Articles | Volume 7, issue 5
https://doi.org/10.5194/gmd-7-1889-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-1889-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
02 Sep 2014
Development and technical paper |
| 02 Sep 2014
Optimization of NWP model closure parameters using total energy norm of forecast error as a target
P. Ollinaho
Finnish Meteorological Institute, Helsinki, Finland
University of Helsinki, Department of Physics, Helsinki, Finland
H. Järvinen
University of Helsinki, Department of Physics, Helsinki, Finland
European Centre for Medium-Range Weather Forecasts, Reading, UK
Finnish Meteorological Institute, Helsinki, Finland
P. Bechtold
European Centre for Medium-Range Weather Forecasts, Reading, UK
J. Susiluoto
Finnish Meteorological Institute, Helsinki, Finland
University of Helsinki, Department of Physics, Helsinki, Finland
H. Haario
Lappeenranta University of Technology, Lappeenranta, Finland
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Cited
19 citations as recorded by crossref.
- Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations Y. Qian et al. https://doi.org/10.1029/2018JD028927
- Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0 P. Ollinaho et al. https://doi.org/10.5194/gmd-14-2143-2021
- A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF C. Marzban et al. https://doi.org/10.1175/MWR-D-19-0271.1
- Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model Y. Qian et al. https://doi.org/10.1007/s00382-023-06977-3
- Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area Z. Di et al. https://doi.org/10.1007/s00382-017-3729-3
- Calibration of WRF model parameters using multiobjective adaptive surrogate model-based optimization to improve the prediction of the Indian summer monsoon S. Chinta & C. Balaji https://doi.org/10.1007/s00382-020-05288-1
- Sensitivity Analysis of the Spatial Structure of Forecasts in Mesoscale Models: Continuous Model Parameters C. Marzban et al. https://doi.org/10.1175/MWR-D-17-0275.1
- Sensitivity Analysis of the Spatial Structure of Forecasts in Mesoscale Models: Noncontinuous Model Parameters C. Marzban et al. https://doi.org/10.1175/MWR-D-19-0321.1
- Model Uncertainty Representation in a Convection-Permitting Ensemble—SPP and SPPT in HarmonEPS I. Frogner et al. https://doi.org/10.1175/MWR-D-21-0099.1
- A methodology for sensitivity analysis of spatial features in forecasts: the stochastic kinetic energy backscatter scheme C. Marzban et al. https://doi.org/10.1002/met.1775
- Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example L. Tuppi et al. https://doi.org/10.5194/gmd-13-5799-2020
- Hybrid Adaptive Framework for Enhanced Weather Prediction Using Machine Learning B. Harichandana et al. https://doi.org/10.2139/ssrn.5083732
- Efficient Bayesian inference for large chaotic dynamical systems S. Springer et al. https://doi.org/10.5194/gmd-14-4319-2021
- Knee Point‐Based Multiobjective Optimization for the Numerical Weather Prediction Model in the Greater Beijing Area H. Wang et al. https://doi.org/10.1029/2023GL104330
- On the effect of model parameters on forecast objects C. Marzban et al. https://doi.org/10.5194/gmd-11-1577-2018
- Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3 S. Tett et al. https://doi.org/10.5194/gmd-10-3567-2017
- Online identification of large-scale chaotic system V. Shemyakin & H. Haario https://doi.org/10.1007/s11071-018-4239-5
- Surrogate-based model parameter optimization in simulations of the West African monsoon M. Fischer et al. https://doi.org/10.5194/wcd-6-113-2025
- Bayesian inverse problems with conditional Sinkhorn generative adversarial networks in least volume latent spaces Q. Chen et al. https://doi.org/10.1016/j.neunet.2025.107740
19 citations as recorded by crossref.
- Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations Y. Qian et al. https://doi.org/10.1029/2018JD028927
- Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0 P. Ollinaho et al. https://doi.org/10.5194/gmd-14-2143-2021
- A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF C. Marzban et al. https://doi.org/10.1175/MWR-D-19-0271.1
- Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model Y. Qian et al. https://doi.org/10.1007/s00382-023-06977-3
- Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area Z. Di et al. https://doi.org/10.1007/s00382-017-3729-3
- Calibration of WRF model parameters using multiobjective adaptive surrogate model-based optimization to improve the prediction of the Indian summer monsoon S. Chinta & C. Balaji https://doi.org/10.1007/s00382-020-05288-1
- Sensitivity Analysis of the Spatial Structure of Forecasts in Mesoscale Models: Continuous Model Parameters C. Marzban et al. https://doi.org/10.1175/MWR-D-17-0275.1
- Sensitivity Analysis of the Spatial Structure of Forecasts in Mesoscale Models: Noncontinuous Model Parameters C. Marzban et al. https://doi.org/10.1175/MWR-D-19-0321.1
- Model Uncertainty Representation in a Convection-Permitting Ensemble—SPP and SPPT in HarmonEPS I. Frogner et al. https://doi.org/10.1175/MWR-D-21-0099.1
- A methodology for sensitivity analysis of spatial features in forecasts: the stochastic kinetic energy backscatter scheme C. Marzban et al. https://doi.org/10.1002/met.1775
- Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example L. Tuppi et al. https://doi.org/10.5194/gmd-13-5799-2020
- Hybrid Adaptive Framework for Enhanced Weather Prediction Using Machine Learning B. Harichandana et al. https://doi.org/10.2139/ssrn.5083732
- Efficient Bayesian inference for large chaotic dynamical systems S. Springer et al. https://doi.org/10.5194/gmd-14-4319-2021
- Knee Point‐Based Multiobjective Optimization for the Numerical Weather Prediction Model in the Greater Beijing Area H. Wang et al. https://doi.org/10.1029/2023GL104330
- On the effect of model parameters on forecast objects C. Marzban et al. https://doi.org/10.5194/gmd-11-1577-2018
- Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3 S. Tett et al. https://doi.org/10.5194/gmd-10-3567-2017
- Online identification of large-scale chaotic system V. Shemyakin & H. Haario https://doi.org/10.1007/s11071-018-4239-5
- Surrogate-based model parameter optimization in simulations of the West African monsoon M. Fischer et al. https://doi.org/10.5194/wcd-6-113-2025
- Bayesian inverse problems with conditional Sinkhorn generative adversarial networks in least volume latent spaces Q. Chen et al. https://doi.org/10.1016/j.neunet.2025.107740
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