Articles | Volume 27, issue 2
https://doi.org/10.5194/npg-27-329-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/npg-27-329-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 May 2020
Research article |
| 29 May 2020
| 29 May 2020
From research to applications – examples of operational ensemble post-processing in France using machine learning
Maxime Taillardat
CORRESPONDING AUTHOR
Météo-France, Toulouse, France
CNRM UMR 3589, Toulouse, France
Olivier Mestre
Météo-France, Toulouse, France
CNRM UMR 3589, Toulouse, France
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- Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts M. Lakatos & S. Baran https://doi.org/10.1002/met.70015
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- Short-Term Wind Speed Forecasts over the Pearl River Estuary: Numerical Model Evaluation and Deterministic Post-Processing X. SUN et al. https://doi.org/10.3724/j.1006-8775.2024.035
- Vers la généralisation de la prévision hydrologique probabiliste au sein du réseau Vigicrues : estimation, évaluation et communication A. Belleudy et al. https://doi.org/10.1080/27678490.2024.2374079
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- Neighborhood-Based Ensemble Evaluation Using the CRPS J. Stein & F. Stoop https://doi.org/10.1175/MWR-D-21-0224.1
- Radar-based high-resolution ensemble precipitation analyses over the French Alps M. Vernay et al. https://doi.org/10.5194/amt-18-1731-2025
- Improving the blend of multiple weather forecast sources by Reliability Calibration F. Rust et al. https://doi.org/10.1002/met.2142
- A flexible extended generalized Pareto distribution for tail estimation P. Gamet & J. Jalbert https://doi.org/10.1002/env.2744
- Statistical post-processing of multiple meteorological elements using the multimodel integration embedded method X. Ma et al. https://doi.org/10.1016/j.atmosres.2024.107269
- Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing M. Taillardat https://doi.org/10.3390/atmos12080966
- Lead‐time‐continuous statistical postprocessing of ensemble weather forecasts J. Wessel et al. https://doi.org/10.1002/qj.4701
- Multivariable neural network to postprocess short‐term, hub‐height wind forecasts A. Salazar et al. https://doi.org/10.1002/ese3.928
- Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models K. Nikhil Teja et al. https://doi.org/10.1016/j.jhydrol.2023.130176
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- Bridging the gap between ensemble forecasting and end-user needs for decision-making on high-impact events M. Ponzano et al. https://doi.org/10.5194/asr-22-39-2025
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. https://doi.org/10.5194/npg-27-519-2020
- Achieving skilled and reliable daily probabilistic forecasts of wind power at subseasonal-to-seasonal timescales over France E. Lindas et al. https://doi.org/10.1088/2753-3751/ae5ca6
- A parsimonious tail compliant multiscale statistical model for aggregated rainfall P. Ailliot et al. https://doi.org/10.1016/j.advwatres.2026.105216
- Calibration of precipitation forecasts from NWP models for ungauged locations Y. Du et al. https://doi.org/10.1016/j.jhydrol.2025.133733
- A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast M. Alessi & A. DeGaetano https://doi.org/10.1002/met.1976
- Machine learning for precipitation forecasts post-processing — Multi-model comparison and experimental investigation Y. Zhang & A. Ye https://doi.org/10.1175/JHM-D-21-0096.1
- Evaluating probabilistic classifiers: The triptych T. Dimitriadis et al. https://doi.org/10.1016/j.ijforecast.2023.09.007
- Spatio-temporal point process intensity estimation using zero-deflated subsampling applied to a lightning strikes dataset in France J. Coeurjolly et al. https://doi.org/10.1214/24-EJS2325
- A framework for probabilistic weather forecast post-processing across models and lead times using machine learning C. Kirkwood et al. https://doi.org/10.1098/rsta.2020.0099
30 citations as recorded by crossref.
- Quantile Regression Forests for Predictor Selection in Standardized Anomaly Postprocessing P. Liu et al. https://doi.org/10.2151/sola.2025-017
- Truncated generalized extreme value distribution‐based ensemble model output statistics model for calibration of wind speed ensemble forecasts S. Baran et al. https://doi.org/10.1002/env.2678
- Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts M. Lakatos & S. Baran https://doi.org/10.1002/met.70015
- Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network B. Cluzet et al. https://doi.org/10.5194/tc-16-1281-2022
- Proper scoring rules for multivariate probabilistic forecasts based on aggregation and transformation R. Pic et al. https://doi.org/10.5194/ascmo-11-23-2025
- Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
- Post‐processing output from ensembles with and without parametrised convection, to create accurate, blended, high‐fidelity rainfall forecasts E. Gascón et al. https://doi.org/10.1002/qj.4753
- Short-Term Wind Speed Forecasts over the Pearl River Estuary: Numerical Model Evaluation and Deterministic Post-Processing X. SUN et al. https://doi.org/10.3724/j.1006-8775.2024.035
- Vers la généralisation de la prévision hydrologique probabiliste au sein du réseau Vigicrues : estimation, évaluation et communication A. Belleudy et al. https://doi.org/10.1080/27678490.2024.2374079
- Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics G. Evin et al. https://doi.org/10.5194/npg-28-467-2021
- Neighborhood-Based Ensemble Evaluation Using the CRPS J. Stein & F. Stoop https://doi.org/10.1175/MWR-D-21-0224.1
- Radar-based high-resolution ensemble precipitation analyses over the French Alps M. Vernay et al. https://doi.org/10.5194/amt-18-1731-2025
- Improving the blend of multiple weather forecast sources by Reliability Calibration F. Rust et al. https://doi.org/10.1002/met.2142
- A flexible extended generalized Pareto distribution for tail estimation P. Gamet & J. Jalbert https://doi.org/10.1002/env.2744
- Statistical post-processing of multiple meteorological elements using the multimodel integration embedded method X. Ma et al. https://doi.org/10.1016/j.atmosres.2024.107269
- Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing M. Taillardat https://doi.org/10.3390/atmos12080966
- Lead‐time‐continuous statistical postprocessing of ensemble weather forecasts J. Wessel et al. https://doi.org/10.1002/qj.4701
- Multivariable neural network to postprocess short‐term, hub‐height wind forecasts A. Salazar et al. https://doi.org/10.1002/ese3.928
- Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models K. Nikhil Teja et al. https://doi.org/10.1016/j.jhydrol.2023.130176
- Multi‐objective downscaling of precipitation time series by genetic programming T. Zerenner et al. https://doi.org/10.1002/joc.7172
- Bridging the gap between ensemble forecasting and end-user needs for decision-making on high-impact events M. Ponzano et al. https://doi.org/10.5194/asr-22-39-2025
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. https://doi.org/10.5194/npg-27-519-2020
- Achieving skilled and reliable daily probabilistic forecasts of wind power at subseasonal-to-seasonal timescales over France E. Lindas et al. https://doi.org/10.1088/2753-3751/ae5ca6
- A parsimonious tail compliant multiscale statistical model for aggregated rainfall P. Ailliot et al. https://doi.org/10.1016/j.advwatres.2026.105216
- Calibration of precipitation forecasts from NWP models for ungauged locations Y. Du et al. https://doi.org/10.1016/j.jhydrol.2025.133733
- A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast M. Alessi & A. DeGaetano https://doi.org/10.1002/met.1976
- Machine learning for precipitation forecasts post-processing — Multi-model comparison and experimental investigation Y. Zhang & A. Ye https://doi.org/10.1175/JHM-D-21-0096.1
- Evaluating probabilistic classifiers: The triptych T. Dimitriadis et al. https://doi.org/10.1016/j.ijforecast.2023.09.007
- Spatio-temporal point process intensity estimation using zero-deflated subsampling applied to a lightning strikes dataset in France J. Coeurjolly et al. https://doi.org/10.1214/24-EJS2325
- A framework for probabilistic weather forecast post-processing across models and lead times using machine learning C. Kirkwood et al. https://doi.org/10.1098/rsta.2020.0099
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
Statistical post-processing of ensemble forecasts is now a well-known procedure in order to correct biased and misdispersed ensemble weather predictions. But practical application in European national weather services is in its infancy. Different applications of ensemble post-processing using machine learning at an industrial scale are presented. Forecast quality and value are improved compared to the raw ensemble, but several facilities have to be made to adjust to operational constraints.
Statistical post-processing of ensemble forecasts is now a well-known procedure in order to...
