WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian-synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and uncertainties on non-fossil-fuel CO$_2$ fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019, Atmos. Chem. Phys., vol. 19). We also find that our predictions of out-of-sample retrievals from the Total Column Carbon Observing Network are, for the most part, more accurate than those made by the MIP participants. Subsequent versions of the OCO-2 datasets will be ingested into WOMBAT as they become available.
翻译:WOMBAT(WOMBAT)通过考虑一个相关的错误术语、在线偏差校正能力以及提供巴伊西亚统计模型中出现的所有未知数据的不确定性量化方法,扩展了传统的巴伊西亚合成框架(WOMBAT)(Bayesian Trace-gas的Wolong方法)是一个完全Bayesian的等级统计框架,用于原地法和遥感数据的痕量反转。WOMBAT(WOMBAT)通过考虑一个相关的错误术语、在线偏差校正能力以及提供与Bayesian统计模型中出现的所有未知数据相比的不确定性量化。我们在一次观察系统模拟实验(OSE)中显示,当数据确实存在偏差且有相关的错误时,这些扩展至关重要。我们利用GEOS-Chem大气运输模型,我们表明WOMBAT能够从运行的碳观测站-2(OCO-2)中获取与模型Intercomporation项目(MIP)数据相比的后期数据流和MBO-IP(MO)的最近数据流数据流,我们发现我们最能从IMO-IP(MW)网络中检索的数据流数据流中可以更多地成为O-ILO-CO-IL)的一部分。