Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
By A. Ganesan, M. Rigby, A. Zammit‐Mangion, A. Manning, R. Prinn, P. Fraser, C. Harth, Kyung-Ryul Kim, P. Krummel, Shanlan Li and 6 more. 2013
161 citations recorded by the source corpus.
We present a hierarchical Bayesian method for at- mospheric trace gas inversions. This method is used to esti- mate 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 com- plete estimation of emissions and their uncertainties than tra- ditional 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 repre- sentation uncertainties, in particular, can lead to the deriva- tion of emissions and associated uncertainties that are un- realistic and show that those derived using the hierarchical method are likely to be more representative of the true uncer- tainties 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.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/07cee2511bf9e2ca58b232de19d745812b79eb38
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Citations in the corpus
- History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE) (2018, methodology): “Ganesan et al. (2014) explicitly allow such uncertainties to be derived in the inversion to minimize the effect of subjective assumptions on derived fluxes.”
- Estimation of trace gas fluxes with objectively determined basis functions using reversible jump Markov Chain Monte Carlo (2016, methodology): “Ganesan et al. (2014) presented an alternative method, where the hyperparameters and parameters were estimated simultaneously using an MCMC algorithm.”