Saudi Rainfall (SaRa): hourly 0.1° gridded rainfall (1979–present) for Saudi Arabia via machine learning fusion  of satellite and model data

Wang, Xuetong; Alharbi, Raied S.; Baez-Villanueva, Oscar M.; Green, Amy; McCabe, Matthew F.; Wada, Yoshihide; Van Dijk, Albert I. J. M.; Abid, Muhammad A.; Beck, Hylke E.

doi:https://doi.org/10.5194/hess-29-4983-2025

Articles | Volume 29, issue 19

https://doi.org/10.5194/hess-29-4983-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-29-4983-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 29, issue 19

Research article

|

08 Oct 2025

Research article |

| 08 Oct 2025

Saudi Rainfall (SaRa): hourly 0.1° gridded rainfall (1979–present) for Saudi Arabia via machine learning fusion of satellite and model data

Xuetong Wang, Raied S. Alharbi, Oscar M. Baez-Villanueva, Amy Green, Matthew F. McCabe, Yoshihide Wada, Albert I. J. M. Van Dijk, Muhammad A. Abid, and Hylke E. Beck

Data sets

SM2RAIN-ASCAT (2007-2022): global daily satellite rainfall from ASCAT soil moisture (2.1.2n) Luca Brocca et al. https://doi.org/10.5281/zenodo.10376109

SM2RAIN-CCI (1 Jan 1998 – 31 December 2015) global daily rainfall dataset (Version 2) Luca Ciabatta et al. https://doi.org/10.5281/zenodo.1305021

GPM+SM2RAIN (2007-2018): quasi-global 25km/daily rainfall product from the integration of GPM and SM2RAIN-based rainfall products (0.1.0) Christian Massari https://doi.org/10.5281/zenodo.3854817

Short summary

Our paper introduces Saudi Rainfall (SaRa), a high-resolution, near-real-time rainfall product for the Arabian Peninsula. Using machine learning, SaRa combines multiple satellite and (re)analysis datasets with static predictors, outperforming existing products in the region. With the fast development and continuing growth in water demand over this region, SaRa could help to address water challenges and support resource management.