Journal article

A method to extend temporal coverage of high quality precipitation datasets by calibrating reanalysis estimates

Y Li, QJ Wang, H He, Z Wu, G Lu

Journal of Hydrology | Elsevier | Published : 2020

Abstract

Available high quality precipitation datasets generally cover only recent periods. To extend these datasets back in time is challenging due to historically lower rain gauge network density or poorer remote sensing. In this study, a Bayesian joint probability method is presented to extend the temporal coverage of high quality precipitation datasets by calibrating reanalysis estimates. Relationships between precipitation estimates from a high quality dataset and precipitation estimates from a reanalysis dataset are established by using data from the overlapping period of the two datasets. The relationships are then used to calibrate reanalysis estimates for the period when only reanalysis data..

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University of Melbourne Researchers

Grants

Awarded by National Key R&D Program of China, China


Awarded by Fundamental Research Funds for the Central Universities, China


Awarded by National Natural Science Foundation of China, China


Awarded by Postgraduate Research & Practice Innovation Program of Jiangsu Province, China


Funding Acknowledgements

We would like to thank Geoff Pegram and another two anonymous reviewers for their insightful comments. This work was funded by the National Key R&D Program of China, China (Grant No. 2018 YFC0407701, 2017YFC1502403), the Fundamental Research Funds for the Central Universities, China (Grants No. 2019B10214, 2017B681X14), the National Natural Science Foundation of China, China (Grants No. 51579065, 51779071), the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX17_0413), and the China Scholarship Council scholarship, China. We also thank Dr Tongtiegang Zhao for providing technical support to the work.