Observations of extinction-to-backscatter ratio and depolarization ratio of tropospheric aerosols and clouds by high-spectral-resolution lidar

Author(s):  
B. Tatarov ◽  
N. Sugimoto ◽  
I. Matsui ◽  
A. Shimizu
2015 ◽  
Vol 15 (23) ◽  
pp. 13453-13473 ◽  
Author(s):  
S. P. Burton ◽  
J. W. Hair ◽  
M. Kahnert ◽  
R. A. Ferrare ◽  
C. A. Hostetler ◽  
...  

Abstract. Linear particle depolarization ratio is presented for three case studies from the NASA Langley airborne High Spectral Resolution Lidar-2 HSRL-2). Particle depolarization ratio from lidar is an indicator of non-spherical particles and is sensitive to the fraction of non-spherical particles and their size. The HSRL-2 instrument measures depolarization at three wavelengths: 355, 532, and 1064 nm. The three measurement cases presented here include two cases of dust-dominated aerosol and one case of smoke aerosol. These cases have partial analogs in earlier HSRL-1 depolarization measurements at 532 and 1064 nm and in literature, but the availability of three wavelengths gives additional insight into different scenarios for non-spherical particles in the atmosphere. A case of transported Saharan dust has a spectral dependence with a peak of 0.30 at 532 nm with smaller particle depolarization ratios of 0.27 and 0.25 at 1064 and 355 nm, respectively. A case of aerosol containing locally generated wind-blown North American dust has a maximum of 0.38 at 1064 nm, decreasing to 0.37 and 0.24 at 532 and 355 nm, respectively. The cause of the maximum at 1064 nm is inferred to be very large particles that have not settled out of the dust layer. The smoke layer has the opposite spectral dependence, with the peak of 0.24 at 355 nm, decreasing to 0.09 and 0.02 at 532 and 1064 nm, respectively. The depolarization in the smoke case may be explained by the presence of coated soot aggregates. We note that in these specific case studies, the linear particle depolarization ratio for smoke and dust-dominated aerosol are more similar at 355 nm than at 532 nm, having possible implications for using the particle depolarization ratio at a single wavelength for aerosol typing.


2015 ◽  
Vol 15 (17) ◽  
pp. 24751-24803
Author(s):  
S. P. Burton ◽  
J. W. Hair ◽  
M. Kahnert ◽  
R. A. Ferrare ◽  
C. A. Hostetler ◽  
...  

Abstract. Particle depolarization ratio is presented for three case studies from the NASA Langley airborne High Spectral Resolution Lidar-2 (HSRL-2). Particle depolarization ratio from lidar is an indicator of non-spherical particles and is sensitive to the fraction of non-spherical particles and their size. The HSRL-2 instrument measures depolarization at three wavelengths: 355, 532, and 1064 nm. The three measurement cases presented here include two cases of dust aerosol and one case of smoke aerosol. These cases have partial analogs in earlier HSRL-1 depolarization measurements at 532 and 1064 nm and in literature, but the availability of three wavelengths gives additional insight into different scenarios for non-spherical particles in the atmosphere. A case of transported Saharan dust has a spectral dependence with a peak of 0.30 at 532 nm with smaller particle depolarization ratios of 0.27 and 0.25 at 1064 and 355 nm, respectively. A case of locally generated wind-blown North American dust has a maximum of 0.38 at 1064 nm, decreasing to 0.37 and 0.24 at 532 and 355 nm, respectively. The cause of the maximum at 1064 nm is inferred to be very large particles that have not settled out of the dust layer. The smoke layer has the opposite spectral dependence, with the peak of 0.24 at 355 nm, decreasing to 0.09 and 0.02 at 532 and 1064 nm. The depolarization in the smoke case is inferred to be due to the presence of coated soot aggregates. We also point out implications for the upcoming EarthCARE satellite, which will measure particle depolarization ratio only at 355 nm. At 355 nm, the particle depolarization ratios for all three of our case studies are very similar, indicating that smoke and dust may be more difficult to separate with EarthCARE measurements than heretofore supposed.


2016 ◽  
Author(s):  
Dong Liu ◽  
Zhongtao Cheng ◽  
Jing Luo ◽  
Yongying Yang ◽  
Yupeng Zhang ◽  
...  

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