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
Viewed
Total article views: 3,489 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Jan 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,587 | 850 | 52 | 3,489 | 103 | 65 | 56 |
- HTML: 2,587
- PDF: 850
- XML: 52
- Total: 3,489
- Supplement: 103
- BibTeX: 65
- EndNote: 56
Total article views: 2,934 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 29 May 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,281 | 608 | 45 | 2,934 | 103 | 57 | 47 |
- HTML: 2,281
- PDF: 608
- XML: 45
- Total: 2,934
- Supplement: 103
- BibTeX: 57
- EndNote: 47
Total article views: 555 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Jan 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 306 | 242 | 7 | 555 | 8 | 9 |
- HTML: 306
- PDF: 242
- XML: 7
- Total: 555
- BibTeX: 8
- EndNote: 9
Viewed (geographical distribution)
Total article views: 3,489 (including HTML, PDF, and XML)
Thereof 2,949 with geography defined
and 540 with unknown origin.
Total article views: 2,934 (including HTML, PDF, and XML)
Thereof 2,517 with geography defined
and 417 with unknown origin.
Total article views: 555 (including HTML, PDF, and XML)
Thereof 432 with geography defined
and 123 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
18 citations as recorded by crossref.
- Multivariable neural network to postprocess short‐term, hub‐height wind forecasts A. Salazar et al. 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. 10.1016/j.jhydrol.2023.130176
- Multi‐objective downscaling of precipitation time series by genetic programming T. Zerenner et al. 10.1002/joc.7172
- Truncated generalized extreme value distribution‐based ensemble model output statistics model for calibration of wind speed ensemble forecasts S. Baran et al. 10.1002/env.2678
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. 10.5194/npg-27-519-2020
- 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. 10.5194/tc-16-1281-2022
- Post‐processing output from ensembles with and without parametrised convection, to create accurate, blended, high‐fidelity rainfall forecasts E. Gascón et al. 10.1002/qj.4753
- A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast M. Alessi & A. DeGaetano 10.1002/met.1976
- Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics G. Evin et al. 10.5194/npg-28-467-2021
- Neighborhood-Based Ensemble Evaluation Using the CRPS J. Stein & F. Stoop 10.1175/MWR-D-21-0224.1
- Machine learning for precipitation forecasts post-processing — Multi-model comparison and experimental investigation Y. Zhang & A. Ye 10.1175/JHM-D-21-0096.1
- Improving the blend of multiple weather forecast sources by Reliability Calibration F. Rust et al. 10.1002/met.2142
- Evaluating probabilistic classifiers: The triptych T. Dimitriadis et al. 10.1016/j.ijforecast.2023.09.007
- A framework for probabilistic weather forecast post-processing across models and lead times using machine learning C. Kirkwood et al. 10.1098/rsta.2020.0099
- A flexible extended generalized Pareto distribution for tail estimation P. Gamet & J. Jalbert 10.1002/env.2744
- Statistical post-processing of multiple meteorological elements using the multimodel integration embedded method X. Ma et al. 10.1016/j.atmosres.2024.107269
- Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing M. Taillardat 10.3390/atmos12080966
- Lead‐time‐continuous statistical postprocessing of ensemble weather forecasts J. Wessel et al. 10.1002/qj.4701
18 citations as recorded by crossref.
- Multivariable neural network to postprocess short‐term, hub‐height wind forecasts A. Salazar et al. 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. 10.1016/j.jhydrol.2023.130176
- Multi‐objective downscaling of precipitation time series by genetic programming T. Zerenner et al. 10.1002/joc.7172
- Truncated generalized extreme value distribution‐based ensemble model output statistics model for calibration of wind speed ensemble forecasts S. Baran et al. 10.1002/env.2678
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. 10.5194/npg-27-519-2020
- 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. 10.5194/tc-16-1281-2022
- Post‐processing output from ensembles with and without parametrised convection, to create accurate, blended, high‐fidelity rainfall forecasts E. Gascón et al. 10.1002/qj.4753
- A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast M. Alessi & A. DeGaetano 10.1002/met.1976
- Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics G. Evin et al. 10.5194/npg-28-467-2021
- Neighborhood-Based Ensemble Evaluation Using the CRPS J. Stein & F. Stoop 10.1175/MWR-D-21-0224.1
- Machine learning for precipitation forecasts post-processing — Multi-model comparison and experimental investigation Y. Zhang & A. Ye 10.1175/JHM-D-21-0096.1
- Improving the blend of multiple weather forecast sources by Reliability Calibration F. Rust et al. 10.1002/met.2142
- Evaluating probabilistic classifiers: The triptych T. Dimitriadis et al. 10.1016/j.ijforecast.2023.09.007
- A framework for probabilistic weather forecast post-processing across models and lead times using machine learning C. Kirkwood et al. 10.1098/rsta.2020.0099
- A flexible extended generalized Pareto distribution for tail estimation P. Gamet & J. Jalbert 10.1002/env.2744
- Statistical post-processing of multiple meteorological elements using the multimodel integration embedded method X. Ma et al. 10.1016/j.atmosres.2024.107269
- Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing M. Taillardat 10.3390/atmos12080966
- Lead‐time‐continuous statistical postprocessing of ensemble weather forecasts J. Wessel et al. 10.1002/qj.4701
Latest update: 29 Jun 2024
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...
