4.3 Article

Retrievals of sulfur dioxide from the Global Ozone Monitoring Experiment 2 (GOME-2) using an optimal estimation approach: Algorithm and initial validation

期刊

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1029/2011JD015808

关键词

-

资金

  1. NASA
  2. Smithsonian Institution
  3. NIMR/KMA

向作者/读者索取更多资源

We apply an optimal estimation algorithm originally developed for retrieving ozone profiles from the Global Ozone Monitoring Experiment (GOME) and the Ozone Monitoring Instrument (OMI) to make global observations of sulfur dioxide from the Global Ozone Monitoring Experiment 2 (GOME-2) on the MetOp-A satellite. Our approach combines a full radiative transfer calculation, retrieval algorithm, and trace gas climatologies to implicitly include the effects of albedo, clouds, ozone, and SO2 profiles in the retrieval. Under volcanic conditions, the algorithm may also be used to directly retrieve SO2 plume altitude. Retrieved SO2 columns over heavy anthropogenic pollution typically agree with those calculated using a two-step slant column and air mass factor approach to within 10%. Retrieval uncertainties are quantified for GOME-2 SO2 amounts; these are dominated by uncertainty contributions from noise, surface albedo, profile shape, correlations with other retrieved parameters, atmospheric temperature, choice of wavelength fitting window, and aerosols. When plume altitudes are also simultaneously retrieved, additional significant uncertainties result from uncertainties in the a priori altitude, the model's vertical layer resolution, and instrument calibration. Retrieved plume height information content is examined using the Mount Kasatochi volcanic plume on 9 August 2008. An a priori altitude of 10 km and uncertainty of 2 km produce degrees of freedom for signal of at least 0.9 for columns >30 Dobson units. GOME-2 estimates of surface SO2 are compared with in situ annual means over North America in 2008 from the Clear Air Status and Trends Network (CASTNET; r = 0.85, N = 65) and Air Quality System (AQS) and National Air Pollution Surveillance (NAPS; r = 0.40, N = 438) networks.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据