Analog data assimilation for the selection of suitable general circulation models

• Autores: Ruiz, Juan Jose; Ailliot, Pierre; Chau, Thi Tuyet Trang; Le Bras, Pierre; Monbet, Valérie; Sévellec, Florian; Tandeo, Pierre
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Analog regression; Machine learning; Model validation; Data assimilation
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Data assimilation is a relevant framework to merge a dynamical model with noisy observations. When various models are in competition, the question is to find the model that best matches the observations. This matching can be measured by using the model evidence, defined by the likelihood of the observations given the model. This study explores the performance of model selection based on model evidence computed using data-driven data assimilation, where dynamical models are emulated using machine learning methods. In this work, the methodology is tested with the three-variable Lorenz model and with an intermediate complexity atmospheric general circulation model (a.k.a. the SPEEDY model). Numerical experiments show that the data-driven implementation of the model selection algorithm performs as well as the one that uses the dynamical model. The technique is able to select the best model among a set of possible models and also to characterize the spatiotemporal variability of the model sensitivity. Moreover, the technique is able to detect differences among models in terms of local dynamics in both time and space which are not reflected in the first two moments of the climatological probability distribution. This suggests the implementation of this technique using available long-term observations and model simulations.
Fil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Ailliot, Pierre. Laboratoire de Mathématiques de Bretagne Atlantique; Francia
Fil: Chau, Thi Tuyet Trang. Laboratoire Des Sciences Du Climat Et de L'environnement; Francia
Fil: Le Bras, Pierre. Institut Universitaire Européen de la Mer; Francia. Laboratoire Des Sciences Et Techniques de L'information, de la Communication Et de la Connaissance; Francia
Fil: Monbet, Valérie. Universite de Rennes I; Francia. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Sévellec, Florian. Institut Universitaire Européen de la Mer; Francia. University of Southampton; Reino Unido
Fil: Tandeo, Pierre. Laboratoire Des Sciences Et Techniques de L'information, de la Communication Et de la Connaissance; Francia