Abdelrazaq et al. (2025) Benchmarking MSWEP Precipitation Accuracy in Arid Zones Against Traditional and Satellite Measurements

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-12-26
• Authors: Abdulrahman Saeed Abdelrazaq, Humaid Abdulla Alnuaimi, Faisal Baig, Mohamed Elkollaly, Mohsen Sherif
• DOI: 10.3390/rs18010095

Research Groups

Not explicitly mentioned in the provided text. The study focuses on evaluating precipitation datasets across the United Arab Emirates.

Short Summary

This study assesses the performance of the MSWEP v2.8 precipitation dataset against ground gauges and three satellite products (CMORPH, IMERG, GSMaP) in the arid United Arab Emirates from 2004 to 2020, finding moderate overall performance but significant biases (overestimation of light rainfall, underestimation of extreme events, and seasonal variations) that necessitate bias correction for hydrological applications.

Objective

• To assess the performance of the Multi-Source Weighted-Ensemble Precipitation v2.8 (MSWEP) dataset against ground-based gauge data and three satellite precipitation products (CMORPH, IMERG, and GSMaP) across the United Arab Emirates (UAE).

Study Configuration

• Spatial Scale: United Arab Emirates (UAE)
• Temporal Scale: 17 years (2004 to 2020)

Methodology and Data

• Models used:
  • Multi-Source Weighted-Ensemble Precipitation v2.8 (MSWEP)
  • CMORPH (Climate Prediction Center Morphing Technique)
  • IMERG (Integrated Multi-satellitE Retrievals for GPM)
  • GSMaP (Global Satellite Mapping of Precipitation)
• Data sources:
  • Ground-based gauge data
  • Satellite precipitation products (CMORPH, IMERG, GSMaP, MSWEP)

Main Results

• MSWEP shows a moderate correlation with gauge data (mean Correlation Coefficient, CC = 0.62).
• MSWEP performs better than CMORPH (CC = 0.54) but below IMERG (CC = 0.68).
• MSWEP yields lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to CMORPH and GSMaP.
• MSWEP overestimates light rainfall and underestimates extreme events, reflected in a lower Kling-Gupta Efficiency (KGE = 0.42).
• MSWEP shows weak performance in the 95th percentile rainfall, particularly in coastal and mountainous areas.
• Seasonal analysis reveals MSWEP overestimation in winter and underestimation during summer convective storms.

Contributions

• Provides a comprehensive evaluation of the MSWEP v2.8 dataset's performance in an arid environment (UAE), highlighting its strengths and specific biases.
• Quantifies the comparative performance of MSWEP against other widely used satellite precipitation products and ground-based gauges in a data-scarce region.
• Identifies critical biases of MSWEP, such as overestimation of light rainfall, underestimation of extreme events, and seasonal discrepancies, which are crucial for its application in arid hydrology.
• Emphasizes the necessity for bias correction and the integration of multiple datasets to improve rainfall estimation accuracy for hydrological and climate-related applications in arid regions.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Abdelrazaq2025Benchmarking,
  author = {Abdelrazaq, Abdulrahman Saeed and Alnuaimi, Humaid Abdulla and Baig, Faisal and Elkollaly, Mohamed and Sherif, Mohsen},
  title = {Benchmarking MSWEP Precipitation Accuracy in Arid Zones Against Traditional and Satellite Measurements},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs18010095},
  url = {https://doi.org/10.3390/rs18010095}
}