Interpreting aerosol lidar profiles to better estimate surface PM2.5 for columnar AOD measurements
Interpreting aerosol lidar profiles to better estimate surface PM2.5 for columnar AOD measurements
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DOI：
10.1016/j.atmosenv.2013.06.031
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发表时间：
2013-11-01
影响因子：
5
通讯作者：
Lin, Neng-Hui
中科院分区：
文献类型：
--
作者：
Chu, D. Allen;Tsai, Tzu-Chin;Lin, Neng-Hui
卫星气溶胶光学厚度（AOD）产品已被用于估计世界不同地区的表面PM2.5。然而，有些研究显示，气溶胶光学厚度与PM2.5之间的关系良好，但有些关系相对较差。越来越多的基于激光雷达的气溶胶消光剖面提供了对边界层以及其上残差的深入了解。在这里，我们报告了一项在台湾使用四年（2006-2009）MPLNet数据来表征气溶胶垂直分布的研究。我们从MPLNet气溶胶消光廓线中推导出霾层高度（HLH），并通过平均PBL消光（MPE）和近地面消光（NSE）对廓线差异进行分类。前者代表边界层内的平均消光，后者代表最接近地面的消光。MPE与NSE的比较导致三种不同的气溶胶分布图分类，以帮助解释AOD和PM2.5之间的关系。HLH对AODAERONET归一化的近似值与MPE的PM2.5相关性非常接近（季节相关>= 0.8，廓线分类相关>= 0.85）。AODmonis/HLH的相关性系统地低于AODAERONET/HLH的相关性。PM2.5值总体上更好地估计轮廓分类比那些来自季节。PM2.5的更好的性能是通过近似获得的（即，用HLH标准化AOD）比仅用AOD。用于量化关系的性能指标显示，与仅使用ADD相比，AODAERoNET/HLH和AODmonis/HLH的不确定性分别提高了2.9 μ g m（-3）（或20%）和2.3 μ g m（-3）（或15%）。2013爱思唯尔有限公司保留所有权利。
Satellite aerosol optical depth (AOD) products have been used to estimate surface PM2.5 in different parts of the world. However, some revealed good but some relatively poorer relationship between AOD and PM2.5. The increasingly available lidar-based aerosol extinction profiles provide insights into the boundary layer as well as residual above it. Here we report a study in Taiwan using four-year (2006-2009) MPLNet data to characterize aerosol vertical distribution. We derived haze layer height (HLH) from MPLNet aerosol extinction profiles and classified profile differences by mean PBL extinction (MPE) and near-surface extinction (NSE). The former represents the mean extinction within boundary layer and the latter the closest extinction to surface. The comparison of MPE versus NSE leads to three distinct classifications of aerosol profiles to help interpret the relationship between AOD and PM2.5. The approximation of normalizing AOD AERONET by HLH closely follows MPE in correlating with PM2.5 (>= 0.8 with respect to season or >= 0.85 with respect to profile classification). The correlation resulted from AODmonis/HLH is systematically lower than that derived by AODAERONET/HLH. PM2.5 values are overall better estimated by profile classification than those derived by season. Better performance of PM2.5 is obtained with the approximation (i.e., normalizing AOD by HLH) than that using AOD only. The performance metrics used in quantifying the relationship reveal improvements in uncertainty by 2.9 mu g m(-3) (or 20%) with AODAERoNET/HLH and 2.3 mu g m(-3) (or 15%) with AODmonis/HLH in comparison to using ADD only. 2013 Elsevier Ltd. All rights reserved.