Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo
Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo
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DOI：
10.5194/gmd-9-3213-2016
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发表时间：
2016-09-19
影响因子：
5.1
通讯作者：
Manning, Alistair J.
中科院分区：
文献类型：
--
作者：
Lunt, Mark F.;Rigby, Matt;Manning, Alistair J.
大气痕量气体反演通常试图使用观测将通量归因于高维网格。为了使这个问题在计算上可行，并减少欠确定的程度，通常进行某种形式的降维。在这里，我们提出了一个客观的方法来减少大气痕量气体反演参数空间的空间维数。除了解决一组未知数，管理微量气体的排放量，我们制定了一个框架，认为未知数的数量本身就是一个未知数。我们依赖于完善的可逆跳马尔可夫链蒙特卡罗算法使用的数据来确定参数空间的维数。该框架提供了一个单步过程，可以求解反演网格的分辨率以及该网格的通量大小。因此，在后验参数分布中考虑了聚集选择的不确定性。这个transdimensional马尔可夫链的后验分布提供了一个自然平滑的解决方案，从空间域的粗糙分区的合奏形成。我们描述的可逆跳跃算法的形式，以及它如何可能被应用到跟踪气体反演。我们将系统构建成一个分层贝叶斯框架，其中还可以探索其他未知因素，如模型不确定性的大小。一个伪数据的例子是用来显示这种方法的有用性相比，主观选择的分区的空间域。反演使用真实的数据也示出了说明的尺度，在该数据允许西北欧的甲烷排放量得到解决。
Atmospheric trace gas inversions often attempt to attribute fluxes to a high-dimensional grid using observations. To make this problem computationally feasible, and to reduce the degree of under-determination, some form of dimension reduction is usually performed. Here, we present an objective method for reducing the spatial dimension of the parameter space in atmospheric trace gas inversions. In addition to solving for a set of unknowns that govern emissions of a trace gas, we set out a framework that considers the number of unknowns to itself be an unknown. We rely on the well-established reversible-jump Markov chain Monte Carlo algorithm to use the data to determine the dimension of the parameter space. This framework provides a single-step process that solves for both the resolution of the inversion grid, as well as the magnitude of fluxes from this grid. Therefore, the uncertainty that surrounds the choice of aggregation is accounted for in the posterior parameter distribution. The posterior distribution of this transdimensional Markov chain provides a naturally smoothed solution, formed from an ensemble of coarser partitions of the spatial domain. We describe the form of the reversible-jump algorithm and how it may be applied to trace gas inversions. We build the system into a hierarchical Bayesian framework in which other unknown factors, such as the magnitude of the model uncertainty, can also be explored. A pseudo-data example is used to show the usefulness of this approach when compared to a subjectively chosen partitioning of a spatial domain. An inversion using real data is also shown to illustrate the scales at which the data allow for methane emissions over north-west Europe to be resolved.