Optimization of Statistical Metrics for Satellite Precipitation Products: Towards Improved Hydrological Modeling
A special issue of Atmosphere (ISSN 2073-4433).
Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 3433
Special Issue Editors
Interests: hydrology; hydrogeology; water resources management; hydrological modeling
Interests: environmental impact assessment
Interests: African geology; structural geology; petrology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Elevate hydrological modeling with precisely optimized satellite precipitation data! Refining statistical measures is crucial to enhancing the accuracy and reliability of rainfall information collected from space. By systematically improving metrics such as RMSE and bias, we enhance the accuracy of how satellite products align with ground rainfall measurements.
This crucial optimization leads to more reliable flood predictions, informed climate analysis, and enhanced water resource management. The integration of advanced techniques, including machine learning and multi-sensor fusion, further sharpens these datasets. The outcome is more robust hydrological simulations, empowering better-informed decisions for critical water-related challenges and a deeper understanding of weather extremes
Dr. Nour-Eddine Laftouhi
Prof. Dr. Lahoucine Hanich
Prof. Dr. Hassan Ibouh
Guest Editors
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Keywords
- satellite precipitation products
- statistical metrics optimization
- hydrological modeling
- bias correction
- error metrics (RMSE, POD, FAR)
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