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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-294
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-294
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  25 Sep 2020

25 Sep 2020

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This preprint is currently under review for the journal HESS.

Global component analysis of errors in five satellite-only global precipitation estimates

Hanqing Chen1,2,3, Bin Yong1,4, Leyang Wang3, Liliang Ren1,4, and Yang Hong5 Hanqing Chen et al.
  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
  • 2School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
  • 3Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, Nanchang 330013, China
  • 4College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
  • 5School of Civil Engineering and Environment Sciences, University of Oklahoma, Norman, OK 73019, USA

Abstract. Revealing the error components for satellite-only precipitation products (SPPs) can help algorithm developers and end-users substantially understand their error features and meanwhile is fundamental to customize retrieval algorithms and error adjustment models. Two error decomposition schemes were employed to explore the error components for five SPPs (i.e., MERG-Late, IMERG-Early, GSMaP-MVK, GSMaP-NRT, and PERSIANN-CCS) over different seasons, rainfall intensities, and topography classes. Firstly, this study depicted global maps of the total bias (total mean squared error) and its three (two) independent components for these five SPPs over four seasons for the first time. We found that the evaluation results between similar regions could not be extended to one another. Hit and/or false biases are major components of the total bias in most regions of the global land areas. In addition, the proportions of the systematic error are less than 20 % of total errors in most areas. One should note that each SPP has larger systematic errors in several regions (i.e., Russia, China, and Conterminous United States) for all four seasons, these larger systematic errors from retrieval algorithms are primarily due to the missed precipitation. Furthermore, IMERG suite and GSMaP-NRT display less systematic error in the rain rates with intensity less than 40 mm/day, while the systematic errors of GSMaP-MVK and PERSIANN-CCS increase with increasing rainfall intensity. Given that mean elevation cannot reflect the complex degree of terrain, we introduced the standard deviation of elevation (SDE) to replace mean elevation to better describe topographic complexity. Compared with other SPPs, GSMaP suite shows a stronger topographic dependency in the four bias scores. A novel metric namely normalized error component (NEC) was proposed to fairly evaluate the impact of the solely topographic factor on systematic (random) error. It is found that these products show different topographic dependency patterns in systematic (random) error. Meanwhile, the pattern of the impact of the solely topographic factor on systematic (random) error is similar to the relationship between systematic (random) error and topography because the average precipitations of all topography categories are very close. Finally, the potential directions of the improvement in satellite precipitation retrieval algorithms and error adjustment models were identified in this study.

Hanqing Chen et al.

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