Reconstruction of an atmospheric tracer source in Fusion Field Trials: Analyzing resolution features

Abstract

Reconstruction of unknown atmospheric releases using measured concentrations is an ill-posed inverse problem. Due to insufficient measurements and dispersion model uncertainties, reliable interpretation of a retrieved source is limited by lack of resolution, nonuniqueness, and instability in the inverse solution. The study presents an optimality analysis, in terms of resolution, stability, and reliability, of an inverse solution given by a recently proposed inversion technique, called as renormalization. The inversion technique is based on an adjoint source-receptor framework and construction of a weight function which interprets a priori information about the unknown release apparent to the monitoring network. The properties of weight function provide a perfect data resolution, maximum model resolution, and minimum variance (or stability) for the retrieved source. The reliability of the retrieved source is interpreted in view of the information derived from the geometry of the monitoring network. The inversion technique and resolution features are evaluated for a point source reconstruction using measurements from a recent dispersion experiment (Fusion Field Trials 2007) conducted at Dugway Proving Ground, Utah. With the real measurements, the point release is reconstructed within an average distance of 23 m from the true release where the average distance of the nearest receptor from the true source was 32 m. In all the trials, the point release is retrieved within 3-60 m Euclidean distance from their true location. The source strength is retrieved within a factor of 1.5 to the true release mass. The posterior uncertainty in the release parameters is observed to be within 20% of their mean value. The source localization features are resolved to its maximum extent feasible with the design of the monitoring network. The sensitivity studies are conducted to highlight the importance of receptors reporting zero concentration measurements and variations in the resolution features of the source retrieval with respect to the various arrangements of the receptors.

Comparative interpretations of renormalization inversion technique for reconstructing unknown emissions from measured atmospheric concentrations

“…However, with renormalization, these artifacts are overcome in a natural way (Figure ) and the source information is well localized around the true source. An upwind extension in Figure reflect the lack of information in the upwind region of the monitoring network which will be avoided with the sufficient observability of the monitoring network [ Singh et al , ].…”

“…The technique requires minimal a priori information about the unknown releases and is shown efficient in reconstructing both point as well as areal distributed emission sources [ Sharan et al , ; Kumar et al , , and others]. A unique feature is the utilization of a weight matrix derived from the geometry of the monitoring network which helps to (i) provide an apparent a priori information about unknown releases to the monitoring network [ Sharan et al , ] and (ii) optimize the localization features of the retrieved source [ Turbelin et al , ; Singh et al , ]. The traditional presentation of the technique (see section 2) is different from the other inversion techniques and thus lacks a straightforward comparison with them in terms of a priori information, constraints, and parameters utilized.…”

Retrieval of Unknown Number of Source Terms in Dispersion Events Involving Multiple Point Sources

“…), cost function formulations (Ma et al, ; Thomson et al, ; Wang et al, , etc. ), and applications (Chai et al, ; Kumar et al, ; Sharan, Issartel, et al, ; Singh & Rani, ; Singh, Turbelin, et al, , etc. ).…”

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

“…With the known source terms, they provide a unique 30 opportunity to evaluate the STE methods. Singh and Rani (2014) and Singh et al (2015) used measurements from a recent dispersion experiment (Fusion Field Trials 2007) data to evaluate a least-squares technique for identification of a point release.…”