title: Linear regression models and neural networks for the fast emulation of a molecular absorption code.
abstract: The background scene generator MATISSE, whose main functionality is to generate natural background radiance images, makes use of the so-called Correlated K (CK) model. It necessitates either loading or computing thousands of CK coefficients for each atmospheric profile. When the CK coefficients cannot be loaded, the computation time becomes prohibitive. The idea developed in this paper is to substitute fast approximate models for the exact CK generator; using the latter, a representative set of numerical examples is built and used to train linear or nonlinear regression models. The resulting models enable an accurate CK coefficient computation for all the profiles of an image in a reasonable time.
authors.affiliation: Equipe de Statistique Appliquée, Ecole Supérieure de Physique et de Chimie Industrielles,10 rue Vauquelin, 75005 Paris, France. guillaume.euvrard@espci.fr
authors.forename: Guillaume
authors.initials: G
authors.lastname: Euvrard
authors.forename: Isabelle
authors.initials: I
authors.lastname: Rivals
authors.forename: Thierry
authors.initials: T
authors.lastname: Huet
authors.forename: Sidonie
authors.initials: S
authors.lastname: Lefebvre
authors.forename: Pierre
authors.initials: P
authors.lastname: Simoneau