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Atmosphere
Volume 16
Issue 11
10.3390/atmos16111234
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco

by
Achraf Chakri
1,
Sana Abakarim
2,
João C. Antunes Rodrigues
3,
Nour-Eddine Laftouhi
1,*,
Hassan Ibouh
4,
Lahcen Zouhri
5 and
Elena Zaitseva
6
1
GéoSciences Semlalia-Unit, Laboratory of Water Sciences, Microbial Biotechnologies, and Natural Resources Sustainability (AQUABIOTECH), Cadi Ayyad University, Av Abdelkrim Khattabi, BP 511, Marrakesh 40000, Morocco
2
Computer and Systems Engineering Laboratory, Faculty of Science and Techniques, Cadi Ayyad University, Bd. A. Khattabi, BP 549, Marrakesh 40000, Morocco
3
Research Center for Asset Management and Systems Engineering (RCM2+), Faculty of Engineering, Lusófona University, 1749-024 Lisbon, Portugal
4
L3G Laboratory, Department of Earth Sciences, Faculty of Science and Techniques, Cadi Ayyad University, Bd. A. Khattabi, BP 549, Marrakesh 40000, Morocco
5
AGHYLE, Institut Polytechnique UniLaSalle Beauvais, 19 Rue Pierre Waguet, 60000 Beauvais, France
6
Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, 01026 Zilina, Slovakia
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(11), 1234; https://doi.org/10.3390/atmos16111234 (registering DOI)
Submission received: 12 September 2025 / Revised: 20 October 2025 / Accepted: 23 October 2025 / Published: 26 October 2025
(This article belongs to the Special Issue Optimization of Statistical Metrics for Satellite Precipitation Products: Towards Improved Hydrological Modeling)
Versions Notes

Abstract

Accurate precipitation data are essential for effective water resource management. This study aimed to correct precipitation values from the ERA5_Ag reanalysis dataset using observational data from 20 meteorological stations located in the Tensift basin, Morocco. Five machine learning models were evaluated: MLP, XGBoost, CatBoost, LightGBM, and Random Forest. Model performance was assessed using RMSE, MAE, R², and bias metrics, enabling the selection of the best−performing model to apply the correction. The results showed significant improvements in the accuracy of precipitation estimates, with R² ranging between 0.80 and 0.90 in most stations. The best model was subsequently used to correct and generate raster maps of corrected precipitation over 42 years, providing a spatially detailed tool of great value for water resource management. This study is particularly important in semi−arid regions such as the Tensift basin, where water scarcity demands more accurate and informed decision−making.
Keywords: machine learning; ERA5_Ag precipitation dataset; bias correction; corrected time series; statistical metrics; raster correction; Tensift basin; semi-arid machine learning; ERA5_Ag precipitation dataset; bias correction; corrected time series; statistical metrics; raster correction; Tensift basin; semi-arid

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MDPI and ACS Style

Chakri, A.; Abakarim, S.; Rodrigues, J.C.A.; Laftouhi, N.-E.; Ibouh, H.; Zouhri, L.; Zaitseva, E. Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco. Atmosphere 2025, 16, 1234. https://doi.org/10.3390/atmos16111234

AMA Style

Chakri A, Abakarim S, Rodrigues JCA, Laftouhi N-E, Ibouh H, Zouhri L, Zaitseva E. Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco. Atmosphere. 2025; 16(11):1234. https://doi.org/10.3390/atmos16111234

Chicago/Turabian Style

Chakri, Achraf, Sana Abakarim, João C. Antunes Rodrigues, Nour-Eddine Laftouhi, Hassan Ibouh, Lahcen Zouhri, and Elena Zaitseva. 2025. "Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco" Atmosphere 16, no. 11: 1234. https://doi.org/10.3390/atmos16111234

APA Style

Chakri, A., Abakarim, S., Rodrigues, J. C. A., Laftouhi, N.-E., Ibouh, H., Zouhri, L., & Zaitseva, E. (2025). Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco. Atmosphere, 16(11), 1234. https://doi.org/10.3390/atmos16111234

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