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首页> 外文期刊>Atmospheric Measurement Techniques >Simulation of the Ozone Monitoring Instrument aerosol index using the NASA Goddard Earth Observing System aerosol reanalysis products
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Simulation of the Ozone Monitoring Instrument aerosol index using the NASA Goddard Earth Observing System aerosol reanalysis products

机译:使用NASA戈达德地球观测系统气溶胶再分析产品对臭氧监测仪气溶胶指数进行模拟

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We provide an analysis of the commonly used Ozone Monitoring Instrument (OMI) aerosol index (AI) product for qualitative detection of the presence and loading of absorbing aerosols. In our analysis, simulated top-of-atmosphere (TOA) radiances are produced at the OMI footprints from a?model atmosphere and aerosol profile provided by the NASA Goddard Earth Observing System (GEOS-5) Modern-Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). Having established the credibility of the MERRAero simulation of the OMI AI in a?previous paper we describe updates in the approach and aerosol optical property assumptions. The OMI TOA radiances are computed in cloud-free conditions from the MERRAero atmospheric state, and the AI is calculated. The simulated TOA radiances are fed to the OMI near-UV aerosol retrieval algorithms (known as OMAERUV) is compared to the MERRAero calculated AI. Two main sources of discrepancy are discussed: one pertaining to the OMI algorithm assumptions of the surface pressure, which are generally different from what the actual surface pressure of an observation is, and the other related to simplifying assumptions in the molecular atmosphere radiative transfer used in the OMI algorithms. Surface pressure assumptions lead to systematic biases in the OMAERUV AI, particularly over the oceans. Simplifications in the molecular radiative transfer lead to biases particularly in regions of topography intermediate to surface pressures of 600 and 1013.25?hPa. Generally, the errors in the OMI AI due to these considerations are less than 0.2 in magnitude, though larger errors are possible, particularly over land. We recommend that future versions of the OMI algorithms use surface pressures from readily available atmospheric analyses combined with high-spatial-resolution topographic maps and include more surface pressure nodal points in their radiative transfer lookup tables.
机译:我们对常用的臭氧监测仪(OMI)气溶胶指数(AI)产品进行分析,以定性检测吸收气溶胶的存在和含量。在我们的分析中,模拟的大气顶(TOA)辐射是由NASA戈达德地球观测系统(GEOS-5)提供的现代时代回顾性分析研究和应用的模型大气和气溶胶剖面在OMI足迹处产生的气溶胶再分析(MERRAero)。在先前的论文中建立了MERRAero对OMI AI进行仿真的可信度后,我们描述了方法和气溶胶光学特性假设方面的更新。根据MERRAero大气状态在无云条件下计算OMI TOA辐射,并计算AI。将模拟的TOA辐射馈入OMI近紫外气溶胶检索算法(称为OMAERUV),并与MERRAero计算的AI进行比较。讨论了两个主要的差异来源:一个与表面压力的OMI算法假设有关,通常与观测值的实际表面压力不同,另一个与简化用于观测的分子大气辐射传递的假设有关。 OMI算法。假设地表压力会导致OMAERUV AI出现系统性偏差,尤其是在海洋上。分子辐射传递的简化特别是在表面压力介于600和1013.25?hPa之间的形貌区域中产生了偏差。通常,由于这些考虑,OMI AI中的误差的幅度小于0.2,尽管较大的误差是可能的,尤其是在陆地上。我们建议OMI算法的未来版本使用来自现成的大气分析的表面压力以及高空间分辨率的地形图,并在其辐射传递查找表中包括更多的表面压力节点。

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