Articles | Volume 27, issue 2
Nonlin. Processes Geophys., 27, 329–347, 2020
https://doi.org/10.5194/npg-27-329-2020

Special issue: Advances in post-processing and blending of deterministic...

Nonlin. Processes Geophys., 27, 329–347, 2020
https://doi.org/10.5194/npg-27-329-2020

Research article 29 May 2020

Research article | 29 May 2020

From research to applications – examples of operational ensemble post-processing in France using machine learning

Maxime Taillardat and Olivier Mestre

Related authors

Calibrated ensemble forecasts of the height of new snow using quantile regression forests and Ensemble Model Output Statistics
Guillaume Evin, Matthieu Lafaysse, Maxime Taillardat, and Michaël Zamo
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2021-18,https://doi.org/10.5194/npg-2021-18, 2021
Preprint under review for NPG
Short summary
Preface: Advances in post-processing and blending of deterministic and ensemble forecasts
Stephan Hemri, Sebastian Lerch, Maxime Taillardat, Stéphane Vannitsem, and Daniel S. Wilks
Nonlin. Processes Geophys., 27, 519–521, https://doi.org/10.5194/npg-27-519-2020,https://doi.org/10.5194/npg-27-519-2020, 2020

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Training a convolutional neural network to conserve mass in data assimilation
Yvonne Ruckstuhl, Tijana Janjić, and Stephan Rasp
Nonlin. Processes Geophys., 28, 111–119, https://doi.org/10.5194/npg-28-111-2021,https://doi.org/10.5194/npg-28-111-2021, 2021
Short summary
Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network
Ashesh Chattopadhyay, Pedram Hassanzadeh, and Devika Subramanian
Nonlin. Processes Geophys., 27, 373–389, https://doi.org/10.5194/npg-27-373-2020,https://doi.org/10.5194/npg-27-373-2020, 2020
Short summary

Cited articles

Athey, S., Tibshirani, J., and Wager, S.: Generalized random forests, Ann. Stat., 47, 1148–1178, 2019. a
Baran, S. and Lerch, S.: Combining predictive distributions for the statistical post-processing of ensemble forecasts, Int. J. Forecast., 34, 477–496, 2018. a
Barry, R. G.: Mountain weather and climate, London and New York, Routledge, 2nd edn., 2008. a
Bellier, J., Bontron, G., and Zin, I.: Using meteorological analogues for reordering postprocessed precipitation ensembles in hydrological forecasting, Water Resour. Res., 53, 10085–10107, 2017. a
Bellier, J., Zin, I., and Bontron, G.: Generating Coherent Ensemble Forecasts After Hydrological Postprocessing: Adaptations of ECC-Based Methods, Water Resour. Res., 54, 5741–5762, 2018. a
Download
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.