Articles | Volume 2, issue 1
https://doi.org/10.5194/esd-2-161-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/esd-2-161-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 Jun 2011
Research article |
| 30 Jun 2011
A multi-model ensemble method that combines imperfect models through learning
L. A. van den Berge
Royal Netherlands Meteorological Institute, Wilhelminalaan 10, 3732 GK De Bilt, The Netherlands
F. M. Selten
Royal Netherlands Meteorological Institute, Wilhelminalaan 10, 3732 GK De Bilt, The Netherlands
W. Wiegerinck
Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Geert Grooteplein 21, 6525 EZ Nijmegen, The Netherlands
G. S. Duane
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80309, USA
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20 citations as recorded by crossref.
- Supermodeling in Simulation of Melanoma Progression W. Dzwinel et al. 10.1016/j.procs.2016.05.396
- Predicting long-term population dynamics with bagging and boosting of process-based models N. Simidjievski et al. 10.1016/j.eswa.2015.07.004
- Multi-model cross-pollination in time H. Du & L. Smith 10.1016/j.physd.2017.06.001
- An efficient training scheme for supermodels F. Schevenhoven & F. Selten 10.5194/esd-8-429-2017
- A ‘Portfolio of Model Approximations’ approach to understanding invasion success with vector-borne disease M. Young & N. Fefferman 10.1016/j.mbs.2023.108994
- Role of atmosphere-ocean interactions in supermodeling the tropical Pacific climate M. Shen et al. 10.1063/1.4990713
- Phase synchronization of baroclinic waves in a differentially heated rotating annulus experiment subject to periodic forcing with a variable duty cycle P. Read et al. 10.1063/1.5001817
- Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels G. Duane et al. 10.1063/1.5018728
- Improved modeling by coupling imperfect models M. Mirchev et al. 10.1016/j.cnsns.2011.11.003
- Attractor learning in synchronized chaotic systems in the presence of unresolved scales W. Wiegerinck & F. Selten 10.1063/1.4990660
- Empirical correction techniques: analysis and applications to chaotically driven low-order atmospheric models I. Trpevski et al. 10.5194/npg-20-199-2013
- Dynamically combining climate models to “supermodel” the tropical Pacific M. Shen et al. 10.1002/2015GL066562
- A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base M. Wyatt & J. Peters 10.1186/2193-1801-1-68
- Framework for an Ocean‐Connected Supermodel of the Earth System F. Counillon et al. 10.1029/2022MS003310
- Simulating climate with a synchronization-based supermodel F. Selten et al. 10.1063/1.4990721
- Improving weather and climate predictions by training of supermodels F. Schevenhoven et al. 10.5194/esd-10-789-2019
- Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1 F. Schevenhoven & A. Carrassi 10.5194/gmd-15-3831-2022
- Forecast improvement in Lorenz 96 system L. Basnarkov & L. Kocarev 10.5194/npg-19-569-2012
- Complete synchronization of chaotic atmospheric models by connecting only a subset of state space P. Hiemstra et al. 10.5194/npg-19-611-2012
- A concept of a prognostic system for personalized anti-tumor therapy based on supermodeling W. Dzwinel et al. 10.1016/j.procs.2017.05.013
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