Yousefnezhad et al. (2026) Accuracy Assessment of CMORPH and GPCP Satellite Precipitation Products Across Iran

Identification

• Journal: Climate
• Year: 2026
• Date: 2026-04-06
• Authors: Mohammad Ramyar Yousefnezhad, Manuchehr Farajzadeh, Y Ghavidel Rahimi
• DOI: 10.3390/cli14040082

Research Groups

• Mohammad Ramyar Yousefnezhad, Manuchehr Farajzadeh, Yousef Ghavidel Rahimi (Department of Physical Geography, Tarbiat Modares University, Tehran 14117-13116, Iran)

Short Summary

This study evaluates and compares the accuracy of CMORPH and GPCP satellite-based precipitation products across daily, monthly, and annual scales over Iran against 128 meteorological stations. It found that CMORPH generally offers higher accuracy at finer temporal resolutions and in event detection, while GPCP shows stronger correlations at broader scales but tends to overestimate precipitation.

Objective

• To evaluate and compare the accuracy of CMORPH and GPCP precipitation products across daily, monthly, and annual time scales over Iran using continuous and categorical statistical metrics.
• To identify the strengths and limitations of each product under varying topographic and climatic conditions, with particular attention to their performance in arid, semi-arid, and mountainous environments.
• To derive practical recommendations for operational dataset selection in Iran and similar data-scarce regions.

Study Configuration

• Spatial Scale: Iran (between 25° and 40° N latitude and 44° and 64° E longitude), covering approximately 1,648,000 square kilometers. CMORPH: 0.25° × 0.25° (approximately 625 square kilometers at the equator). GPCP: 1° × 1° (approximately 10,000 square kilometers).
• Temporal Scale: 15-year period from 2008 to 2022, analyzed at daily, monthly, and annual scales.

Methodology and Data

• Models used:
  • CMORPH (Climate Prediction Center MORPHing technique) Version 1.0 ADJ
  • GPCP (Global Precipitation Climatology Project) daily Version 1.3
• Data sources:
  • Ground-based observations from 128 synoptic meteorological stations across Iran, provided by the Islamic Republic of Iran Meteorological Organization (IRIMO).
  • CMORPH data accessed from NOAA–NCEP Climate Prediction Center.
  • GPCP data accessed from NOAA–NCEI.

Main Results

• Daily Scale: CMORPH outperformed GPCP in correlation coefficient (mean CC: 0.39 vs. 0.35), root mean square error (mean RMSE: 3.5 mm vs. 4.0 mm), probability of detection (mean POD: 0.57 vs. 0.48), and critical success index (mean CSI: 0.30 vs. 0.28). GPCP had a slightly lower false alarm ratio (mean FAR: 0.61 vs. 0.63). CMORPH consistently underestimated daily precipitation, while GPCP systematically overestimated it.
• Monthly Scale: Both products showed stronger correlations than at the daily scale. CMORPH achieved a higher mean correlation (CC: 0.84 vs. 0.76), while GPCP yielded a lower mean RMSE (26.2 mm vs. 31.1 mm).
• Annual Scale: GPCP demonstrated a higher mean correlation coefficient (CC: 0.73 vs. 0.55) but consistently overestimated annual precipitation. CMORPH generally provided estimates equal to or lower than observed values, except in 2010 and 2015. RMSE values were similar (CMORPH: 168.6 mm; GPCP: 170.9 mm).
• Spatial Variability: Both products exhibited larger errors and lower correlations in mountainous regions (Alborz and Zagros) and weaker performance in central and northeastern arid/semi-arid regions. CMORPH performed better in the northwest, while GPCP showed better correlation in the Zagros region.

Contributions

• First systematic comparison of CMORPH and GPCP datasets specifically for Iran, addressing a gap in the literature for this topographically complex, arid-to-semi-arid region.
• Provides practical recommendations for selecting appropriate satellite precipitation products for hydrological modeling, drought monitoring, and water resource management in Iran and similar data-scarce regions, based on temporal scale and application needs.
• Highlights the distinct algorithmic trade-offs of CMORPH (higher resolution, better event detection, higher FAR) and GPCP (coarser resolution, lower bias, more temporal stability) in diverse climatic and topographic settings.
• Emphasizes the importance of considering regional climatic and topographic characteristics for interpreting satellite precipitation products and the need for expanded high-elevation ground-based observations.

Funding

This research received no external funding. The manuscript is derived in part from the doctoral dissertation of Mohammad Ramyar Yousefnezhad, submitted to Tarbiat Modares University.

Citation

@article{Yousefnezhad2026Accuracy,
  author = {Yousefnezhad, Mohammad Ramyar and Farajzadeh, Manuchehr and Rahimi, Y Ghavidel},
  title = {Accuracy Assessment of CMORPH and GPCP Satellite Precipitation Products Across Iran},
  journal = {Climate},
  year = {2026},
  doi = {10.3390/cli14040082},
  url = {https://doi.org/10.3390/cli14040082}
}