Articles | 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.
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.
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
20 citations as recorded by crossref.
- Impacts of uncertain cloud-related parameters on Pacific Walker circulation simulation in GAMIL2 F. XIE et al. 10.1080/16742834.2018.1392228
- Uncertainty reduction in quantitative precipitation prediction by tuning of Kain–Fritch scheme input parameters in the WRF model using the simulated annealing optimization method M. Afshar et al. 10.1002/met.1919
- Assessing the Influence of Microphysical and Environmental Parameter Perturbations on Orographic Precipitation A. Morales et al. 10.1175/JAS-D-18-0301.1
- An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3) L. Wu et al. 10.5194/gmd-13-41-2020
- Performance optimization and evaluation for parallel processing of big data in earth system models Y. Wang et al. 10.1007/s10586-017-1477-0
- The Art and Science of Climate Model Tuning F. Hourdin et al. 10.1175/BAMS-D-15-00135.1
- Recent Progress in Numerical Atmospheric Modeling in China R. Yu et al. 10.1007/s00376-019-8203-1
- Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
- Adjoint-Based Climate Model Tuning: Application to the Planet Simulator G. Lyu et al. 10.1002/2017MS001194
- Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation S. Li et al. 10.1002/2017MS001222
- Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method T. Zhang et al. 10.5194/gmd-11-5189-2018
- The importance of uncertainty quantification in model reproducibility V. Volodina & P. Challenor 10.1098/rsta.2020.0071
- Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3 S. Tett et al. 10.5194/gmd-10-3567-2017
- Automated parameter tuning applied to sea ice in a global climate model L. Roach et al. 10.1007/s00382-017-3581-5
- Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation S. Li et al. 10.5194/gmd-12-3017-2019
- Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases A. Anand et al. 10.1038/s41598-018-21865-1
- The GAMIL3: Model Description and Evaluation L. Li et al. 10.1029/2020JD032574
- Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity S. Burrows et al. 10.1007/s00376-018-7300-x
- Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6) R. Pathak et al. 10.3389/fclim.2021.670740
- Assessing Parameter Importance of the Weather Research and Forecasting Model Based On Global Sensitivity Analysis Methods D. Ji et al. 10.1002/2017JD027348
20 citations as recorded by crossref.
- Impacts of uncertain cloud-related parameters on Pacific Walker circulation simulation in GAMIL2 F. XIE et al. 10.1080/16742834.2018.1392228
- Uncertainty reduction in quantitative precipitation prediction by tuning of Kain–Fritch scheme input parameters in the WRF model using the simulated annealing optimization method M. Afshar et al. 10.1002/met.1919
- Assessing the Influence of Microphysical and Environmental Parameter Perturbations on Orographic Precipitation A. Morales et al. 10.1175/JAS-D-18-0301.1
- An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3) L. Wu et al. 10.5194/gmd-13-41-2020
- Performance optimization and evaluation for parallel processing of big data in earth system models Y. Wang et al. 10.1007/s10586-017-1477-0
- The Art and Science of Climate Model Tuning F. Hourdin et al. 10.1175/BAMS-D-15-00135.1
- Recent Progress in Numerical Atmospheric Modeling in China R. Yu et al. 10.1007/s00376-019-8203-1
- Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
- Adjoint-Based Climate Model Tuning: Application to the Planet Simulator G. Lyu et al. 10.1002/2017MS001194
- Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation S. Li et al. 10.1002/2017MS001222
- Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method T. Zhang et al. 10.5194/gmd-11-5189-2018
- The importance of uncertainty quantification in model reproducibility V. Volodina & P. Challenor 10.1098/rsta.2020.0071
- Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3 S. Tett et al. 10.5194/gmd-10-3567-2017
- Automated parameter tuning applied to sea ice in a global climate model L. Roach et al. 10.1007/s00382-017-3581-5
- Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation S. Li et al. 10.5194/gmd-12-3017-2019
- Indian Summer Monsoon Simulations: Usefulness of Increasing Horizontal Resolution, Manual Tuning, and Semi-Automatic Tuning in Reducing Present-Day Model Biases A. Anand et al. 10.1038/s41598-018-21865-1
- The GAMIL3: Model Description and Evaluation L. Li et al. 10.1029/2020JD032574
- Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity S. Burrows et al. 10.1007/s00376-018-7300-x
- Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6) R. Pathak et al. 10.3389/fclim.2021.670740
- Assessing Parameter Importance of the Weather Research and Forecasting Model Based On Global Sensitivity Analysis Methods D. Ji et al. 10.1002/2017JD027348
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Latest update: 30 Jul 2021
Special issue
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...
