Articles | Volume 24, issue 4
Hydrol. Earth Syst. Sci., 24, 2061–2081, 2020
https://doi.org/10.5194/hess-24-2061-2020
Hydrol. Earth Syst. Sci., 24, 2061–2081, 2020
https://doi.org/10.5194/hess-24-2061-2020

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 (https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim) 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 https://doi.org/10.1002/qj.828

GPCP pentad precipitation analyses: An experimental dataset based on gauge observations and satellite estimates (https://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html) P. Xie, J. E. Janowiak, P. A. Arkin, R. Adler, A. Gruber, R. Ferraro, G. J. Huffman, and S. Curtis https://doi.org/10.1175/2769.1

Monthly gridded surface precipitation at 0.5° in China (V2.0) Y. Zhao, J. Zhu, Y. Xu, and N. Liu http://data.cma.cn/data/detail/dataCode/SEVP_CLI_CHN_PRE_DAY_GRID_0.50/},

GPCC Full Data Reanalysis Version 6.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data (https://opendata.dwd.de/climate_environment/GPCC/html/fulldata_v6_doi_download.html) U. Schneider, A. Becker, P. Finger, A. Meyer-Christoffer, B. Rudolf, and M. Ziese https://doi.org/10.5676/DWD_GPCC/FD_M_V6_050

Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset (https://iridl.ldeo.columbia.edu/SOURCES/.UEA/.CRU/.TS3p21/) I. Harris, P. D. Jones, T. J. Osborn, and D. H. Lister https://doi.org/10.1002/joc.3711

The Global Precipitation Climatology Project (GPCP) monthly analysis (New Version 2.3) and a review of 2017 global precipitation (https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html) 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 https://doi.org/10.3390/atmos9040138

MSWEP: 3-hourly 0.25{\degree} global gridded precipitation (1979--2015) by merging gauge, satellite, and reanalysis data (https://platform.princetonclimate.com/PCA_Platform/mswepRetro.html) H. E. Beck, A. I. J. M. van Dijk, V. Levizzani, J. Schellekens, D. G. Miralles, B. Martens, and A. de Roo https://doi.org/10.5194/hess-21-589-2017

An overview of CMIP5 and the experiment design (https://esgf-node.llnl.gov/projects/cmip5/) K. E. Taylor, R. J. Stouffer, and G. A. Meehl https://doi.org/10.1175/bams-d-11-00094.1

Download
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.