Articles | Volume 24, issue 4
Hydrol. Earth Syst. Sci., 24, 2061–2081, 2020
Hydrol. Earth Syst. Sci., 24, 2061–2081, 2020

Research article 23 Apr 2020

Research article | 23 Apr 2020

A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products

Xudong Zhou et al.

Data sets

The ERA-Interim reanalysis: Configuration and performance of the data assimilation system ( D. P. Dee, S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. Beljaars, L. van~de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. Mcnally, B. M. Monge-Sanz, J. J. Morcrette, B. K. Park, C. Peubey, P. de~Rosnay, C. Tavolato, J. N. Thépaut, and F. Vitart

GPCP pentad precipitation analyses: An experimental dataset based on gauge observations and satellite estimates ( P. Xie, J. E. Janowiak, P. A. Arkin, R. Adler, A. Gruber, R. Ferraro, G. J. Huffman, and S. Curtis

Monthly gridded surface precipitation at 0.5° in China (V2.0) Y. Zhao, J. Zhu, Y. Xu, and N. Liu},

GPCC Full Data Reanalysis Version 6.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data ( U. Schneider, A. Becker, P. Finger, A. Meyer-Christoffer, B. Rudolf, and M. Ziese

Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset ( I. Harris, P. D. Jones, T. J. Osborn, and D. H. Lister

The Global Precipitation Climatology Project (GPCP) monthly analysis (New Version 2.3) and a review of 2017 global precipitation ( R. F. Adler, M. R. Sapiano, G. J. Huffman, J. J. Wang, G. Gu, D. Bolvin, L. Chiu, U. Schneider, A. Becker, E. Nelkin, P. Xie, P., R. Ferraro, and D. B. Shin

MSWEP: 3-hourly 0.25{\degree} global gridded precipitation (1979--2015) by merging gauge, satellite, and reanalysis data ( H. E. Beck, A. I. J. M. van Dijk, V. Levizzani, J. Schellekens, D. G. Miralles, B. Martens, and A. de Roo

An overview of CMIP5 and the experiment design ( K. E. Taylor, R. J. Stouffer, and G. A. Meehl

Short summary
This article proposes a new estimation approach for assessing the uncertainty with multiple datasets by fully considering all variations in temporal and spatial dimensions. Comparisons demonstrate that classical metrics may underestimate the uncertainties among datasets due to an averaging process in their algorithms. This new approach is particularly suitable for overall assessment of multiple climatic products, but can be easily applied to other spatiotemporal products in related fields.