Abstract
Abstract. Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3.
Highlights
The source term describes the spatiotemporal distribution of an atmospheric release, and it is of great interest in the case of an accidental atmospheric release
The same box-and-whisker plots are computed for the LS-APC-VB method with the same scheme of selection of tuning parameters errx using the cross-validation method and for the LS-APC-VB algorithm with the tuning parameter errx set to 100 as recommended in Tichý et al (2016). These results suggest that the LS-APC-VB method with fixed start (but weighted to data using selection of ω(0)) slightly outperforms other methods in terms of the mean mean absolute error (MAE) for European Tracer Experiment (ETEX) data with various assumed prior source terms without the necessity of exhaustive tuning
The resulting estimates of the total released activity are displayed in Fig. 9 where the total of the prior source term used xa is displayed with a dashed red line
Summary
The source term describes the spatiotemporal distribution of an atmospheric release, and it is of great interest in the case of an accidental atmospheric release. 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). One common regularization is the knowledge of the prior source term, known as the first guess, considered within the optimization procedure (Eckhardt et al, 2008; Liu et al, 2017; Chai et al, 2018). This knowledge could dominate the resulting estimate and even outweigh the information present in the measured data. We utilize the ETEX (European Tracer Experiment) and Chernobyl datasets for demonstration
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