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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/npg-2019-65
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-65
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 10 Jan 2020

Submitted as: research article | 10 Jan 2020

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).

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

Maxime Taillardat1,2 and Olivier Mestre1,2 Maxime Taillardat and Olivier Mestre
  • 1Météo-France, Toulouse, France
  • 2CNRM UMR 3589, Toulouse, France

Abstract. Statistical post-processing of ensemble forecasts, from simple linear regressions to more sophisticated techniques, is now a well-known procedure in order to correct biased and misdispersed ensemble weather predictions. However, practical applications in National Weather Services is still in its infancy compared to deterministic post-processing. This paper presents two different applications of ensemble post-processing using machine learning at an industrial scale. The first is a station-based post-processing of surface temperature in a medium resolution ensemble system. The second is a gridded post-processing of hourly rainfall amounts in a high resolution ensemble prediction system. The techniques used rely on quantile regression forests (QRF) and ensemble copula coupling (ECC), chosen for their robustness and simplicity of training whatever the variable subject to calibration.

Moreover, some variants of classical techniques used such as QRF or ECC have been developed in order to adjust to operational constraints. A forecast anomaly-based QRF is used for temperature for a better prediction of cold and heat waves. A variant of ECC for hourly rainfall is built, accounting for more realistic longer rainfall accumulations. It is shown that forecast quality as well as forecast value is improved compared to the raw ensemble. At last, comments about model size and computation time are made.

Maxime Taillardat and Olivier Mestre
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Maxime Taillardat and Olivier Mestre
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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 applications in National Weather Services is still in its infancy. Two different applications of ensemble post-processing using machine learning at an industrial scale are presented. Forecast quality and value is improved compared t othe 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...
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