Li Jing, Li Xichen, Carlson Barbara E, Kahn Ralph A, Lacis Andrew A, Dubovik Oleg, Nakajima Teruyuki
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China.
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.
J Geophys Res Atmos. 2016 Nov 27;121(22):13609-13627. doi: 10.1002/2016jd025469. Epub 2016 Oct 26.
Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)-based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by ~27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.
人们已经设计了各种天基传感器,并开发了相应的算法来反演气溶胶光学厚度(AOD),这是最基本的气溶胶光学特性,但这些不同的卫星数据集之间仍存在相当大的分歧。地面观测旨在为验证卫星数据提供地面真值;因此,其部署地点应尽可能包含尽可能多的空间信息,即高空间代表性。我们使用一种基于新型集合卡尔曼滤波器(EnKF)的方法,客观地评估了当前气溶胶机器人网络(AERONET)站点的空间代表性。将来自中分辨率成像光谱仪、多角度成像光谱仪、海视宽视场传感器、臭氧监测仪以及大气科学反射率的偏振和各向异性与激光雷达观测相结合的多传感器月平均AOD数据集组合成一个包含605个成员的集合,并将AERONET数据视为使用EnKF同化到该集合中的观测值。通过将(受地面测量约束的)分析误差方差与(仅基于卫星数据的)背景误差方差进行比较来进行评估。结果表明,总不确定性平均降低了约27%,在某些地方可能达到50%以上。不确定性降低模式也有明显的季节性模式,对应于季节性变化的气溶胶类型的空间分布,例如北半球春季的沙尘和南半球秋季的生物质燃烧。沙尘和生物质燃烧站点具有最高的空间代表性,农村和海洋站点也可以代表中等空间信息,而城市站点的代表性相对局限。根据不确定性降低情况为每个AERONET站点分配一个从1到3的空间分数,表明其代表性水平。