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Two Practical Methods to Retrieve Aerosol Optical Properties from Coherent Doppler Lidar.
- Source :
-
Remote Sensing . Jun2022, Vol. 14 Issue 11, p2700-2700. 17p. - Publication Year :
- 2022
-
Abstract
- Complexly distributed aerosol particles have significant impacts on climate and environmental changes. As one of the vital atmospheric power sources, the wind field deeply affects the distribution and transport of aerosol particles. For a more comprehensive investigation of the aerosols flux and transport mechanism, two retrieval methods of aerosol optical properties (backscatter coefficient and extinction coefficient at 1550 nm) from coherent Doppler lidar (CDL) observation are proposed in this paper. The first method utilizes the calculated aerosol backscatter coefficient (532 nm) from Mie-scattering lidar datasets and the iterative Fernald method to retrieve aerosol optical property profiles during joint measurements with CDL and Mie-scattering lidar. After verifying the correctness of the first method compared with AERONET datasets, we proposed the second retrieval method. Using the forward integral Fernald method with near-ground reference aerosol extinction coefficient calculated by atmospheric visibility, aerosol optical properties at 1550 nm could be obtained. Thirty-six-day joint measurements with two lidars were specially designed and conducted to verify the correctness of these retrieval methods. The validation results of these two methods indicate great performances, where the mean relative errors are 0.0272 and 0.1656, and the correlation coefficients are 0.9306 and 0.9197, respectively. In conclusion, the feasibility of these two retrieval methods extends the capability of CDL to detect aerosol optical properties and also provides a possibility to observe the aerosol distribution and transport process comprehensively, which is a great promotion of aerosol transport studies development. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 14
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 157369084
- Full Text :
- https://doi.org/10.3390/rs14112700