Improved identification of the solution space of aerosol microphysical properties derived from the inversion of profiles of lidar optical data, part 2: simulations with synthetic optical data

Kolgotin Alexei; Mueller Detlef; Chemyakin Eduard; Romanov Anton

摘要

We developed a mathematical scheme that allows us to improve retrieval products obtained from the inversion of multiwavelength Raman/HSRL lidar data, commonly dubbed "3 backscatter + 2 extinction" (3 beta + 2 alpha) lidar. This scheme works independently of the automated inversion method that is currently being developed in the framework of the Aerosol-Cloud-Ecosystem (ACE) mission and which is successfully applied since 2012 [Atmos. Meas. Tech. 7, 3487 (2014); "Comparison of aerosol optical and microphysical retrievals from HSRL-2 and in-situ measurements during DISCOVER-AQ 2013 (California and Texas)," in International Laser Radar Conference, July 2015, paper PS-C1-14] to data collected with the first airborne multiwavelength 3 beta + 2 alpha high spectral resolution lidar (HSRL) developed at NASA Langley Research Center. The mathematical scheme uses gradient correlation relationships we presented in part 1 of our study [Appl. Opt. 55, 9839 (2016)] in which we investigated lidar data products and particle microphysical parameters from one and the same set of optical lidar profiles. For an accurate assessment of regression coefficients that are used in the correlation relationships we specially designed the proximate analysis method that allows us to search for a first-estimate solution space of particle microphysical parameters on the basis of a look-up table. The scheme works for any shape of particle size distribution. Simulation studies demonstrate a significant stabilization of the various solution spaces of the investigated aerosol microphysical data products if we apply this gradient correlation method in our traditional regularization technique. Surface-area concentration can be estimated with an uncertainty that is not worse than the measurement error of the underlying extinction coefficients. The retrieval uncertainty of the effective radius is as large as +/- 0.07 mu m for fine mode particles and approximately 100% for particle size distributions composed of fine (submicron) and coarse (supermicron) mode particles. The volume concentration uncertainty is defined by the sum of the uncertainty of surface-area concentration and the uncertainty of the effective radius. The uncertainty of number concentration is better than 100% for any radius domain between 0.03 and 10 mu m. For monomodal PSDs, the uncertainties of the real and imaginary parts of the CRI can be restricted to +/- 0.1 and +/- 0.01 on the domains [1.3; 1.8] and [0; 0.1], respectively. (C) 2016 Optical Society of America

机译：我们开发了一种数学方案，使我们能够改善从多波长拉曼/ HSRL LIDAR数据的反转获得的检索产品，通常被称为“3反向散射+ 2灭绝”（3 beta + 2 alpha）延迟雷达。该方案独立于自动转换方法，目前正在开发的Aerosol-Cloud-Ecosystem（ACE）任务的框架中，并且自2012年以来成功应用[Atmos。 MEAS。技术。 7,3487（2014）; “在2015年7月7月的国际激光雷达会议中，HSRL-2和原位测量的气溶胶光学和微微药物检索的比较，2015年7月，纸PS-C1-14，与之收集的数据第一个空中多波长3β+ 2 Alpha高光谱分辨率LIDAR（HSRL）在美国宇航局兰利研究中心开发。数学方案使用我们研究第1部分中所呈现的梯度相关关系[应用。选择。 55,9839（2016）]在其中从一个和同一组光学激光雷达轮廓调查了LIDAR数据产品和粒子微物理参数。为了准确评估在相关关系中使用的回归系数，我们特别设计了近似分析方法，该方法允许我们在查找表的基础上搜索粒子微物理参数的第一估计解决方案空间。该方案适用于任何粒度分布的形状。模拟研究表明，如果我们在传统的正则化技术中应用这种梯度相关方法，则表明了研究的气溶胶微物理数据产品的各种溶液空间的显着稳定性。可以用不确定度估计表面积浓度，这些不确定度不比底层消光系数的测量误差更差。有效半径的检索不确定度对于精细模式颗粒和大约100％的粒度分布大约100％，由精细（亚微米）和粗（超微）模式粒子组成。体积浓度不确定度由表面积浓度的不确定度和有效半径的不确定性的总和限定。对于任何半径域的数字浓度的不确定性在0.03和10μm之间的任何半径结构域之间优于100％。对于单阳极PSD，CRI的真实和虚部的不确定性可以限制在域上的+/- 0.1和+/- 0.01 [1.3; 1.8]和[0;分别为0.1]。（c）2016年美国光学学会

Improved identification of the solution space of aerosol microphysical properties derived from the inversion of profiles of lidar optical data, part 2: simulations with synthetic optical data

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