Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
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DOI：
10.5194/acp-14-3855-2014
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发表时间：
2014-01-01
影响因子：
6.3
通讯作者：
Weiss, R. F.
中科院分区：
文献类型：
--
作者：
Ganesan, A. L.;Rigby, M.;Weiss, R. F.
提出了一种大气痕量气体反演的分层贝叶斯方法。该方法用于估计痕量气体的排放量以及表征先验排放量和模型测量协方差的概率密度函数（PDF）的“超参数”。通过探索“不确定性中的不确定性”的空间，我们表明，分层方法比传统的贝叶斯反演，这在很大程度上依赖于专家的判断，在一个更完整的估计排放量及其不确定性。我们提出了一个分析，显示了包括超参数的影响，这些超参数本身是由数据提供信息的，并表明这种方法可以用来减少关于先验排放和模型测量不确定性的假设中的误差的影响。然后，我们应用这种方法来估计六氟化硫（SF6）的排放量超过2012年的四个先进的全球大气气体实验（AGAGE）站周围的地区。我们发现，不适当的会计模型表示的不确定性，特别是，可能会导致推导的排放量和相关的不确定性是不现实的，并表明那些使用分层方法得到的可能是更有代表性的系统中的真实不确定性。我们通过这个SF6的案例研究表明，这种方法是不敏感的离群数据和主观假设的先验排放量和模型测量的不确定性比传统的方法。
We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods.