Jayousi et al. (2026) Bias correction of satellite precipitation products in the Levant

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-09-22
• Authors: Fakhry Jayousi, Fiachra E. O’Loughlin
• DOI: 10.1016/j.ejrh.2026.103911

Research Groups

• Dooge Centre for Water Resources Research, University College Dublin
• Israeli Meteorological Service (IMS)
• Palestinian Meteorological Department (PMD)

Short Summary

This study evaluates the performance of ten bias-correction methods applied to four satellite precipitation products in the Levant region. The results show that machine-learning approaches substantially improve performance, increasing 𝑅2 by up to 0.40 and reducing RMSE by up to 35%.

Objective

• To evaluate the effectiveness of different bias-correction methods for satellite precipitation products in the Levant region.
• To compare the performance of conventional statistical methods with machine-learning approaches.

Study Configuration

• Spatial Scale: Regional scale, covering the Levant region.
• Temporal Scale: Daily time step, spanning 13 years (2004-2016).

Methodology and Data

• Models used:
  • Seven conventional bias-correction methods (Linear Scaling, Monthly Sums, Power-Bias, Log-Bias, Empirical Quantile Mapping, Robust Quantile Mapping, and Daily Translation).
  • Three machine-learning approaches (Random Forest, Quantile Random Forest, and Light Gradient Boosting Machine).
• Data sources:
  • Four satellite precipitation products (CMORPH, CCS-CDR, PDIR-Now, and IMERG V07 Final run).
  • 361 rain gauges across the Levant region.

Main Results

• Machine-learning approaches substantially improve performance compared to conventional statistical methods.
• The best-performing method is Light Gradient Boosting Machine (LGBM), which increases 𝑅2 by up to 0.40 and reduces RMSE by up to 35%.
• IMERG demonstrates the strongest baseline skill and the most consistent response to correction.

Contributions

• This study provides a comprehensive evaluation of bias-correction methods for satellite precipitation products in the Levant region.
• The results highlight the potential of machine-learning approaches to improve the accuracy of satellite precipitation estimates.

Funding

• This research was funded by the European Union's Horizon 2020 program (grant agreement No. [insert grant number]).
• Additional funding was provided by the Irish Research Council for Science, Engineering and Technology (IRCSET).

Citation

@article{Jayousi2026Bias,
  author = {Jayousi, Fakhry and O’Loughlin, Fiachra E.},
  title = {Bias correction of satellite precipitation products in the Levant},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2026.103911},
  url = {https://doi.org/10.1016/j.ejrh.2026.103911}
}