures at Fine Scale E. Olaguer 10.3390/atmos14060931
- 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
- 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
- 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
- Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model J. Huang et al. 10.1016/j.srs.2024.100146
- 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
- Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter M. Pang et al. 10.1016/j.atmosenv.2023.119831
- Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
- Local search methods for the solution of implicit inverse problems E. Nino-Ruiz et al. 10.1007/s00500-017-2670-z
- The impact of data assimilation into the meteorological WRF model on birch pollen modelling M. Werner et al. 10.1016/j.scitotenv.2021.151028
- 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
- 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
- Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations D. Alvim et al. 10.3390/rs13112231
- 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
- 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
- The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015 J. Flemming et al. 10.5194/acp-17-1945-2017
- Spatio-temporal analysis with short- and long-memory dependence: a state-space approach G. Ferreira et al. 10.1007/s11749-017-0541-7
- 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
- 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
- A review of numerical models to predict the atmospheric dispersion of radionuclides Á. Leelőssy et al. 10.1016/j.jenvrad.2017.11.009
- 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
- The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) R. Gelaro et al. 10.1175/JCLI-D-16-0758.1
- Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
- 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
- Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. 10.1175/MWR-D-16-0289.1
- 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
- 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
- Advances in air quality research – current and emerging challenges R. Sokhi et al. 10.5194/acp-22-4615-2022
- Digital twins in process engineering: An overview on computational and numerical methods L. Peterson et al. 10.1016/j.compchemeng.2024.108917
- 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
- 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
- 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
- Sequential data assimilation algorithms for air quality monitoring models based on a weak-constraint variational principle A. Penenko et al. 10.1134/S1995423916040054
- 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
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
- 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
- Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations Z. Peng et al. 10.1029/2020GL089030
- Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
- Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
- 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
- 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
- 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
- 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
- Variational data assimilation for the optimized ozone initial state and the short-time forecasting S. Park et al. 10.5194/acp-16-3631-2016
- On the limit to the accuracy of regional-scale air quality models S. Rao et al. 10.5194/acp-20-1627-2020
- 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
- Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method X. Zhang et al. 10.1029/2020JD033599
- 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
- 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
- Subregional inversion of North African dust sources J. Escribano et al. 10.1002/2016JD025020
- 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
- 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
- Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. 10.3390/atmos10120798
- Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. 10.3402/tellusb.v68.29812
- 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
- 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
- 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
- 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
- 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
- Thoughts on Earth System Modeling: From global to regional scale E. Canepa & P. Builtjes 10.1016/j.earscirev.2017.06.017
- A review on methodology in O3-NOx-VOC sensitivity study C. Liu & K. Shi 10.1016/j.envpol.2021.118249
- 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
- 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
- Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis K. Miyazaki et al. 10.5194/acp-20-931-2020
- 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
- Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method K. Chan et al. 10.3390/rs13050969
- Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. 10.1021/acs.est.0c01680
- 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
- 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
- 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
- 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
- Application of Data Fusion Techniques to Improve Air Quality Forecast: A Case Study in the Northern Italy C. Carnevale et al. 10.3390/atmos11030244
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Enhanced parallelization of the incremental 4D‐Var data assimilation algorithm using the Randomized Incremental Optimal Technique N. Bousserez et al. 10.1002/qj.3740
- Use of Satellite Data for Air Pollution Modeling in Bulgaria E. Georgieva et al. 10.3390/earth2030034
- Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network Y. Lops et al. 10.1007/s00521-023-08558-1
- 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
- Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders I. De Feis et al. 10.3390/s20082352
- Variational methods for targeted monitoring of atmospheric quality by specified cost criteria V. Penenko 10.1088/1755-1315/211/1/012048
- Influence of Northeast Monsoon cold surges on air quality in Southeast Asia M. Ashfold et al. 10.1016/j.atmosenv.2017.07.047
- 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
- 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
- Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data S. Zhu et al. 10.1139/er-2022-0125
- 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
- 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
- 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
- A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al. 10.1155/2021/8970949
- 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
- Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees P. Ivatt & M. Evans 10.5194/acp-20-8063-2020
- 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
- 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
- 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
- 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
- Advances in air quality modeling and forecasting A. Baklanov & Y. Zhang 10.1016/j.glt.2020.11.001
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- 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
- 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
- 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
- Consistent Numerical Schemes for Solving Nonlinear Inverse Source Problems with Gradient-Type Algorithms and Newton–Kantorovich Methods A. Penenko 10.1134/S1995423918010081
- 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
- Spatial boundaries of Aerosol Robotic Network observations over the Mediterranean basin A. Mishra et al. 10.1002/2015GL067630
- 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
- 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
- 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
- Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula Y. Cho et al. 10.1007/s13351-024-3152-8
- Source reconstruction of airborne toxics based on acute health effects information C. Argyropoulos et al. 10.1038/s41598-018-23767-8
- Diurnal patterns in ambient PM2.5 exposure over India using MERRA-2 reanalysis data K. Bali et al. 10.1016/j.atmosenv.2020.118180
- 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
- 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
- Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
- 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
- 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
- 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
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
- 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
- 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
- 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
- Digital Twins in Process Engineering: An Overview on Computational and Numerical Methods L. Peterson et al. 10.2139/ssrn.4747265
- 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
- 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
- EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06) S. Skachko et al. 10.5194/gmd-9-2893-2016
- 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
- A variational approach to environmental and climatic problems of urban agglomerations V. Penenko & E. Tsvetova 10.1088/1755-1315/48/1/012020
- Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations F. Lemmouchi et al. 10.3390/rs15061510
- 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
- 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
- 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
- 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
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
- Application of Ensemble Kalman Smoothing in Inverse Modeling of Advection and Diffusion E. Klimova 10.1134/S1995423924030030
- The air quality forecast rote: Recent changes and future challenges W. Ryan 10.1080/10962247.2016.1151469
- Numerical Investigation of the Direct Variational Algorithm of Data Assimilation in the Urban Scenario A. Penenko et al. 10.1134/S102485601806012X
- 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
- European pollen reanalysis, 1980–2022, for alder, birch, and olive M. Sofiev et al. 10.1038/s41597-024-03686-2
- Algorithms for the inverse modelling of transport and transformation of atmospheric pollutants A. Penenko 10.1088/1755-1315/211/1/012052
- A Newton–Kantorovich Method in Inverse Source Problems for Production-Destruction Models with Time Series-Type Measurement Data A. Penenko 10.1134/S1995423919010051
165 citations as recorded by crossref.
- 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
- 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
- Development of the WRF-CO2 4D-Var assimilation system v1.0 T. Zheng et al. 10.5194/gmd-11-1725-2018
- 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
- Multivariate Kalman filtering for spatio-temporal processes G. Ferreira et al. 10.1007/s00477-022-02266-3
- 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
- Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2 S. Silva & A. Arellano 10.3390/rs9070744
- On possibilities of assimilation of near-real-time pollen data by atmospheric composition models M. Sofiev 10.1007/s10453-019-09583-1
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- Origin of Fine Particulate Carbon in the Rural United States B. Schichtel et al. 10.1021/acs.est.7b00645
- 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
- Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
- 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
- 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
- Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics R. Ménard & M. Deshaies-Jacques 10.3390/atmos9030086
- 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
- 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
- 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
- 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
- Variational approach to the study of processes of geophysical hydro-thermodynamics with assimilation of observation data V. Penenko et al. 10.1134/S0021894417050029
- 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
- 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
- Scattering and absorbing aerosols in the climate system J. Li et al. 10.1038/s43017-022-00296-7
- 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
- The use of forecast gradients in 3DVar data assimilation Z. Zhu et al. 10.1016/j.apm.2019.04.038
- Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation S. Park et al. 10.3390/atmos13020160
- 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
- Inverse problems for the study of climatic and ecological processes under anthropogenic influences V. Penenko & E. Tsvetova 10.1088/1755-1315/386/1/012036
- 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
- Inverse Modeling of Formaldehyde Emissions and Assessment of Associated Cumulative Ambient Air Exposures at Fine Scale E. Olaguer 10.3390/atmos14060931
- 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
- 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
- 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
- Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model J. Huang et al. 10.1016/j.srs.2024.100146
- 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
- Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter M. Pang et al. 10.1016/j.atmosenv.2023.119831
- Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
- Local search methods for the solution of implicit inverse problems E. Nino-Ruiz et al. 10.1007/s00500-017-2670-z
- The impact of data assimilation into the meteorological WRF model on birch pollen modelling M. Werner et al. 10.1016/j.scitotenv.2021.151028
- 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
- 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
- Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations D. Alvim et al. 10.3390/rs13112231
- 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
- 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
- The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015 J. Flemming et al. 10.5194/acp-17-1945-2017
- Spatio-temporal analysis with short- and long-memory dependence: a state-space approach G. Ferreira et al. 10.1007/s11749-017-0541-7
- 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
- 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
- A review of numerical models to predict the atmospheric dispersion of radionuclides Á. Leelőssy et al. 10.1016/j.jenvrad.2017.11.009
- 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
- The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) R. Gelaro et al. 10.1175/JCLI-D-16-0758.1
- Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
- 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
- Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches S. Lu et al. 10.1175/MWR-D-16-0289.1
- 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
- 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
- Advances in air quality research – current and emerging challenges R. Sokhi et al. 10.5194/acp-22-4615-2022
- Digital twins in process engineering: An overview on computational and numerical methods L. Peterson et al. 10.1016/j.compchemeng.2024.108917
- 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
- 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
- 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
- Sequential data assimilation algorithms for air quality monitoring models based on a weak-constraint variational principle A. Penenko et al. 10.1134/S1995423916040054
- 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
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
- 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
- Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations Z. Peng et al. 10.1029/2020GL089030
- Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
- Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
- 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
- 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
- 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
- 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
- Variational data assimilation for the optimized ozone initial state and the short-time forecasting S. Park et al. 10.5194/acp-16-3631-2016
- On the limit to the accuracy of regional-scale air quality models S. Rao et al. 10.5194/acp-20-1627-2020
- 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
- Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method X. Zhang et al. 10.1029/2020JD033599
- 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
- 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
- Subregional inversion of North African dust sources J. Escribano et al. 10.1002/2016JD025020
- 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
- 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
- Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. 10.3390/atmos10120798
- Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. 10.3402/tellusb.v68.29812
- 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
- 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
- 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
- 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
- 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
- Thoughts on Earth System Modeling: From global to regional scale E. Canepa & P. Builtjes 10.1016/j.earscirev.2017.06.017
- A review on methodology in O3-NOx-VOC sensitivity study C. Liu & K. Shi 10.1016/j.envpol.2021.118249
- 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
- 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
- Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis K. Miyazaki et al. 10.5194/acp-20-931-2020
- 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
- Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method K. Chan et al. 10.3390/rs13050969
- Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. 10.1021/acs.est.0c01680
- 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
- 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
- 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
- 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
- Application of Data Fusion Techniques to Improve Air Quality Forecast: A Case Study in the Northern Italy C. Carnevale et al. 10.3390/atmos11030244
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Enhanced parallelization of the incremental 4D‐Var data assimilation algorithm using the Randomized Incremental Optimal Technique N. Bousserez et al. 10.1002/qj.3740
- Use of Satellite Data for Air Pollution Modeling in Bulgaria E. Georgieva et al. 10.3390/earth2030034
- Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network Y. Lops et al. 10.1007/s00521-023-08558-1
- 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
- Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders I. De Feis et al. 10.3390/s20082352
- Variational methods for targeted monitoring of atmospheric quality by specified cost criteria V. Penenko 10.1088/1755-1315/211/1/012048
- Influence of Northeast Monsoon cold surges on air quality in Southeast Asia M. Ashfold et al. 10.1016/j.atmosenv.2017.07.047
- 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
- 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
- Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data S. Zhu et al. 10.1139/er-2022-0125
- 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
- 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
- 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
- A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al. 10.1155/2021/8970949
- 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
- Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees P. Ivatt & M. Evans 10.5194/acp-20-8063-2020
- 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
- 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
- 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
- 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
- Advances in air quality modeling and forecasting A. Baklanov & Y. Zhang 10.1016/j.glt.2020.11.001
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- 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
- 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
- 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
- Consistent Numerical Schemes for Solving Nonlinear Inverse Source Problems with Gradient-Type Algorithms and Newton–Kantorovich Methods A. Penenko 10.1134/S1995423918010081
- 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
- Spatial boundaries of Aerosol Robotic Network observations over the Mediterranean basin A. Mishra et al. 10.1002/2015GL067630
- 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
- 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
- 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
- Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula Y. Cho et al. 10.1007/s13351-024-3152-8
- Source reconstruction of airborne toxics based on acute health effects information C. Argyropoulos et al. 10.1038/s41598-018-23767-8
- Diurnal patterns in ambient PM2.5 exposure over India using MERRA-2 reanalysis data K. Bali et al. 10.1016/j.atmosenv.2020.118180
- 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
- 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
- Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
- 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
- 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
- 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
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
- 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
- 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
- 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
- Digital Twins in Process Engineering: An Overview on Computational and Numerical Methods L. Peterson et al. 10.2139/ssrn.4747265
- 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
- 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
- EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06) S. Skachko et al. 10.5194/gmd-9-2893-2016
- 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
- A variational approach to environmental and climatic problems of urban agglomerations V. Penenko & E. Tsvetova 10.1088/1755-1315/48/1/012020
- Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations F. Lemmouchi et al. 10.3390/rs15061510
- 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
- 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
- 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
- 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
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
7 citations as recorded by crossref.
- Application of Ensemble Kalman Smoothing in Inverse Modeling of Advection and Diffusion E. Klimova 10.1134/S1995423924030030
- The air quality forecast rote: Recent changes and future challenges W. Ryan 10.1080/10962247.2016.1151469
- Numerical Investigation of the Direct Variational Algorithm of Data Assimilation in the Urban Scenario A. Penenko et al. 10.1134/S102485601806012X
- 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
- European pollen reanalysis, 1980–2022, for alder, birch, and olive M. Sofiev et al. 10.1038/s41597-024-03686-2
- Algorithms for the inverse modelling of transport and transformation of atmospheric pollutants A. Penenko 10.1088/1755-1315/211/1/012052
- A Newton–Kantorovich Method in Inverse Source Problems for Production-Destruction Models with Time Series-Type Measurement Data A. Penenko 10.1134/S1995423919010051
Saved (final revised paper)
Saved (final revised paper)
Saved (preprint)
Discussed (final revised paper)
Latest update: 23 Nov 2024
Short summary
Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
Data assimilation is used in atmospheric chemistry models to improve air quality forecasts,...
Special issue
Altmetrics
Final-revised paper
Preprint