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.
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
28 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 PM<sub>2.5</sub> 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
- 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 PM<sub>2.5</sub> 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
- 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
28 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 PM<sub>2.5</sub> 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
- 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 PM<sub>2.5</sub> 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
- 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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