Articles | Volume 18
Adv. Sci. Res., 18, 145–156, 2021
https://doi.org/10.5194/asr-18-145-2021
Adv. Sci. Res., 18, 145–156, 2021
https://doi.org/10.5194/asr-18-145-2021
 
17 Sep 2021
17 Sep 2021

Addressing up-scaling methodologies for convection-permitting EPSs using statistical and machine learning tools

Tiziana Comito et al.

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Cited articles

Allen, S. and Ferro, C. A.: Regime-dependent statistical post-processing of ensemble forecasts, Q. J. Roy. Meteor. Soc., 145, 3535–3552, https://doi.org/10.1002/qj.3638, 2019. a
Bari, D. and Ouagabi, A.: Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts, SN Applied Sciences, 2, 1–13, https://doi.org/10.1007/s42452-020-2327-x, 2020. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, 2015. a
Ben Bouallègue, Z. and Theis, S. E.: Spatial techniques applied to precipitation ensemble forecasts: from verification results to probabilistic products, Meteorol. Appl., 21, 922–929, https://doi.org/10.1002/met.1435, 2014. a
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W., Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., et al.: The HARMONIE–AROME model configuration in the ALADIN–HIRLAM NWP system, Mon. Weather Rev., 145, 1919–1935, 2017. a, b
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Short summary
Convection-permitting models allow for prediction of rainfall events with increasing levels of detail. However, this increased resolution can create problems such as the so-called double penalty problem when attempting to verify model forecast accuracy. This problem is amplified when trying to maximise the value of a convection-permitting ensemble prediction system (EPS). Post-processing of the EPS can help to overcome these issues. In this spirit, two new up-scaling algorithms based on Machine