Integrated Evaluation and error decomposition of four gridded precipitation products using dense rain gauge observations over the Yunnan-Kweichow Plateau, China

ABSTRACT

Evaluating the precision and applicability of high-quality precipitation products in the distinctive terrain and intricate climate of the Yunnan-Kweichow Plateau (YKP) is pivotal for climate research. This study comprehensively assesses four gridded precipitation datasets (AERA5-Asia, AIMERG, ERA5-Land, and IMERG-Final) using the China Meteorological Administration’s surface precipitation data. It employs eight statistical indicators and error decomposition methods at various spatiotemporal scales. The main findings are as follows: (1) AERA5-Asia, AIMERG, and IMERG-Final show similar precipitation patterns, with ERA5-Land overestimating. While all display minor seasonal variations, AERA5-Asia underestimates summer rain. ERA5-Land tends to overstate, whereas AIMERG and IMERG-Final are generally accurate but slightly undervalued in southern YKP. (2) Hourly analysis reveals AERA5-Asia leads in performance metrics (CC: 0.23, MAE: 0.49 mm/hour, RMSE: 0.18 mm/hour, CSI: 0.27). In contrast, ERA5-Land lags, marked by the lowest BIAS (35.39%), FAR (0.74), and FBI (2.85). AIMERG and IMERG-Final display comparable results but underperform in CC (0.16, 0.13), POD (0.31, 0.30), and CSI (0.19, 0.18). (3) False bias significantly contributes to the total bias of precipitation products. AERA5-Asia and AIMERG mitigate total bias and enhance false precipitation situations through calibration algorithms, albeit introducing missed bias in the central region of YKP. The study findings offer valuable insights into YKP precipitation, informing the development of grid-based fusion algorithms in the region’s complex terrain.

KEYWORDS:

• Precipitation products
• comprehensive assessment
• error decomposition
• Yunnan-Kweichow plateau

Acknowledgments

We express our gratitude to the providers of operational precipitation products and rain gauge measurements for generously making their data available to us.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Conceptualization, H. L.; Data curation, Y. Y., X. L., and S. Y.; Formal analysis, T. L.; Funding acquisition, H. L.; Methodology, T. L.; Project administration, Q. X.; Resources, J. R. and J. H.; Supervision, H. L.; Validation, T. L.; Visualization, T. L.; Writing – original draft, T. L.; Writing – review & editing, H. L. and Z. Z. All authors have read and agreed to the published version of the manuscript.

Data availability statement

We express our sincere appreciation to the data providers, particularly the CMPA provider (http://data.cma.cn). The AERA5-Asia products are accessible for download at https://data.tpdc.ac.cn/zh-hans/data/7bb5feae-c1c9–4677-87d6-5c64dba659cd/, last accessed on 5 January 2023. Likewise, the AIMERG products can be obtained from https://data.tpdc.ac.cn/zh-hans/data/1090dead-dbba-44c8-980c-ca4c631d3d5c, accessed on the same date. For the IMERG-Final products, they are available for download at https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_06/summary?keywords=%22IMERG%20final%22, accessed on 5 January 2023. Furthermore, the ERA5-Land reanalysis datasets can be acquired from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview, accessed on the same date. Our profound gratitude extends to these organizations for generously providing the data indispensable to this study.

Additional information

Funding

This study was made possible through financial support from the National Natural Science Foundation of China [42361074], Guizhou Bijie United Fund Project, Bike United [No. [2023]25; [2023]8;[2023]49]; School Undergraduate Teaching Quality Improvement Project, [No. 2022013]; Big Data Application Engineering Research Center, [No. 202301].