Articles | Volume 7, issue 5
https://doi.org/10.5194/gmd-7-2333-2014
© 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-7-2333-2014
© 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
| 
14 Oct 2014
Development and technical paper |
| 14 Oct 2014
Multi-model ensemble: technique and validation
J. R. Rozante
Center for Weather Forecasting and Climate Studies, INPE, Cachoeira Paulista, SP, Brazil
D. S. Moreira
Center for Weather Forecasting and Climate Studies, INPE, Cachoeira Paulista, SP, Brazil
R. C. M. Godoy
Center for Weather Forecasting and Climate Studies, INPE, Cachoeira Paulista, SP, Brazil
A. A. Fernandes
Center for Weather Forecasting and Climate Studies, INPE, Cachoeira Paulista, SP, Brazil
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Cited
22 citations as recorded by crossref.
- Evaluation of subseasonal precipitation forecasts in the Uruguay River basin J. Badagian et al. https://doi.org/10.1002/joc.8634
- Subseasonal Forecast of Surface Air Temperature Using Superensemble Approaches: Experiments over Northeast Asia for 2018 S. Zhu et al. https://doi.org/10.1175/WAF-D-20-0096.1
- Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide A. Elshall et al. https://doi.org/10.3389/feart.2022.786223
- Improving the skill of medium range ensemble rainfall and temperature forecasts over India using MoES grand ensemble (MGE)-Part-II V. Abhijith et al. https://doi.org/10.1007/s00703-025-01107-6
- High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia H. Gu et al. https://doi.org/10.5194/hess-22-3087-2018
- Current and future potential of solar and wind energy over Africa using the RegCM4 CORDEX-CORE ensemble W. Sawadogo et al. https://doi.org/10.1007/s00382-020-05377-1
- Multi-Model Ensemble Prediction of Summer Precipitation in China Based on Machine Learning Algorithms J. Yang et al. https://doi.org/10.3390/atmos13091424
- Improving the skill of medium range ensemble rainfall forecasts over India using MoES grand ensemble (MGE)-part-I A. Dube et al. https://doi.org/10.1007/s00703-024-01035-x
- Ready, Set & Go! An anticipatory action system against droughts G. Guimarães Nobre et al. https://doi.org/10.5194/nhess-24-4661-2024
- Multi-output Deep Learning for Bus Arrival Time Predictions N. Petersen et al. https://doi.org/10.1016/j.trpro.2019.09.025
- Performance of statistical and machine learning ensembles for daily temperature downscaling X. Li et al. https://doi.org/10.1007/s00704-020-03098-3
- Assessing the coastal hazard of Medicane Ianos through ensemble modelling C. Ferrarin et al. https://doi.org/10.5194/nhess-23-2273-2023
- Improved forecasting of thermospheric densities using multi-model ensembles S. Elvidge et al. https://doi.org/10.5194/gmd-9-2279-2016
- Improving Estuarine Hydrodynamic Forecasts Through Numerical Model Ensembles I. Iglesias et al. https://doi.org/10.3389/fmars.2022.812255
- Evaluation and correction of sub-seasonal dynamic model forecast of precipitation in eastern China L. Liu et al. https://doi.org/10.1007/s00382-023-06788-6
- Performance‐based projection of the climate‐change effects on precipitation extremes in East Asia using two metrics S. Kwon et al. https://doi.org/10.1002/joc.5954
- Improving the Accuracy of Rainfall Prediction Using Bias-Corrected NMME Outputs: A Case Study of Surabaya City, Indonesia D. Faidah et al. https://doi.org/10.1155/2022/9779829
- Multi‐Model Ensembles for Upper Atmosphere Models S. Elvidge et al. https://doi.org/10.1029/2022SW003356
- Two Models Solutions for the Douro Estuary: Flood Risk Assessment and Breakwater Effects I. Iglesias et al. https://doi.org/10.1007/s12237-018-0477-5
- Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa S. Gebrechorkos et al. https://doi.org/10.1029/2021WR031480
- High-fidelity data-driven dynamics model for reinforcement learning-based control in HL-3 tokamak N. Wu et al. https://doi.org/10.1038/s42005-025-02302-y
- Applicability of Multimodel Ensemble Prediction of Heavy Precipitation for the Perm Region: A Case Study for the Summer of 2019 A. Vetrov & S. Kostarev https://doi.org/10.3103/S1068373921070037
22 citations as recorded by crossref.
- Evaluation of subseasonal precipitation forecasts in the Uruguay River basin J. Badagian et al. https://doi.org/10.1002/joc.8634
- Subseasonal Forecast of Surface Air Temperature Using Superensemble Approaches: Experiments over Northeast Asia for 2018 S. Zhu et al. https://doi.org/10.1175/WAF-D-20-0096.1
- Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide A. Elshall et al. https://doi.org/10.3389/feart.2022.786223
- Improving the skill of medium range ensemble rainfall and temperature forecasts over India using MoES grand ensemble (MGE)-Part-II V. Abhijith et al. https://doi.org/10.1007/s00703-025-01107-6
- High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia H. Gu et al. https://doi.org/10.5194/hess-22-3087-2018
- Current and future potential of solar and wind energy over Africa using the RegCM4 CORDEX-CORE ensemble W. Sawadogo et al. https://doi.org/10.1007/s00382-020-05377-1
- Multi-Model Ensemble Prediction of Summer Precipitation in China Based on Machine Learning Algorithms J. Yang et al. https://doi.org/10.3390/atmos13091424
- Improving the skill of medium range ensemble rainfall forecasts over India using MoES grand ensemble (MGE)-part-I A. Dube et al. https://doi.org/10.1007/s00703-024-01035-x
- Ready, Set & Go! An anticipatory action system against droughts G. Guimarães Nobre et al. https://doi.org/10.5194/nhess-24-4661-2024
- Multi-output Deep Learning for Bus Arrival Time Predictions N. Petersen et al. https://doi.org/10.1016/j.trpro.2019.09.025
- Performance of statistical and machine learning ensembles for daily temperature downscaling X. Li et al. https://doi.org/10.1007/s00704-020-03098-3
- Assessing the coastal hazard of Medicane Ianos through ensemble modelling C. Ferrarin et al. https://doi.org/10.5194/nhess-23-2273-2023
- Improved forecasting of thermospheric densities using multi-model ensembles S. Elvidge et al. https://doi.org/10.5194/gmd-9-2279-2016
- Improving Estuarine Hydrodynamic Forecasts Through Numerical Model Ensembles I. Iglesias et al. https://doi.org/10.3389/fmars.2022.812255
- Evaluation and correction of sub-seasonal dynamic model forecast of precipitation in eastern China L. Liu et al. https://doi.org/10.1007/s00382-023-06788-6
- Performance‐based projection of the climate‐change effects on precipitation extremes in East Asia using two metrics S. Kwon et al. https://doi.org/10.1002/joc.5954
- Improving the Accuracy of Rainfall Prediction Using Bias-Corrected NMME Outputs: A Case Study of Surabaya City, Indonesia D. Faidah et al. https://doi.org/10.1155/2022/9779829
- Multi‐Model Ensembles for Upper Atmosphere Models S. Elvidge et al. https://doi.org/10.1029/2022SW003356
- Two Models Solutions for the Douro Estuary: Flood Risk Assessment and Breakwater Effects I. Iglesias et al. https://doi.org/10.1007/s12237-018-0477-5
- Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa S. Gebrechorkos et al. https://doi.org/10.1029/2021WR031480
- High-fidelity data-driven dynamics model for reinforcement learning-based control in HL-3 tokamak N. Wu et al. https://doi.org/10.1038/s42005-025-02302-y
- Applicability of Multimodel Ensemble Prediction of Heavy Precipitation for the Perm Region: A Case Study for the Summer of 2019 A. Vetrov & S. Kostarev https://doi.org/10.3103/S1068373921070037
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