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首页> 外文期刊>Atmospheric chemistry and physics >Technical Note: Atmospheric CO2 inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation
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Technical Note: Atmospheric CO2 inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation

机译：技术说明：使用数据驱动的现有不确定因素：方法论和系统评估

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Atmospheric inversions are widely used in the optimization of surface carbon fluxes on a regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not directly reflect the true flux uncertainties but is used to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model-data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, in which the flux constraint is derived following the same residual analysis but applied to the model-model mismatch. The synthetic study showed a quite good agreement between posterior and "true" fluxes on European, country, annual and monthly scales. Posterior monthly and country-aggregated fluxes improved their correlation coefficient with the "known truth" by 7% compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. The ratio of the SD between the posterior and reference and between the prior and reference was also reduced by 33% with a mean value of 1.15. We identified temporal and spatial scales on which the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.
机译：使用来自大气二氧化碳干摩尔级分的信息，广泛用于在区域碳通量的优化中广泛应用于区域碳通量。在许多研究中，应用于反转方案的先前助焊剂不确定性并不直接反映真正的助焊剂不确定性，但用于规范逆问题。这里，我们的目标是使用Jena反演系统实现反演方案，并应用使用欧洲域中的全年期间使用高空间和时间分辨率来实现从模型数据剩余分析的先前磁通误差结构。我们分析了具有合成实验的反演系统的性能，其中助焊剂约束在相同的残余分析之后导出，但应用于模型模型不匹配。综合研究表明，欧洲，国家，年度和每月尺度的后验和“真实”势略之间存在相当愉快的一致性。与参考相比，后期月和国家聚集的通量随着先前的估计，与先前估计相比，其相关系数提高了它们的相关系数7％，其平均相关性为0.92。后验和参考和参考之间的SD与参考之间的比率也减少了33％，平均值为1.15。我们确定了反转系统最大化派生信息的时间和空间尺度;每月时间尺度约为200公里的空间分辨率似乎最大化信息增益。

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