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A new ground-based differential absorption sunphotometer for measuring atmospheric columnar CO_2 and preliminary applications

机译:一种用于测量大气柱状CO_2的新型地基差分吸收太阳光度计及其初步应用

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Carbon dioxide is commonly considered as the most important greenhouse gas. Ground-based remote sensing technology of acquiring CO_2 columnar concentration is needed to provide validation for spaceborne CO_2 products. A new ground-based sunphotometer prototype for remotely measuring atmospheric CO_2 is introduced in this paper, which is designed to be robust, portable, automatic and suitable for field observation. A simple quantity, Differential Absorption Index (DAI) related to CO_2 optical depth, is proposed to derive the columnar CO_2 information based on the differential absorption principle around 1.57 micron. Another sun/sky radiometer CE318, is used to provide correction parameters of aerosol extinction and water vapor absorption. A cloud screening method based on the measurement stability is developed. A systematic error assessment of the prototype and DAI is also performed. We collect two-year DAI observation from 2010 to 2012 in Beijing, analyze the DAI seasonal variation and find that the daily average DAI decreases in growing season and reaches to a minimum on August, while increases after that until January of the next year, when DAI reaches its highest peak, showing generally the seasonal cycle of CO_2. We also investigate the seasonal differences of DAI variation and attribute the tendencies of high in the morning and evening while low in the noon to photosynthesis efficiency variation of vegetation and anthropogenic emissions. Preliminary comparison between DAI and model simulated XCO_2 (Carbon Tracker 2011) is conducted, showing that DAI roughly reveals some temporal characteristics of CO_2 when using the average of multiple measurements.
机译:二氧化碳通常被认为是最重要的温室气体。需要用于获取CO_2柱状浓度的地面遥感技术,以对星载CO_2产品进行验证。本文介绍了一种用于远程测量大气中CO_2的新型地面日光光度计原型,该原型设计坚固,便携,自动且适合于野外观察。提出了一种基于CO_2光学深度的微分吸收指数(DAI),基于1.57微米左右的微分吸收原理推导柱状CO_2信息。另一个日光辐射计CE318用于提供气溶胶消光和水蒸气吸收的校正参数。开发了一种基于测量稳定性的云筛查方法。还对原型和DAI进行了系统的误差评估。我们收集了2010年至2012年在北京进行的两年DAI观测,分析了DAI的季节性变化,发现在生长季节,DAI的日平均DAI有所下降,并在8月达到最低值,而之后的DAI则一直上升到次年的1月。 DAI达到最高峰,总体上显示了CO_2的季节性周期。我们还调查了DAI变化的季节差异,并将早晨和晚上较高而中午较低的趋势归因于植被和人为排放的光合作用效率变化。对DAI和模型模拟的XCO_2(Carbon Tracker 2011)进行了初步比较,表明当使用多次测量的平均值时,DAI大致揭示了CO_2的一些时间特性。

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