We improved two-wavelength polarization Mie-scattering lidars at several main sites of the Asian dust and aerosol lidar observation network (AD-Net) by adding a nitrogen Raman scatter measurement channel at 607 nm and have conducted ground-based network observation with the improved Mie–Raman lidars (MRL) in East Asia since 2009. This MRL provides 1α+2β+1δ data at nighttime: extinction coefficient (α532), backscatter coefficient (β532), and depolarization ratio (δ532) of particles at 532 nm and an attenuated backscatter coefficient at 1064 nm (βat,1064). Furthermore, we developed a Multi-wavelength Mie-Raman lidar (MMRL) providing 2α+3β+2δ data (α at 355 and 532 nm; β at 355 and 532; βat at 1064 nm; and δ at 355 and 532 nm) and constructed MMRLs at several main sites of the AD-Net. We identified an aerosol-rich layer and height of the planetary boundary layer (PBL) using βat,1064 data, and derived aerosol optical properties (AOPs, for example, αa, βa, δa, and lidar ratio (Sa)). We demonstrated that AOPs cloud be derived with appropriate accuracy. Seasonal means of AOPs in the PBL were evaluated for each MRL observation site using three-year data from 2010 through 2012; the AOPs changed according to each season and region. For example, Sa,532 at Fukue, Japan, were 44±15 sr in winter and 49±17 in summer; those at Seoul, Korea, were 56±18 sr in winter and 62±15 sr in summer. We developed an algorithm to estimate extinction coefficients at 532 nm for black carbon, dust, sea-salt, and air-pollution aerosols consisting of a mixture of sulfate, nitrate, and organic-carbon substances using the 1α532+2β532 and 1064+1δ532 data. With this method, we assume an external mixture of aerosol components and prescribe their size distributions, refractive indexes, and particle shapes. We applied the algorithm to the observed data to demonstrate the performance of the algorithm and determined the vertical structure for each aerosol component.
|ジャーナル||Journal of Quantitative Spectroscopy and Radiative Transfer|
|出版ステータス||出版済み - 2 1 2017|
All Science Journal Classification (ASJC) codes