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Atmospheric transport model (ATM) uncertainty continues to be a significant constraining factor in making confident top-down (inverse model based) GHG emission estimates. Despite its importance, accurately gauging model uncertainty and capturing its temporal fluctuations remains a challenge. Inversion frameworks typically involve an empirical selection of data to be assimilated whereby only the data from periods where the ATM has the lowest uncertainties are used for the inversion. There are numerous data filtering methods, that often depend on modelled parameters (mixing height, wind speed, potential temperature), which could result in data selection bias.To address this, we present analysis of radon measurements, a natural radioactive noble gas with simple and well-constrained source and sink. Radons unique characteristics make it an ideal tracer to study the transport and mixing of air and thus has potential to act as an independent metric to evaluate ATM performance. A new approach involves utilising measured and modelled radon (calculated using the Met Office Numerical Atmospheric Modelling Environment (NAME) dispersion model and radon flux map) to classify the ATM output uncertainty as either high (poor performance) or low (the best performance). This approach could be universally applied to any location measuring radon from a single inlet height and in conjunction with any other dispersion modelling scenarios. To evaluate the effectiveness of the radon selection method, we assess the methane (CH4) emissions across the UK using four tall tower sites (part of the Deriving Emissions linked to Climate Change - DECC network): Heathfield, Ridge Hill, Tacolneston and Weybourne. The CH4 emissions are estimated by the Met Offices inversion modelling system Inversion Technique for Emission Modelling (InTEM). We will compare how emissions sensitivity varies between our radon-based approach and the current selection method, which relies on model parameters and the vertical gradient of CH4 measurements. This comparative analysis aims to demonstrate the potential advantages of using radon as a tool for improving the accuracy of ATM performance assessments in GHG emission estimates.
Kikaj et al. (Mon,) studied this question.