Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
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DOI：
10.5194/gmd-13-2095-2020
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发表时间：
2019-06
影响因子：
5.1
通讯作者：
L. Western;Z. Sha;M. Rigby;A. Ganesan;A. Manning;K. Stanley;S. O'Doherty;D. Young;J. Rougier
中科院分区：
文献类型：
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作者：
L. Western;Z. Sha;M. Rigby;A. Ganesan;A. Manning;K. Stanley;S. O'Doherty;D. Young;J. Rougier
摘要。我们提出了一种从大气测量和化学输运模型中推断温室气体时空相关排放的方法。该方法允许使用层次贝叶斯框架作为马尔科夫链蒙特卡罗算法的替代方案快速计算空间排放。空间发射遵循高斯过程，具有mat<s:1>相关结构，可以通过随机偏微分方程方法表示为高斯马尔可夫随机场。该推理是基于一个集成嵌套拉普拉斯近似（INLA）的高斯隐场层次模型。结合自回归时间相关和matsamrn场提供了一个完整的时空相关结构。我们首先在一个合成数据示例上演示了该方法，然后使用一个经过充分研究的测试案例，从大气摩尔分数的高塔测量中推断英国甲烷排放量。这两个试验用例的结果表明，该方法可以准确地估算区域温室气体排放量，并考虑了传统大气反演模型中被忽略的时空不确定性。
Abstract. We present a method to infer spatially and spatio-temporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. The method allows fast computation of spatial emissions using a hierarchical Bayesian framework as an alternative to Markov chain Monte Carlo algorithms. The spatial emissions follow a Gaussian process with a Matérn correlation structure which can be represented by a Gaussian Markov random field through a stochastic partial differential equation approach. The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. Combining an autoregressive temporal correlation and the Matérn field provides a full spatio-temporal correlation structure. We first demonstrate the method on a synthetic data example and follow this using a well-studied test case of inferring UK methane emissions from tall tower measurements of atmospheric mole fraction. Results from these two test cases show that this method can accurately estimate regional greenhouse gas emissions, accounting for spatio-temporal uncertainties that have traditionally been neglected in atmospheric inverse modelling.