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GARRLiC and LIRIC: strengths and limitations for the characterization of dust and marine particles along with their mixtures

机译:GARLiC和LIRIC:表征灰尘和海洋颗粒及其混合物的优势和局限性

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The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) and the LIdar-Radiometer Inversion Code (LIRIC) provide the opportunity to study the aerosol vertical distribution by combining ground-based lidar and sun-photometric measurements. Here, we utilize the capabilities of both algorithms for the characterization of Saharan dust and marine particles, along with their mixtures, in the south-eastern Mediterranean during the CHARacterization of Aerosol mixtures of Dust and Marine origin Experiment (CHARADMExp). Three case studies are presented, focusing on dust-dominated, marine-dominated and dust–marine mixing conditions. GARRLiC and LIRIC achieve a satisfactory characterization for the dust-dominated case in terms of particle microphysical properties and concentration profiles. The marine-dominated and the mixture cases are more challenging for both algorithms, although GARRLiC manages to provide more detailed microphysical retrievals compared to AERONET, while LIRIC effectively discriminates dust and marine particles in its concentration profile retrievals. The results are also compared with modelled dust and marine concentration profiles and surface in situ measurements.
机译:辐射计和激光雷达组合数据算法(GARRLiC)和激光雷达辐射计反演代码(LIRIC)的广义气溶胶反演提供了通过结合地面激光雷达和太阳光度法测量来研究气溶胶垂直分布的机会。在这里,我们利用两种算法的功能来表征东南部地中海尘埃和海洋起源的气溶胶混合物的特征(CHARADMExp)中的撒哈拉尘埃和海洋颗粒及其混合物。提出了三个案例研究,重点研究了粉尘为主,海洋为主和粉尘-海洋混合条件。 GARRLiC和LIRIC在颗粒微物理性质和浓度分布方面,对粉尘占主导的情况获得了令人满意的表征。尽管与AERONET相比,GARRLiC设法提供了更详细的微观物理检索,而LIRIC在其浓度分布图检索中有效地区分了灰尘和海洋颗粒,但两种算法都以海洋为主和混合情况更具挑战性。还将结果与建模的尘埃和海洋浓度曲线以及表面原位测量结果进行比较。

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