Sources of uncertainty in atmospheric dispersion modeling in support of Comprehensive Nuclear–Test–Ban Treaty monitoring and verification system

On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART

“…The aim of inverse modeling is to reconstruct the source term by maximization of agreement between the ambient measurements and prediction of an atmospheric transport model in a so-called topdown approach (Nisbet and Weiss, 2010). Since information provided by the measurements is often insufficient in both spatial and temporal domains (Mekhaimr and Wahab, 2019), additional information and regularization of the problem are crucial for a reasonable estimation of the source term (Seibert et al, 2011). Otherwise, the top-down determination of the source term can produce artifacts, often resulting in some completely implausible values of the source term.…”

“…The optimization terms are typically weighted by covariance matrices whose forms and estimation have been studied in the literature. Diagonal covariance matrices have been considered by Michalak et al (2005) and its entries estimated using the maximum likelihood method. Since the estimation of full covariance matrices tends to diverge (Berchet et al, 2013), approaches using a fixed common autocorrelation timescale parameter for non-diagonal entries has been introduced (Ganesan et al, 2014;Henne et al, 2016) for atmospheric gas inversion.…”