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首页> 外文期刊>Remote Sensing >A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering?¢????Part 1: Radiative Transfer and a Potential OCO-2 XCO 2 Retrieval Setup
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A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering?¢????Part 1: Radiative Transfer and a Potential OCO-2 XCO 2 Retrieval Setup

机译:用于高光谱仪器的快速大气痕量气体检索,近似于多次散射-第1部分:辐射传递和潜在的OCO-2 XCO 2检索设置

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摘要

Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO 2 (XCO 2 ) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O 2 and CO 2 absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO 2 data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO 2 retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO 2 monitoring satellites will produce at least an order of magnitude more data. Here we introduce the Fast atmOspheric traCe gAs retrievaL FOCAL including a scalar RT model which approximates multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer and a Lambertian surface. The computational performance is similar to an absorption only model and currently determined by the convolution of the simulated spectra with the instrumental line shape function (ILS). We assess FOCAL?¢????s quality by confronting it with accurate multiple scattering vector RT simulations using SCIATRAN. The simulated scenarios do not cover all possible geophysical conditions but represent, among others, some typical cloud and aerosol scattering scenarios with optical thicknesses of up to 0.7 which have the potential to survive the pre-processing of a XCO 2 algorithm for real OCO-2 measurements. Systematic errors of XCO 2 range from ?¢????2.5 ppm (?¢????6.3?¢???°) to 3.0 ppm (7.6?¢???°) and are usually smaller than ???±0.3 ppm (0.8?¢???°). The stochastic uncertainty of XCO 2 is typically about 1.0 ppm (2.5?¢???°). FOCAL simultaneously retrieves the dry-air column-average mole fraction of H 2 O (XH 2 O) and the solar induced chlorophyll fluorescence at 760 nm (SIF). Systematic and stochastic errors of XH 2 O are most times smaller than ???±6 ppm and 9 ppm, respectively. The systematic SIF errors are always below 0.02 mW/m 2 /srm, i.e., it can be expected that instrumental or forward model effects causing an in-filling of the used Fraunhofer lines will dominate the systematic errors when analyzing actually measured data. The stochastic uncertainty of SIF is usually below 0.3 mW/m 2 /srm. Without understating the importance of analyzing synthetic measurements as presented here, the actual retrieval performance can only be assessed by analyzing measured data which is subject to part 2 of this publication.
机译:在适当的近(NIR)和短波红外(SWIR)O 2和CO 2吸收带的高光谱测量基础上,卫星对大气干空气CO 2(XCO 2)的平均摩尔分数的检索可以帮助回答重要问题关于碳循环,但对XCO 2数据产品的精度和准确性要求很高。光在气溶胶和云中的多次散射可能是XCO 2检索的重要误差来源。因此,开发了所谓的全物理检索算法,旨在通过明确拟合与散射相关的属性(例如云水/冰含量,气溶胶光学厚度,云高度等)来最大程度地减少与散射相关的误差。但是,多次散射辐射转移的计算成本( RT)的计算可能非常庞大。处理轨道碳天文台2(OCO-2)的所有数据可能需要多达数千个CPU核心,而下一代CO 2监测卫星将至少产生至少一个数量级的数据。在这里,我们介绍了包括标量RT模型的快速大气轨迹gAs检索FOCAL,该模型使用各向同性散射层和朗伯表面的RT问题的解析解来近似多重散射效应。计算性能类似于仅吸收模型,当前通过模拟光谱与仪器线形函数(ILS)的卷积确定。我们通过使用SCIATRAN进行精确的多重散射矢量RT模拟来评估FOCAL的质量。模拟场景并未涵盖所有可能的地球物理条件,但除其他外,代表了一些典型的云和气溶胶散射场景,其光学厚度最大为0.7,它们有可能在针对真实OCO-2的XCO 2算法的预处理中幸存下来测量。 XCO 2的系统误差范围为2.5 ppm(6.3°C)到3.0 ppm(7.6°C)的3.0 ppm,通常小于。 ±0.3ppm(0.8≤¢°)。 XCO 2的随机不确定性通常约为1.0 ppm(2.5 ???????°)。 FOCAL同时获取H 2 O(XH 2 O)的干燥空气柱平均摩尔分数和760 nm(SIF)的太阳诱导叶绿素荧光。 XH 2 O的系统误差和随机误差多数时候分别小于±6 ppm和9 ppm。系统SIF误差始终低于0.02 mW / m 2 / sr / nm,即可以预期,在分析实际测量数据时,仪器或正向模型效应会导致所用Fraunhofer线的填充,从而将主导系统误差。 SIF的随机不确定性通常低于0.3 mW / m 2 / sr / nm。在不低估此处介绍的分析合成测量值的重要性的情况下,只能通过分析测量数据来评估实际的检索性能,该数据受本出版物第2部分的约束。

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