22 citations as recorded by crossref.

1. Comparison of IASI water vapor retrieval with H2O-Raman lidar in the framework of the Mediterranean HyMeX and ChArMEx programs P. Chazette et al. https://doi.org/10.5194/acp-14-9583-2014
2. The December 2016 extreme weather and particulate matter pollution episode in the Paris region (France) G. Foret et al. https://doi.org/10.1016/j.atmosenv.2022.119386
3. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models M. Bocquet et al. https://doi.org/10.5194/acp-15-5325-2015
4. Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. https://doi.org/10.3402/tellusb.v68.29812
5. Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts C. Ma et al. https://doi.org/10.1007/s00376-017-7179-y
6. Analysis of a warehouse fire smoke plume over Paris with an N2 Raman lidar and an optical thickness matching algorithm X. Shang et al. https://doi.org/10.5194/amt-11-6525-2018
7. Optical properties of an industrial fire observed with a ground based N2-Raman lidar over the Paris area X. Shang et al. https://doi.org/10.1051/epjconf/201817604006
8. Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. https://doi.org/10.1029/2019JD031465
9. Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al. https://doi.org/10.5194/acp-14-12031-2014
10. Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. https://doi.org/10.1016/j.jes.2022.04.012
11. An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. https://doi.org/10.5194/gmd-15-3555-2022
12. Information constraints in variational data assimilation M. Kahnert https://doi.org/10.1002/qj.3347
13. Parametrization of Horizontal and Vertical Transfers for the Street-Network Model MUNICH Using the CFD Model Code_Saturne A. Maison et al. https://doi.org/10.3390/atmos13040527
14. AEROCOM and AEROSAT AAOD and SSA study – Part 1: Evaluation and intercomparison of satellite measurements N. Schutgens et al. https://doi.org/10.5194/acp-21-6895-2021
15. Simulation of particle diversity and mixing state over Greater Paris: a model–measurement inter-comparison S. Zhu et al. https://doi.org/10.1039/C5FD00175G
16. The Secondary Organic Aerosol Processor (SOAP v1.0) model: a unified model with different ranges of complexity based on the molecular surrogate approach F. Couvidat & K. Sartelet https://doi.org/10.5194/gmd-8-1111-2015
17. 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. https://doi.org/10.5194/gmd-13-6285-2020
18. How much information do extinction and backscattering measurements contain about the chemical composition of atmospheric aerosol? M. Kahnert & E. Andersson https://doi.org/10.5194/acp-17-3423-2017
19. Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: lidar observations L. El Amraoui et al. https://doi.org/10.5194/amt-13-4645-2020
20. A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations D. Wang et al. https://doi.org/10.5194/gmd-15-1821-2022
21. Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing X. Cheng et al. https://doi.org/10.1016/j.scitotenv.2019.05.186
22. Three‐dimensional modeling of the mixing state of particles over Greater Paris S. Zhu et al. https://doi.org/10.1002/2015JD024241