Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3439-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3439-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Jul 2022
Data description paper |
| 29 Jul 2022
| 29 Jul 2022
Climatology of aerosol component concentrations derived from multi-angular polarimetric POLDER-3 observations using GRASP algorithm
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yevgeny Derimian
Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique
Atmosphérique, 59000 Lille, France
Cheng Chen
Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique
Atmosphérique, 59000 Lille, France
GRASP-SAS, Villeneuve d'Ascq, France
Xindan Zhang
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Gregory L. Schuster
NASA Langley Research Center, Hampton, VA, USA
David Fuertes
GRASP-SAS, Villeneuve d'Ascq, France
Pavel Litvinov
GRASP-SAS, Villeneuve d'Ascq, France
Tatyana Lapyonok
Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique
Atmosphérique, 59000 Lille, France
Anton Lopatin
GRASP-SAS, Villeneuve d'Ascq, France
Christian Matar
GRASP-SAS, Villeneuve d'Ascq, France
Fabrice Ducos
Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique
Atmosphérique, 59000 Lille, France
Yana Karol
GRASP-SAS, Villeneuve d'Ascq, France
Benjamin Torres
Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique
Atmosphérique, 59000 Lille, France
Ke Gui
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yu Zheng
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yuanxin Liang
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yadong Lei
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Jibiao Zhu
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Lei Zhang
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Junting Zhong
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Xiaoye Zhang
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique
Atmosphérique, 59000 Lille, France
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16 citations as recorded by crossref.
- Changes in aerosol loading before, during and after the COVID-19 pandemic outbreak in China: Effects of anthropogenic and natural aerosol Y. Liang et al. 10.1016/j.scitotenv.2022.159435
- Recent Progress in Atmospheric Chemistry Research in China: Establishing a Theoretical Framework for the “Air Pollution Complex” T. Zhu et al. 10.1007/s00376-023-2379-0
- A robust and flexible satellite aerosol retrieval algorithm for multi-angle polarimetric measurements with physics-informed deep learning method M. Tao et al. 10.1016/j.rse.2023.113763
- Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols O. Hasekamp et al. 10.5194/amt-17-1497-2024
- FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG H. Ding et al. 10.3390/rs14215591
- Preliminary Assessment and Verification of the Langley Plots Calibration of the Sun Photometer at Mt Foyeding Observatory, Beijing Y. Zheng et al. 10.3390/rs14174321
- Light-absorbing properties of polar- and non-polar brown carbon fractions of aerosols in Delhi A. Alang et al. 10.1016/j.atmosres.2023.107073
- Aerosol optical and radiative properties and their environmental effects in China: A review H. Che et al. 10.1016/j.earscirev.2023.104634
- Influence of aerosol properties and surface albedo on radiative forcing efficiency of key aerosol types using global AERONET data A. Chen et al. 10.1016/j.atmosres.2022.106519
- An AERONET-based methodology to retrieve black carbon light absorption and comparison with MERRA-2 data N. Dehkhoda et al. 10.1016/j.apr.2023.101994
- Climatology of Dust Aerosols over the Jianghan Plain Revealed with Space-Borne Instruments and MERRA-2 Reanalysis Data during 2006–2021 C. Liu et al. 10.3390/rs14174414
- Climatology and interannual variability of type-dependent aerosol optical depth and vertical distribution over southwest China and northern India from multiple satellite and aerosol reanalysis datasets H. Cai et al. 10.1016/j.atmosenv.2022.119528
- Features and sources of aerosol properties over the western Pacific Ocean based on shipborne measurements W. Wang et al. 10.1007/s00703-023-00960-7
- Long-term characteristics of dust aerosols over central China from 2010 to 2020 observed with polarization lidar D. Jing et al. 10.1016/j.atmosres.2023.107129
- Columnar optical-radiative properties and components of aerosols in the Arctic summer from long-term AERONET measurements Y. Liang et al. 10.1016/j.scitotenv.2023.169052
- Modeling atmospheric brown carbon in the GISS ModelE Earth system model M. DeLessio et al. 10.5194/acp-24-6275-2024
Latest update: 29 Jun 2024
Short summary
A climatology of aerosol composition concentration derived from POLDER-3 observations using GRASP/Component is presented. The conceptual specifics of the GRASP/Component approach are in the direct retrieval of aerosol speciation without intermediate retrievals of aerosol optical characteristics. The dataset of satellite-derived components represents scarce but imperative information for validation and potential adjustment of chemical transport models.
A climatology of aerosol composition concentration derived from POLDER-3 observations using...
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