Articles | Volume 17, issue 11
https://doi.org/10.5194/gmd-17-4689-2024
https://doi.org/10.5194/gmd-17-4689-2024
Methods for assessment of models
 | 
13 Jun 2024
Methods for assessment of models |  | 13 Jun 2024

Multivariate adjustment of drizzle bias using machine learning in European climate projections

Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld

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Cited articles

Anagnostopoulou, C. and Tolika, K.: Extreme precipitation in Europe: statistical threshold selection based on climatological criteria, Theor. Appl. Climatol., 107, 479–489, 2012. a
Argüeso, D., Evans, J. P., and Fita, L.: Precipitation bias correction of very high resolution regional climate models, Hydrol. Earth Syst. Sci., 17, 4379–4388, https://doi.org/10.5194/hess-17-4379-2013, 2013. a, b
Baigorria, G. A., Jones, J. W., Shin, D.-W., Mishra, A., and O’Brien, J. J.: Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs, Clim. Res., 34, 211–222, 2007. a
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Breiman, L.: Random forests, Mach Learn., 45, 5–32, 2001b. a
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Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
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