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

Related authors

Assessing future streamflow in Cyprus through hydrological model calibration under non-stationary climate and regional climate model ensemble selection
Ioannis Sofokleous, George Zittis, Gerald Dörflinger, and Adriana Bruggeman
Hydrol. Earth Syst. Sci., 30, 3903–3924, https://doi.org/10.5194/hess-30-3903-2026,https://doi.org/10.5194/hess-30-3903-2026, 2026
Short summary
Modelling the deep convective transport of trace gases (CO, NH3 and SO2) from the planetary boundary layer to the Asian summer monsoon anticyclone
Jianzhong Ma, Bin Chen, Qianshan He, Xiaolu Yan, Gaili Wang, Siyang Cheng, Benedikt Steil, Christoph Brühl, Holger Tost, Michael Höpfner, Andrea Pozzer, and Jos Lelieveld
Atmos. Chem. Phys., 26, 8125–8144, https://doi.org/10.5194/acp-26-8125-2026,https://doi.org/10.5194/acp-26-8125-2026, 2026
Short summary
A Multi-Criteria Framework for CORDEX-CORE2 GCM Selection
Moetasim Ashfaq, Erika Coppola, Chris Lennard, Claas Teichmann, Deeksha Rastogi, Elias Massoud, Erasmo Buonomo, Eun-Soon Im, George Zittis, Jason P. Evans, Jesus Fernandez, Katherine J. Evans, Maria Leidinice da Silva, Marianna Adinolfi, Melissa Bukovsky, Rosmeri Porfirio da Rocha, Shabeh ul Hasson, Silvina A. Solman, Stefan Sobolowski, Sushant Das, Swen Brands, Tereza Cavazos, Thanh Ngo-Duc, and Xuejie Gao
EGUsphere, https://doi.org/10.5194/egusphere-2026-2649,https://doi.org/10.5194/egusphere-2026-2649, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Chiral volatile organic compound fluxes from soil in the Amazon Rainforest across seasons
Johanna Margaretha Schüttler, Giovanni Pugliese, Joseph Byron, Cléo Quaresma Dias-Júnior, Carolina de A. Monteiro, Hartwig Harder, Jos Lelieveld, and Jonathan Williams
Biogeosciences, 23, 3467–3498, https://doi.org/10.5194/bg-23-3467-2026,https://doi.org/10.5194/bg-23-3467-2026, 2026
Short summary
A small-footprint Cavity Ring-Down Spectroscopy instrument for in-situ measurements of NO3 and N2O5
Gunther N. T. E. Türk, Simone T. Andersen, Patrick Dewald, Jan Schuladen, Jos Lelieveld, and John N. Crowley
EGUsphere, https://doi.org/10.5194/egusphere-2026-2487,https://doi.org/10.5194/egusphere-2026-2487, 2026
Short summary

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
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001a. a
Breiman, L.: Random forests, Mach Learn., 45, 5–32, 2001b. a
Download
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.
Share