Articles | Volume 13, issue 1
https://doi.org/10.5194/acp-13-269-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-13-269-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Jan 2013
Research article |
| 11 Jan 2013
Assimilation of ground versus lidar observations for PM10 forecasting
Y. Wang
LSCE, joint laboratory CEA-CNRS, UMR8212, 91191 Gif-sur-Yvette, France
CEREA, joint laboratory Ecole des Ponts ParisTech - EDF R&D, Université Paris-Est, 77455 Champs sur Marne, France
K. N. Sartelet
CEREA, joint laboratory Ecole des Ponts ParisTech - EDF R&D, Université Paris-Est, 77455 Champs sur Marne, France
M. Bocquet
INRIA, Paris-Rocquencourt Research Center, Le Chesnay, France
CEREA, joint laboratory Ecole des Ponts ParisTech - EDF R&D, Université Paris-Est, 77455 Champs sur Marne, France
P. Chazette
LSCE, joint laboratory CEA-CNRS, UMR8212, 91191 Gif-sur-Yvette, France
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Cited
30 citations as recorded by crossref.
- Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011 Y. Tang et al. 10.1080/10962247.2015.1062439
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- Remote sensing of two exceptional winter aerosol pollution events and representativeness of ground-based measurements A. Baron et al. 10.5194/acp-20-6749-2020
- Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign Y. Wang et al. 10.5194/acp-14-3511-2014
- Three-dimensional variational assimilation of Lidar extinction profiles: Application to PM2.5 prediction in north China L. Gao et al. 10.1016/j.atmosenv.2021.118828
- The characterization of Taklamakan dust properties using a multiwavelength Raman polarization lidar in Kashi, China Q. Hu et al. 10.5194/acp-20-13817-2020
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- The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign J. Jung et al. 10.1029/2019JD030641
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- Background error statistics for aerosol variables from WRF/Chem predictions in Southern California Z. Zang et al. 10.1007/s13143-015-0063-8
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- Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. 10.1016/j.jes.2022.04.012
- 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
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- 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
- Water vapor Raman lidar observations from multiple sites in the framework of WaLiNeAs F. Laly et al. 10.5194/essd-16-5579-2024
- 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
- Information constraints in variational data assimilation M. Kahnert 10.1002/qj.3347
30 citations as recorded by crossref.
- Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011 Y. Tang et al. 10.1080/10962247.2015.1062439
- Transport of aerosols over the French Riviera – link between ground-based lidar and spaceborne observations P. Chazette et al. 10.5194/acp-19-3885-2019
- Remote sensing of two exceptional winter aerosol pollution events and representativeness of ground-based measurements A. Baron et al. 10.5194/acp-20-6749-2020
- Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign Y. Wang et al. 10.5194/acp-14-3511-2014
- Three-dimensional variational assimilation of Lidar extinction profiles: Application to PM2.5 prediction in north China L. Gao et al. 10.1016/j.atmosenv.2021.118828
- The characterization of Taklamakan dust properties using a multiwavelength Raman polarization lidar in Kashi, China Q. Hu et al. 10.5194/acp-20-13817-2020
- 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
- The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign J. Jung et al. 10.1029/2019JD030641
- Near-surface and columnar measurements with a micro pulse lidar of atmospheric pollen in Barcelona, Spain M. Sicard et al. 10.5194/acp-16-6805-2016
- Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al. 10.5194/acp-14-12031-2014
- Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM2.5 forecasts across China Y. Liang et al. 10.5194/gmd-13-6285-2020
- Characterizing vertical distribution patterns of PM2.5 in low troposphere of Shanghai city, China: Implications from the perspective of unmanned aerial vehicle observations R. Song et al. 10.1016/j.atmosenv.2021.118724
- 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
- Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. 10.3402/tellusb.v68.29812
- Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China H. Ye et al. 10.1016/j.atmosres.2022.106069
- CALIOP near-real-time backscatter products compared to EARLINET data T. Grigas et al. 10.5194/acp-15-12179-2015
- Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer Z. Zang et al. 10.1016/j.scitotenv.2016.09.186
- Background error statistics for aerosol variables from WRF/Chem predictions in Southern California Z. Zang et al. 10.1007/s13143-015-0063-8
- 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
- Atmospheric aerosol variability above the Paris Area during the 2015 heat wave - Comparison with the 2003 and 2006 heat waves P. Chazette et al. 10.1016/j.atmosenv.2017.09.055
- Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. 10.1016/j.jes.2022.04.012
- 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
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- 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
- Bayesian inversion of emissions from large urban fire using in situ observations E. Launay et al. 10.1016/j.atmosenv.2024.120391
- Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing X. Cheng et al. 10.1016/j.scitotenv.2019.05.186
- 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
- Water vapor Raman lidar observations from multiple sites in the framework of WaLiNeAs F. Laly et al. 10.5194/essd-16-5579-2024
- 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
- Information constraints in variational data assimilation M. Kahnert 10.1002/qj.3347
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