Sparse optimization for inverse problems in atmospheric modelling. (May 2016)

• Record Type: Journal Article
• Authors: Adam, L.; Branda, M.
• Is Part Of: Environmental modelling & software. Volume 79(2016:May)
• Page Start: 256
• Page End: 266
• Publication Date: 2016-05
• Subjects: Inverse modelling -- Sparse optimization -- Integer optimization -- Least squares -- European tracer experiment -- Free Matlab codes; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.02.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We consider inverse problems in atmospheric modelling represented by a linear system which is based on a source-receptor sensitivity matrix and measurements. Instead of using the ordinary least squares, we add a weighting matrix based on the topology of measurement points and show the connection with Bayesian modelling. Since the source-receptor sensitivity matrix is usually ill-conditioned, the problem is often regularized, either by perturbing the objective function or by modifying the sensitivity matrix. However, both these approaches may be heavily dependent on specified parameters. To ease this burden, we propose to use techniques looking for a sparse solution with a small number of positive elements. Finally, we compare all these methods on the European Tracer Experiment (ETEX) data where there is no apriori information apart from the release position and some measurements. Highlights: Inverse problems in atmospheric modelling are investigated. Sparse optimization techniques are introduced and discussed. Natural restrictions on the solution (nonnegativity of the release) are considered. The methods are compared on the European Tracer Experiment (ETEX) data. Free Matlab codes are provided.