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Comparison of aerosol properties retrieved using GARRLiC, LIRIC, and Raman algorithms applied to multi-wavelength lidar and sun/sky-photometer data

机译:利用GARRLIC,栅格和拉曼算法检索气溶胶属性的比较应用于多波长激光器和太阳/天空光度计数据

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摘要

Aerosol particles are important and highly variable components of the terrestrial atmosphere, and they affect both air quality and climate. In order to evaluate their multiple impacts, the most important requirement is to precisely measure their characteristics. Remote sensing technologies such as lidar (light detection and ranging) and sun/sky photometers are powerful tools for determining aerosol optical and microphysical properties. In our work, we applied several methods to joint or separate lidar and sun/sky-photometer data to retrieve aerosol properties. The Raman technique and inversion with regularization use only lidar data. The LIRIC (LIdar-Radiometer Inversion Code) and recently developed GARRLiC (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data) inversion methods use joint lidar and sun/sky-photometer data. This paper presents a comparison and discussion of aerosol optical properties (extinction coefficient profiles and lidar ratios) and microphysical properties (volume concentrations, complex refractive index values, and effective radius values) retrieved using the aforementioned methods. The comparison showed inconsistencies in the retrieved lidar ratios. However, other aerosol properties were found to be generally in close agreement with the AERONET (AErosol RObotic NETwork) products. In future studies, more cases should be analysed in order to clearly define the peculiarities in our results.
机译:气溶胶颗粒是陆地气氛的重要和高度可变的组件,它们影响空气质量和气候。为了评估它们的多次影响,最重要的要求是精确测量其特征。 LIDAR(光检测和测距)和SUN / SKY光度计等遥感技术是用于确定气溶胶光学和微物理性质的强大工具。在我们的工作中,我们将几种方法应用于联合或独立的激光乐节和太阳/天空光度计数据来检索气溶胶属性。拉曼技术和正则化的反演仅使用LIDAR数据。栅格(激光辐射计反转码)和最近开发的GARRIC(来自辐射计和LIDAR组合数据的广义气溶胶检索)反转方法使用联合激光乐队和太阳/天空光度计数据。本文介绍了使用上述方法检索的气溶胶光学性质(消光系数谱和激光延伸比)和微神科性质(体积浓度,复数折射率值和有效半径值)的比较和讨论。比较显示了检索到的LIDAR比率的不一致。然而,发现其他气溶胶属性通常与AeroNet(气溶胶机器人网络)产品密切一致。在未来的研究中,应分析更多案例以便在我们的结果中清楚地定义特性。
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