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Bias adjustment of satellite rainfall data through Gaussian process regression (GPR) based on rain intensity classification in the Greater Bay Area, China

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Abstract

Estimating precipitation over large spatial areas remains a challenging problem for hydrologists. Satellite-based remote sensing rainfall products have the advantage of large-scale synchronous coverage, but the reliability of their inversion still needs to be improved. Correcting the bias of satellite-based precipitation estimates (SPEs) is a major challenge in applications such as environmental modeling, hydrology, and water resource management. In this paper, a new bias correction method—a Gaussian process regression (GPR) model method based on rain intensity classification—is proposed to improve the accuracy of a satellite precipitation product—the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run (FR) product—at the daily scale in the Guangdong-Hong Kong-Macao Greater Bay Area. By comparing the effects of the proposed method with those of other existing classical correction methods, namely, quantile mapping (QM), the support vector machine (SVM) approach and direct GPR, it is found that all four methods improve the accuracy of the FR product to varying degrees. GPR based on rain intensity classification has the best effect of FR product improvement, with the CORR increasing from 0.55 to 0.59, the RMSE ranging from 14.37 to 12.79 mm/day, and the BIAS ranging from − 0.14 to 0.03 during the validation period. GPR without rain intensity classification also yields good results, with the QM and SVM methods being the least effective.

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Data availability

The datasets used in the present study are freely available: (i) The daily rain gauge observations are freely available for download on http://data.cma.cn/. (ii) The latest IMERG V05 product is freely available for download on https://pmm.nasa.gov/GPM.

Code availability

Not applicable.

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Acknowledgements

The authors would like to thank the developer of IMERG products for providing the data freely available to public.

Funding

This work was supported by the National Key Research and Development Program of China (No. 2017YFC1502702), the Science and Technology Program of Guangdong Province (No. 2020B1515120079), and the Professorial and Doctoral Scientific Research Foundation of Huizhou University (No. 2021JB014).

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Authors and Affiliations

  1. School of Geography and Planning, Sun Yat-Sen University, No.135 Xingang Xi Road, Guangzhou, 510275, China

    Xue Li, Yangbo Chen, Yueyuan Zhang & Lingfang Chen

  2. School of Geography and Tourism, Huizhou University, No.46 Yanda Road, Huizhou, 516007, China

    Xue Li

Authors
  1. Xue Li
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  2. Yangbo Chen
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  3. Yueyuan Zhang
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  4. Lingfang Chen
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Contributions

All authors contributed to the study conception and design. Xue Li: data curation, formal analysis, visualization, software, and writing—original draft preparation. Yueyuan Zhang: software and revision of the article. Lingfang Chen: revision of the article. Yangbo Chen: supervision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yangbo Chen.

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This research did not involve human subjects. Meteorological datasets used in this study can all be obtained from publicly accessible archives.

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Li, X., Chen, Y., Zhang, Y. et al. Bias adjustment of satellite rainfall data through Gaussian process regression (GPR) based on rain intensity classification in the Greater Bay Area, China. Theor Appl Climatol 152, 1115–1127 (2023). https://doi.org/10.1007/s00704-023-04435-y

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