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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模拟臭氧监测仪器的气溶胶指数

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摘要

We provide an analysis of the commonly used Ozone MonitoringInstrument (OMI) aerosol index (AI) product for qualitativedetection of the presence and loading of absorbing aerosols. In ouranalysis, simulated top-of-atmosphere (TOA) radiances are producedat the OMI footprints from a model atmosphere and aerosol profileprovided by the NASA Goddard Earth Observing System (GEOS-5)Modern-Era Retrospective Analysis for Research and Applicationsaerosol reanalysis (MERRAero). Having established the credibility ofthe MERRAero simulation of the OMI AI in a previous paper wedescribe updates in the approach and aerosol optical propertyassumptions. The OMI TOA radiances are computed in cloud-freeconditions from the MERRAero atmospheric state, and the AI iscalculated. The simulated TOA radiances are fed to the OMI near-UV aerosol retrieval algorithms (known as OMAERUV) is comparedto the MERRAero calculated AI. Two main sources of discrepancy arediscussed: one pertaining to the OMI algorithm assumptions of thesurface pressure, which are generally different from what the actualsurface pressure of an observation is, and the other related tosimplifying assumptions in the molecular atmosphere radiativetransfer used in the OMI algorithms. Surface pressure assumptionslead to systematic biases in the OMAERUV AI, particularly over theoceans. Simplifications in the molecular radiative transfer lead tobiases particularly in regions of topography intermediate to surfacepressures of 600 and 1013.25 hPa. Generally, the errors inthe OMI AI due to these considerations are less than 0.2 inmagnitude, though larger errors are possible, particularly overland. We recommend that future versions of the OMI algorithms usesurface pressures from readily available atmospheric analysescombined with high-spatial-resolution topographic maps and includemore surface pressure nodal points in their radiative transferlookup tables.
机译:我们对常用的臭氧监测仪器(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,由NASA戈达德地球观测系统(GEOS-5)提供的现代时代回顾性研究和应用气溶胶再分析(MERRAero)提供的模型大气和气溶胶剖面在OMI足迹上产生了模拟的大气顶(TOA)辐射。 。在上一篇文章中建立了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性质假设方面的更新。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OMI算法中使用的分子大气辐射传递的简化假设有关。表面压力的假设导致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之间的地形区域中导致偏斜。通常,由于这些考虑,OMI AI中的误差小于0.2幅值,尽管可能会出现较大的误差,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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