Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions
Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions
复制标题
已知物理边界的大气反演模型：痕量气体排放的示例
DOI：
10.5194/gmd-7-303-2014
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发表时间：
2013
影响因子：
5.1
通讯作者：
Patricia J. Levi
中科院分区：
文献类型：
--
作者：
Scot M. Miller;A. Michalak;Patricia J. Levi
抽象的。大气科学中的许多反问题涉及已知物理约束的参数。例子包括非负性（例如，某些城市空气污染物的排放）或反应或溶解度常数所暗示的上限。然而，基于高斯假设的概率逆建模方法不能包含这样的界限，因此经常产生不切实际的结果。大气文献缺乏共识的最佳手段来克服这一问题，现有的大气研究依赖于数量有限的可能的方法，很少检查每一个相对的优点。本文研究了几种方法对有界反问题的适用性。一个常见的数据转换方法被发现不切实际地扭曲估计检查的示例应用程序。拉格朗日乘子法和两种马尔可夫链蒙特卡罗（MCMC）方法得到了更真实、更精确的结果。在一般情况下，审查MCMC方法产生最现实的结果，但可能需要大量的计算时间。拉格朗日乘数提供了一个有吸引力的选择，大型，计算密集型的问题时，准确的不确定性界限是不太重要的分析。美国人为甲烷排放量的合成数据反演说明了每种方法的优点和缺点。
Abstract. Many inverse problems in the atmospheric sciences involve parameters with known physical constraints. Examples include nonnegativity (e.g., emissions of some urban air pollutants) or upward limits implied by reaction or solubility constants. However, probabilistic inverse modeling approaches based on Gaussian assumptions cannot incorporate such bounds and thus often produce unrealistic results. The atmospheric literature lacks consensus on the best means to overcome this problem, and existing atmospheric studies rely on a limited number of the possible methods with little examination of the relative merits of each. This paper investigates the applicability of several approaches to bounded inverse problems. A common method of data transformations is found to unrealistically skew estimates for the examined example application. The method of Lagrange multipliers and two Markov chain Monte Carlo (MCMC) methods yield more realistic and accurate results. In general, the examined MCMC approaches produce the most realistic result but can require substantial computational time. Lagrange multipliers offer an appealing option for large, computationally intensive problems when exact uncertainty bounds are less central to the analysis. A synthetic data inversion of US anthropogenic methane emissions illustrates the strengths and weaknesses of each approach.