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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 11
Geosci. Model Dev., 8, 3579–3591, 2015
https://doi.org/10.5194/gmd-8-3579-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Community software to support the delivery of CMIP5

Geosci. Model Dev., 8, 3579–3591, 2015
https://doi.org/10.5194/gmd-8-3579-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 06 Nov 2015

Development and technical paper | 06 Nov 2015

An automatic and effective parameter optimization method for model tuning

T. Zhang et al.

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Cited articles

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003.
Aksoy, A., Zhang, F., and Nielsen-Gammon, J. W.: Ensemble-based simultaneous state and parameter estimation with MM5, Geophys. Res. Lett., 33, L12801, https://doi.org/10.1029/2006GL026186, 2006.
Allen, M. R., Stott, P. A., Mitchell, J. F., Schnur, R., and Delworth, T. L.: Quantifying the uncertainty in forecasts of anthropogenic climate change, Nature, 407, 617–620, 2000.
Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, Signal Processing, IEEE T., 50, 174–188, 2002.
Bardenet, R., Brendel, M., Kégl, B., and Sebag, M.: Collaborative hyperparameter tuning, in: Proceedings of the 30th International Conference on Machine Learning (ICML-13), 16–21 June 2013, Atlanta, Georgia, USA, 199–207, 2013.
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Short summary
A “three-step” methodology is proposed to effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. The optimal results improve the metrics performance by 9%. A software framework can automatically execute any part of the “three-step” calibration strategy. The proposed methodology and framework can easily be applied to other GCMs to speed up the model development process.
A “three-step” methodology is proposed to effectively obtain the optimum combination of some...
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