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Improving quantitative precipitation estimates by radar-rain gauge merging and an integration algorithm in the Yishu River catchment, China

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Abstract

High-precision areal rainfall is crucial for hydrometeorological coupled forecasts. The accuracy of quantitative precipitation estimates (QPE) is improved by merging radar-rain gauge data with an integration approach based on a statistical weight matrix in the Yishu River catchment, China. First, a local Z-R relationship (Z = 85R1.82) is reconstructed using a genetic optimization algorithm to minimize the error from different precipitation patterns and climate zones. Next, based on the local Z-R relationship, six methods of merging radar-rain gauge data are respectively adapted to improve the accuracy of QPE, as follows: mean field bias (MFB), Kalman filter (KLM), optimum interpolation (OPT), variation method (VAR), two-step calibration of KLM and OPT (KOP), and two-step calibration of KLM and VAR (KVR). The results indicate that QPE accuracy is clearly improved, and is in good agreement with rain gauge observations, after the six merging methods are applied. Among these methods, KOP performs the best, reducing the mean relative error from 55.2 to 15.1%. An innovative aspect of this work is the inclusion of an integrated ideology based on a statistical weight matrix, which further improves the accuracy of QPE by incorporating the advantages of each estimation mode. The results further show that the accuracy of QPE derived from the integration approach is higher than that obtained by any individual method; QPE values are similar to those obtained the automatic rain gauge network in both the spatial distribution and location of the intense precipitation centers, and better reflects the precipitation status over the ground surface. This approach could serve as a promising conventional method for QPE in the study region.

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Acknowledgments

The authors are grateful to Linyi Meteorological Bureau of China for providing Doppler radar data and rain gauge data at sites. The authors would like to acknowledge the anonymous reviewers and the editor for their thoughtful comments and suggestions, which have greatly improved the presentation of this paper.

Funding

This work is financially supported by the Special Fund for Natural Science Foundation of Jiangsu province (BK20141001), by the Meteorological Open Research Fund in Huaihe River basin (HRM201702).

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Authors and Affiliations

  1. School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yuehong Shao, Aolin Fu, Jun Zhao & Jinchao Xu

  2. Kunshan Meteorological Bureau, Kunshan, 215300, China

    Junmei Wu

Authors
  1. Yuehong Shao
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  2. Aolin Fu
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  3. Jun Zhao
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  4. Jinchao Xu
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Contributions

All authors contributed substantially towards the success of this study. Dr. Shao plays a guiding role in the whole process as first author and corresponding author. She mainly takes charge of experiment design, data analysis, and manuscript writing of this research. Under the guidance of Dr. Shao, Aolin Fu revised the manuscript according to the reviewer’s suggestions. For manuscript improvement, Dr. Zhao corrected errors in spelling, grammar, consistency, word choice, and sentence clarity. Dr. Xu mainly finished the drawing of some figures in the manuscript. Junmei Wu mainly did the preliminary collation of the data.

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Correspondence to Yuehong Shao.

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Shao, Y., Fu, A., Zhao, J. et al. Improving quantitative precipitation estimates by radar-rain gauge merging and an integration algorithm in the Yishu River catchment, China. Theor Appl Climatol 144, 611–623 (2021). https://doi.org/10.1007/s00704-021-03526-y

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