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- W4283029187 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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." @default.
- W4283029187 created "2022-06-18" @default.
- W4283029187 creator A5017836063 @default.
- W4283029187 date "2022-06-16" @default.
- W4283029187 modified "2023-10-05" @default.
- W4283029187 title "Reply on RC2" @default.
- W4283029187 doi "https://doi.org/10.5194/gmd-2021-434-ac2" @default.
- W4283029187 hasPublicationYear "2022" @default.
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