Gu et al. (2025) Diurnal variation features and dry times impact based on the latest hourly satellite-based precipitation data across China

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-10-15
• Authors: Yu Gu, Dingzhi Peng, Yuwei Gong, Zhenglong Fan, Tao Wang, Bo Pang, Aizhong Ye
• DOI: 10.1016/j.ejrh.2025.102859

Research Groups

College of Water Sciences, Beijing Normal University, Beijing, China

Short Summary

This study comprehensively evaluated the spatiotemporal and diurnal performance of three latest hourly satellite-based precipitation products (IMERG V07, GSMaPGaugeV8, CMORPH_V1.0) across China. It found that IMERG and GSMaP generally outperformed CMORPH, and highlighted the significant impact of dry times on evaluation results, proposing a bias evaluation method based solely on wet times.

Objective

• To comprehensively evaluate the spatiotemporal and diurnal variation features of the latest hourly satellite-based precipitation products (IMERG V07, GSMaPGaugeV8, CMORPH_V1.0) across China.
• To investigate the effect of zero values (dry times) on the evaluation of these products.

Study Configuration

• Spatial Scale: Mainland China (73°E to 135°E, 3°N to 53°N), covering a vast territory with diverse climatic characteristics.
• Temporal Scale: Hourly data from 2008 to 2018.

Methodology and Data

• Models used: Not applicable (evaluating precipitation products, not using a specific model).
• Data sources:
  • Benchmark: China Hourly Merged Precipitation Analysis (CMPA), with 0.1° spatial resolution and 1-hour temporal resolution, obtained from the National Meteorological Information Center (NMIC) of the China Meteorological Administration.
  • Satellite-based Precipitation Products (SPPs) evaluated:
    • Integrated Multi-Satellite Retrievals for Global Precipitation Measurement V07 Final Run (IMERG V07)
    • Global Satellite Mapping of Precipitation GaugeV8 (GSMaPGauge_V8)
    • Climate Prediction Centre Morphing Technique V1.0CRT (CMORPHV1.0)
  • Evaluation Metrics: Correlation coefficient (CC), Mean Error (ME, in mm/h), Root Mean Squared Error (RMSE, in mm/h), Probability of Detection (POD), False Alarm Rate (FAR), and Critical Success Index (CSI).

Main Results

• IMERG and GSMaP generally outperformed CMORPH across China, with GSMaP showing superior detection capacity.
• All evaluated SPPs showed underestimation of wet times and overestimation of dry times, leading to a potential misinterpretation of overall bias if dry times are not excluded from evaluation.
• SPPs performed better between 02:00 and 06:00 (Beijing time), exhibiting smaller errors and greater correlation and detection capacities. Superior detection capabilities were also observed between 15:00 and 20:00.
• The accuracy and detection capabilities of the SPPs were significantly weaker at the hourly scale compared to daily, monthly, or annual scales.
• Spatial differences in data accuracy (detection capability) at different moments primarily occurred in the east or southeast (northeast, center, or south) of China.

Contributions

• Provided a comprehensive spatiotemporal and diurnal evaluation of the latest versions of three widely used hourly satellite-based precipitation products (IMERG V07, GSMaP V8, CMORPH V1.0) across the entirety of China.
• Quantified the significant impact of dry times (zero values) on the evaluation of hourly precipitation products, proposing that bias should be evaluated using only wet times to avoid misleading overall deviation.
• Offered crucial insights for developers to enhance inversion algorithms and for prospective consumers to improve post-processing and utilization of these products, especially for short-duration extreme precipitation events and hydrological simulations.

Funding

National Science and Technology Major Project (2025ZD1204403)

Citation

@article{Gu2025Diurnal,
  author = {Gu, Yu and Peng, Dingzhi and Gong, Yuwei and Fan, Zhenglong and Wang, Tao and Pang, Bo and Ye, Aizhong},
  title = {Diurnal variation features and dry times impact based on the latest hourly satellite-based precipitation data across China},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.102859},
  url = {https://doi.org/10.1016/j.ejrh.2025.102859}
}