A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Space-borne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on the Optimal Estimation algorithm, relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry air mole fraction of carbon dioxide ($X_{{\mathrm{CO}}_{\mathrm{2}}}$) from a sample of measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission. Those have been selected as a compromise between coverage and the lowest aerosol content possible, so that the impact of scattering particles can be neglected, for computational time purposes. For air masses below 3.0, 5AI $X_{{\mathrm{CO}}_{\mathrm{2}}}$ retrievals successfully capture the latitudinal variations of CO₂ and its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a bias of 1.30±1.32 ppm (parts per million), which is comparable to the standard deviation of the Atmospheric CO₂ Observations from Space (ACOS) official products over the same set of soundings. These nonscattering 5AI results, however, exhibit an average difference of about 3 ppm compared to ACOS results. We show that neglecting scattering particles for computational time purposes can explain most of this difference that can be fully corrected by adding to OCO-2 measurements an average calculated–observed spectral residual correction, which encompasses all the inverse setup and forward differences between 5AI and ACOS. These comparisons show the reliability of 5AI as an optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry air mole fractions of greenhouse gases.

中文翻译：

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适应性强的 4A 反演 (5AI)：来自轨道碳观测站 2 (OCO-2) 观测的描述和第一次X _{CO 2}反演

为了应对气候变化的全球挑战，需要更好地了解温室气体表面源和汇。对温室气体大气浓度的星载远程估计可以提供必要的全球覆盖范围，以改善对其通量的限制，从而能够更好地监测人为排放。在这项工作中，我们介绍了自适应 4A 反演 (5AI) 反演方案，旨在从任何遥感观测中检索地球物理参数。该算法基于 Optimal Estimation 算法，依赖于 Automatized Atmospheric Absorption Atlas (4A/OP) 辐射传输前向模型的操作版本以及 Gestion et Étude des Informations Spectroscopiques Atmosphériques：大气光谱信息（GEISA）光谱数据库的管理和研究。在这里，应用 5AI 方案来检索二氧化碳的柱平均干空气摩尔分数（$X_{{\mathrm{二氧化碳}}_{\mathrm{2}}}$) 来自轨道碳观测站 2 (OCO-2) 任务执行的测量样本。这些已被选择为之间的折衷覆盖范围和尽可能低的气溶胶含量，因此出于计算时间的目的，可以忽略散射粒子的影响。对于3.0以下的气团，5AI$X_{{\mathrm{二氧化碳}}_{\mathrm{2}}}$反演成功捕捉到CO ₂的纬度变化及其季节周期和长期增长趋势。与来自总碳柱观测网络 (TCCON) 的地面观测结果进行比较，得出1.30±1.32  ppm（百万分之一）的偏差，与大气CO ₂的标准偏差相当来自太空的观测 (ACOS) 官方产品在同一组探测上。然而，与 ACOS 结果相比，这些非散射 5AI 结果的平均差异约为 3 ppm。我们表明，出于计算时间目的而忽略散射粒子可以解释大部分这种差异，可以通过向 OCO-2 测量添加平均计算-观察到的光谱残差校正来完全校正，其中包括 5AI 和 ACOS 之间的所有反向设置和正向差异. 这些比较表明 5AI 作为最佳估计实施方案的可靠性，可轻松适用于任何旨在检索温室气体的柱平均干空气摩尔分数的仪器。