物理学报2016，Vol.65Issue(8)：084204-1-084204-17,17.DOI:10.7498/aps.65.084204
大气SO2柱总量遥感反演算法比较分析及验证∗
Comparison and validation of band residual difference algorithm and principal comp onent analysis algorithm for retrievals of atmospheric SO2 columns from satellite observations
摘要
Abstract
Remote sensing technology provides an unprecedented tool for the continuous and real-time monitoring of atmo-spheric SO2 from volcanic eruption and anthropogenic emission. The Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), and Ozone Monitoring Instrument (OMI) have high SO2 monitoring capability. The OMI, which was launched on the EOS/Aura platform in July 2004, has the same hyperspectral measurements as the GOME and SCIAMACHY, but offers the improved spatial resolution at nadir (13 × 24 km2) and daily global coverage for short-lifetime SO2. For OMI operational SO2 planetary boundary layer (PBL) retrieval, the previous band residual difference (BRD) algorithm has been replaced by principal component analysis (PCA) algorithm, which effectively reduces the systematic biases in SO2 column retrievals. However, there are few studies on the evaluations and validations of PCA SO2 retrievals over China, and the long-term comparisons with BRD SO2 retrievals also need to be conducted. In this study, the accuracies of PCA and BRD SO2 retrievals are validated by using ground-based multi axis differential optical absorption spectroscopy (MAX-DOAS) located in Beijing, and regional atmospheric modeling system, community multi-scale air quality (RAMS-CMAQ) modeling system model which can simulate the vertical distribution of atmospheric SO2. Moreover, BRD and PCA SO2 retrievals from oceanic area, eastern China and Reunion volcanic eruption are compared to find the long-term trend and spatiotemporal dif-ferences between SO2 columns. Finally, the uncertainty of SO2 retrieval, caused by measurement errors, band selection and input parameter errors in radiative transfer model, are analysed to understand the limitations of BRD and PCA algorithms. Results show that both PCA and BRD SO2 retrievals over Beijing are lower than ground-based MAX-DOAS mea-surements of SO2. PCA and BRD SO2 retrievals over eastern China are lower than the simulated SO2 columns fromRAMS-CMAQ in winter 2008, but in July and August BRD SO2 columns are higher than RAMS-CMAQ simulations. The values of SO2 columns from BRD over China are more consistent with those from ground-based MAX-DOAS and RAMS-CMAQ model than from PCA. Although PCA algorithm effectively reduces the noise in SO2 column retrieval, SO2 columns from PCA over China are lower than those from BRD. For oceanic area where SO2 amount is nearly zero, the standard deviation of PCA results is lower than that of BRD, but the absolute value of averaged PCA SO2 column is larger than that of BRD. In the case of Reunion volcanic eruption with SO2 columns larger than 25 DU, the BRD SO2 columns are lower than PCA retrievals. Meanwhile, with the increase of SO2 column, the difference between BRD and PCA SO2 retrievals increases. Detailed uncertainty analysis shows the influences of measurement errors, band selection and inputs of radiative transfer model on the retrieval results. This study is important for developing the retrieval algorithm, and can also improve the application of OMI SO2 products.关键词
污染气体SO2/卫星遥感反演算法/比较验证/不确定性分析Key words
trace gas SO2/satellite remote sensing/comparison and validation/uncentainty analysis引用本文复制引用
闫欢欢,李晓静,张兴赢,王维和,陈良富,张美根,徐晋..大气SO2柱总量遥感反演算法比较分析及验证∗[J].物理学报,2016,65(8):084204-1-084204-17,17.基金项目
国家卫星气象中心青年人才基金项目、高分辨率对地观测系统重大专项气象应用示范项目(批准号：E310/1112)、公益气象行业专项(批准号：GYHY201106045)、欧盟FP7框架国际合作项目(批准号:606719)和国家自然科学基金(批准号:41501413)资助的课题.* Project supported by the Young Science Fund from National Satellite Meteorological Center, High-resolution Earth Ob-servation System, China (Grant No. E310/1112), the Special Scientific Research Fund of Meteorological Public Welfare Profession of China (Grant No. GYHY201106045), Partnership with China on Space Data (PANDA)(Grant No.606719), National Natural Science Foundation of China (Grant No.41501413) （批准号：E310/1112）
