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GOMOS data characterisation and error estimation

机译:GOMOS数据表征和误差估计

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The Global Ozone Monitoring by Occultation of Stars(GOMOS) instrument uses stellar occultation technique for monitoringozone, other trace gases and aerosols in the stratosphere and mesosphere. Theself-calibrating measurement principle of GOMOS together witha relatively simple data retrieval where only minimal use of a prioridata is required provides excellent possibilities for long-termmonitoring of atmospheric composition.GOMOS uses about 180 of the brightest stars as its lightsource. Depending on the individual spectral characteristics of thestars, the signal-to-noise ratio of GOMOS varies from star to star,resulting also in varying accuracy of retrieved profiles. We presenthere an overview of the GOMOS data characterisation and errorestimation, including modeling errors, for O3, NO2,NO3, and aerosol profiles. The retrieval error (precision) ofnight-time measurements in the stratosphere is typically 0.5–4% forozone, about 10–20% for NO2, 20–40% for NO3 and 2–50%for aerosols. Mesospheric O3, up to 100 km, can be measured with2–10% precision. The main sources of the modeling error areincompletely corrected scintillation, inaccurate aerosol modeling,uncertainties in cross sections of trace gases and in atmospherictemperature. The sampling resolution of GOMOS varies depending on themeasurement geometry. In the data inversion a Tikhonov-typeregularization with pre-defined target resolution requirement isapplied leading to 2–3 km vertical resolution for ozone and 4 km resolutionfor other trace gases and aerosols.
机译:全球星际掩星监测仪(GOMOS)使用恒星掩星技术来监测平流层和中层中的臭氧,其他痕量气体和气溶胶。 GOMOS的自校准测量原理以及相对简单的数据检索(仅需极少使用先验数据)就可以长期监测大气成分。 GOMOS使用约180个最亮的恒星作为光源。取决于恒星的各个光谱特性,GOMOS的信噪比因恒星而异,这也导致所获取轮廓的准确性有所变化。我们在此概述了O 3 ,NO 2 ,NO 3 和气溶胶剖面的GOMOS数据表征和误差估计,包括建模误差。平流层夜间测量的取回误差(精度)通常为甲虫0.5–4%,NO 2 约10-20%,NO 3 和2–50%(对于气雾剂)。可以测量2-10%的精度的中层O 3 。建模误差的主要来源是闪烁校正不完全,气溶胶建模不准确,痕量气体横截面和大气温度的不确定性。 GOMOS的采样分辨率取决于测量几何形状。在数据反演中,采用了具有预定目标分辨率要求的Tikhonov型正则化方法,导致臭氧的垂直分辨率为2–3 km,其他痕量气体和浮质的分辨率为4 km。

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