15 Sep 2022
15 Sep 2022
Status: this preprint is currently under review for the journal ESSD.

TPHiPr: A long-term high-accuracy precipitation dataset for the Third Pole region based on high-resolution atmospheric modeling and dense observations

Yaozhi Jiang1, Kun Yang1,2, Youcun Qi3, Xu Zhou2, Jie He2, Hui Lu1, Xin Li2, Yingying Chen2, Xiaodong Li4, Bingrong Zhou5, Ali Mamtimin6, Changkun Shao1, Xiaogang Ma1, Jiaxin Tian1, and Jianhong Zhou1 Yaozhi Jiang et al.
  • 1Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China
  • 2National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
  • 3Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • 4State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China
  • 5Qinghai Institute of Meteorology Science, Xining, China
  • 6Institute of Desert Meteorology/Taklimakan Desert Meteorology Field Experiment Station, China Meteorological Administration, Urumq, China

Abstract. Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here. Accordingly, this study produces a long-term (1979–2020) high-resolution (1/30°) precipitation dataset (TPHiPr) for the TP by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the Climatology Aided Interpolation and Random Forest methods. Validation shows that the TPHiPr is generally unbiased and has a root mean square error of 4.5 mm day-1, a correlation of 0.84 and a critical success index of 0.67 with respect to all independent rain gauges in the TP, demonstrating that this dataset is remarkably better than the widely-used global/quasi- global datasets, including the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5), the final run version 6 of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP V2). Moreover, the TPHiPr can better detect precipitation extremes compared with the three widely-used datasets. Overall, this study provides a new precipitation dataset with high accuracy for the TP, which may have broad applications in meteorological, hydrological and ecological studies. The produced dataset can be accessed via (Yang and Jiang, 2022).

Yaozhi Jiang et al.

Status: open (until 10 Nov 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2022-299', zeeshan Jaffari, 16 Sep 2022 reply
    • CC2: 'Reply on CC1', Yaozhi Jiang, 17 Sep 2022 reply

Yaozhi Jiang et al.

Data sets

A long-term (1979-2020) high-resolution(1/30°) precipitation dataset for the Third Polar region (TPHiPr) Kun Yang, Yaozhi Jiang

Yaozhi Jiang et al.


Total article views: 248 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
170 69 9 248 3 4
  • HTML: 170
  • PDF: 69
  • XML: 9
  • Total: 248
  • BibTeX: 3
  • EndNote: 4
Views and downloads (calculated since 15 Sep 2022)
Cumulative views and downloads (calculated since 15 Sep 2022)

Viewed (geographical distribution)

Total article views: 229 (including HTML, PDF, and XML) Thereof 229 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 20 Sep 2022
Short summary
Our work produces a long-term (1979–2020) high-resolution (1/30°) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely-used precipitation datasets in the TP. This dataset can be used for the hydrological, meteorological and ecological studies in the TP.