Articles | Volume 15, issue 14
https://doi.org/10.5194/amt-15-4323-2022
https://doi.org/10.5194/amt-15-4323-2022
Research article
 | 
29 Jul 2022
Research article |  | 29 Jul 2022

Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm

Shikuan Jin, Yingying Ma, Cheng Chen, Oleg Dubovik, Jin Hong, Boming Liu, and Wei Gong

Related authors

Extending the wind profile beyond the surface layer by combining physical and machine learning approaches
Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024,https://doi.org/10.5194/acp-24-4047-2024, 2024
Short summary
A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia
Shikuan Jin, Yingying Ma, Zhongwei Huang, Jianping Huang, Wei Gong, Boming Liu, Weiyan Wang, Ruonan Fan, and Hui Li
Atmos. Chem. Phys., 23, 8187–8210, https://doi.org/10.5194/acp-23-8187-2023,https://doi.org/10.5194/acp-23-8187-2023, 2023
Short summary
Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023,https://doi.org/10.5194/acp-23-3181-2023, 2023
Short summary
Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms
Yingying Ma, Yang Zhu, Boming Liu, Hui Li, Shikuan Jin, Yiqun Zhang, Ruonan Fan, and Wei Gong
Atmos. Chem. Phys., 21, 17003–17016, https://doi.org/10.5194/acp-21-17003-2021,https://doi.org/10.5194/acp-21-17003-2021, 2021
Short summary
Evaluation of retrieval methods for planetary boundary layer height based on radiosonde data
Hui Li, Boming Liu, Xin Ma, Shikuan Jin, Yingying Ma, Yuefeng Zhao, and Wei Gong
Atmos. Meas. Tech., 14, 5977–5986, https://doi.org/10.5194/amt-14-5977-2021,https://doi.org/10.5194/amt-14-5977-2021, 2021
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Estimating hourly ground-level aerosols using Geostationary Environment Monitoring Spectrometer aerosol optical depth: a machine learning approach
Sungmin O, Ji Won Yoon, and Seon Ki Park
Atmos. Meas. Tech., 18, 1471–1484, https://doi.org/10.5194/amt-18-1471-2025,https://doi.org/10.5194/amt-18-1471-2025, 2025
Short summary
Performance and evaluation of remote sensing satellites for monitoring dust weather in East Asia
Yuanyuan Zhang, Ning Wang, and Shuanggen Jin
EGUsphere, https://doi.org/10.5194/egusphere-2025-992,https://doi.org/10.5194/egusphere-2025-992, 2025
Short summary
Aerosol effects on day-ahead solar radiation forecasting
Xinyuan Hou, Kyriakoula Papachristopoulou, and Stelios Kazadzis
EGUsphere, https://doi.org/10.5194/egusphere-2025-891,https://doi.org/10.5194/egusphere-2025-891, 2025
Short summary
Decoupling the PBL Height, the Mixing Layer Height, and the Aerosol Layer Top in LiDAR Measurements over Chiang Mai, Northern Thailand
Ronald Macatangay, Thiranan Sonkaew, Sherin Hassan Bran, Worapop Thongsame, Titaporn Supasri, Mana Panya, Jeerasak Longmali, Raman Solanki, Ben Svasti Thomson, and Achim Haug
EGUsphere, https://doi.org/10.5194/egusphere-2025-630,https://doi.org/10.5194/egusphere-2025-630, 2025
Short summary
A global perspective on CO2 satellite observations in high AOD conditions
Timo H. Virtanen, Anu-Maija Sundström, Elli Suhonen, Antti Lipponen, Antti Arola, Christopher O'Dell, Robert R. Nelson, and Hannakaisa Lindqvist
Atmos. Meas. Tech., 18, 929–952, https://doi.org/10.5194/amt-18-929-2025,https://doi.org/10.5194/amt-18-929-2025, 2025
Short summary

Cited articles

Albrecht, B. A.: AEROSOLS, CLOUD MICROPHYSICS, AND FRACTIONAL CLOUDINESS, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. 
Ångstrom, A.: The Parameter of Atmospheric Turbidity, Tellus, 16, 64–75, https://doi.org/10.3402/tellusa.v16i1.8885, 1964. 
Breon, F. M. and Colzy, S.: Cloud detection from the spaceborne POLDER instrument and validation against surface synoptic observations, J. Appl. Meteorol., 38, 777–785, https://doi.org/10.1175/1520-0450(1999)038<0777:cdftsp>2.0.co;2, 1999. 
Breon, F. M. and Goloub, P.: Cloud droplet effective radius from spaceborne polarization measurements, Geophys. Res. Lett., 25, 1879–1882, https://doi.org/10.1029/98gl01221, 1998. 
Che, H., Yang, L., Liu, C., Xia, X., Wang, Y., Wang, H., Wang, H., Lu, X., and Zhang, X.: Long-term validation of MODIS C6 and C6.1 Dark Target aerosol products over China using CARSNET and AERONET, Chemosphere, 236, 124268, https://doi.org/10.1016/j.chemosphere.2019.06.238, 2019. 
Download
Short summary
Aerosol parameter retrievals have always been a research focus. In this study, we used an advanced aerosol algorithms (GRASP, developed by Oleg Dubovik) to test the ability of DPC/Gaofen-5 (the first polarized multi-angle payload developed in China) images to obtain aerosol parameters. The results show that DPC/GRASP achieves good results (R > 0.9). This research will contribute to the development of hardware and algorithms for aerosols
Share