Kolgotin Alexei, Müller Detlef, Chemyakin Eduard, Romanov Anton
Appl Opt. 2016 Dec 1;55(34):9850-9865. doi: 10.1364/AO.55.009850.
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" (3β+2α) 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)10.5194/amt-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 3β+2α 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)APOPAI0003-693510.1364/AO.55.009839] 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 μm 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 μm. 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.
我们开发了一种数学方案，该方案能让我们改进从多波长拉曼/高光谱分辨率拉曼激光雷达（HSRL）数据反演得到的反演产品，这种激光雷达通常被称为“3 次后向散射 + 2 次消光”（3β + 2α）激光雷达。该方案独立于目前在气溶胶 - 云 - 生态系统（ACE）任务框架下正在开发且自2012年以来已成功应用的自动反演方法[《大气测量技术》7, 3487 (2014)10.5194/amt - 7 - 3487 - 2014；“2013年DISCOVER - AQ（加利福尼亚和得克萨斯州）期间HSRL - 2气溶胶光学和微物理反演与现场测量的比较”，发表于2015年7月的国际激光雷达会议，论文PS - C1 - 14]，该方法应用于美国国家航空航天局兰利研究中心研发的首个机载多波长3β + 2α高光谱分辨率激光雷达（HSRL）收集的数据。该数学方案使用了我们在研究的第1部分[《应用光学》55, 9839 (2016)APOPAI0003 - 693510.1364/AO.55.009839]中提出的梯度相关关系，在该部分研究中，我们从同一组光学激光雷达廓线研究了激光雷达数据产品和粒子微物理参数。为了准确评估相关关系中使用的回归系数，我们专门设计了近似分析方法，该方法能让我们基于查找表搜索粒子微物理参数的初始估计解空间。该方案适用于任何形状的粒径分布。模拟研究表明，如果我们在传统正则化技术中应用这种梯度相关方法，所研究的气溶胶微物理数据产品的各种解空间会显著稳定。表面积浓度的估计不确定性不超过基础消光系数的测量误差。对于细模态粒子，有效半径的反演不确定性高达±0.07μm，对于由细（亚微米）和粗（超微米）模态粒子组成的粒径分布，不确定性约为100%。体积浓度不确定性由表面积浓度不确定性和有效半径不确定性之和定义。对于0.03至10μm之间的任何半径范围，数浓度不确定性优于100%。对于单峰粒径分布，复折射指数实部和虚部的不确定性在[1.3; 1.8]和[0; 0.1]范围内可分别限制为±0.1和±0.01。
