Articles | Volume 8, issue 11
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:
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
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Center for Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing 100084, China
L. Li
CORRESPONDING AUTHOR
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Y. Lin
Center for Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing 100084, China
W. Xue
CORRESPONDING AUTHOR
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Center for Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing 100084, China
F. Xie
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
H. Xu
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
X. Huang
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Center for Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing 100084, China
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- Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model D. Williamson et al. 10.5194/gmd-10-1789-2017
- 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
- Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al. 10.1016/j.nhres.2024.11.004
- 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
- Adjoint‐Based Climate Model Tuning: Application to the Planet Simulator G. Lyu et al. 10.1002/2017MS001194
- 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
- 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
- The GAMIL3: Model Description and Evaluation L. Li et al. 10.1029/2020JD032574
- 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
- Impacts of uncertain cloud-related parameters on Pacific Walker circulation simulation in GAMIL2 F. XIE et al. 10.1080/16742834.2018.1392228
- Quantifying Parametric Uncertainty Effects on Tropical Cloud Fraction in an AGCM F. Xie et al. 10.1029/2022MS003221
- Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence T. Schneider et al. 10.5194/acp-24-7041-2024
- Stochastic pricing formulation for hybrid equity warrants T. Roslan et al. 10.3934/math.2022027
- 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
- Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
- 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
- Optimization and Evaluation of Stochastic Unified Convection Using Single‐Column Model Simulations at Multiple Observation Sites J. Shin & J. Baik 10.1029/2022MS003473
- Automated parameter tuning applied to sea ice in a global climate model L. Roach et al. 10.1007/s00382-017-3581-5
- 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
- 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
- A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation X. Wu et al. 10.3390/rs17030408
- Earth system model parameter adjustment using a Green's functions approach E. Strobach et al. 10.5194/gmd-15-2309-2022
- Assessing Parameter Importance of the Weather Research and Forecasting Model Based On Global Sensitivity Analysis Methods D. Ji et al. 10.1002/2017JD027348
28 citations as recorded by crossref.
- Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model D. Williamson et al. 10.5194/gmd-10-1789-2017
- 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
- Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al. 10.1016/j.nhres.2024.11.004
- 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
- Adjoint‐Based Climate Model Tuning: Application to the Planet Simulator G. Lyu et al. 10.1002/2017MS001194
- 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
- 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
- The GAMIL3: Model Description and Evaluation L. Li et al. 10.1029/2020JD032574
- 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
- Impacts of uncertain cloud-related parameters on Pacific Walker circulation simulation in GAMIL2 F. XIE et al. 10.1080/16742834.2018.1392228
- Quantifying Parametric Uncertainty Effects on Tropical Cloud Fraction in an AGCM F. Xie et al. 10.1029/2022MS003221
- Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence T. Schneider et al. 10.5194/acp-24-7041-2024
- Stochastic pricing formulation for hybrid equity warrants T. Roslan et al. 10.3934/math.2022027
- 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
- Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
- 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
- Optimization and Evaluation of Stochastic Unified Convection Using Single‐Column Model Simulations at Multiple Observation Sites J. Shin & J. Baik 10.1029/2022MS003473
- Automated parameter tuning applied to sea ice in a global climate model L. Roach et al. 10.1007/s00382-017-3581-5
- 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
- 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
- A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation X. Wu et al. 10.3390/rs17030408
- Earth system model parameter adjustment using a Green's functions approach E. Strobach et al. 10.5194/gmd-15-2309-2022
- 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: 05 Apr 2025
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...
Special issue