Articles | Volume 15, issue 9
Development and technical paper
 | Highlight paper
12 May 2022
Development and technical paper | Highlight paper |  | 12 May 2022

Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1

Francine Schevenhoven and Alberto Carrassi

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Cited articles

Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, WIREs Clim. Change, 9, e535,, 2018. a, b
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A., and Wehner, M.: Long-term Climate Change: Projections, Commitments and Irreversibility, book section 12, Cambridge University Press, Cambridge, UK and New York, NY, USA, 1029–1136,, 2013. a
Doblas-Reyes, F. J., Hagedorn, R., and Palmer, T.: The rationale behind the success of multi-model ensembles in seasonal forecasting – II. Calibration and combination, Tellus A, 57, 234–252,, 2005. a
Du, H. and Smith, L. A.: Multi-model cross-pollination in time, Physica D, 353–354, 31–38,, 2017. a
Duane, G. S., Yu, D., and Kocarev, L.: Identical synchronization, with translation invariance, implies parameter estimation, Phys. Lett. A, 371, 416–420,, 2007. a, b, c, d
Short summary
In this study, we present a novel formulation to build a dynamical combination of models, the so-called supermodel, which needs to be trained based on data. Previously, we assumed complete and noise-free observations. Here, we move towards a realistic scenario and develop adaptations to the training methods in order to cope with sparse and noisy observations. The results are very promising and shed light on how to apply the method with state of the art general circulation models.