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The main goal of this talk is illustrating the usefulness of some recent
spatiotemporal analysis tools in the field of weather prediction. To this
aim we first present a recent characterization of spatiotemporal error
growth (the so called mean-variance logarithmic (MVL) diagram, Primo et al.
2005, Gutiérrez et al. 2008) and then we describe an application using
coupled ocean-atmosphere Ensemble Prediction Systems (Fernández et al.
2009); in particular we consider the DEMETER multimodel seasonal hindcast
(http://www.ecmwf.int/research/demeter/) and focus on both initial
conditions (three different perturbation procedures) and model errors (seven
coupled GCMs). We show that, as opposite to the standard temporal analysis
(spatially-averaged or single-point), the MVL spatiotemporal analysis
accounts for the nontrivial localization of fluctuations, thus allowing
disentangling the effects of the different initialization procedures
(random, lagged, singular vectors) and the different model formulations. For
instance, it is shown that the shared building blocks of the GCMs
(atmospheric and ocean components) impose similar dynamics among different
models and, thus, contribute to poorly sampling the model formulation
uncertainty.
References: C. Primo, Rodríguez, M.A. López, J.M. Szendro, I. (2005) Predictability, bred vectors, and generation of ensembles in space-time chaotic systems. Physical Review E, 72, 15201-15206. J.M. Gutiérrez, C. Primo, M.A. Rodríguez and J. Fernández (2008) Spatiotemporal Characterization of Ensemble Prediction Systems. The Mean-Variance of Logarithms (MVL) Diagram. Nonlinear Processes in Geophysics, 15, 109-114. http://www.nonlin-processes-geophys.net/15/109/2008/npg-15-109-2008.pdf J. Fernández, C. Primo, A. S. Cofiño, J.M. Gutiérrez, M.A. Rodríguez (2009) MVL Spatiotemporal analysis for model comparison. Application to the DEMETER Multi-model Ensemble. Climate Dynamics, 33, 233-243. DOI: 10.1007/s00382-008-0456-9 |
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