Assessing reanalysis data for understanding rainfall climatology and variability over Central Equatorial Africa

  • Wenjian Hua
  • Liming ZhouEmail author
  • Sharon E. Nicholson
  • Haishan Chen
  • Minhua Qin


Understanding the rainfall climatology and variability over Central Equatorial Africa (CEA) has largely been hampered by the lack of adequate in situ observations and meteorological stations for the last three decades. Large differences and uncertainties among several observational and reanalysis data sets and various climate model simulations present another big challenge. This study comprehensively assesses the currently widely used reanalysis products based on quality-controlled radiosonde observations and a new gauge-based rainfall data set, NIC131, in order to identify the “best” reanalysis products available over CEA. Among the seven reanalysis data sets (i.e., 20CR, CFSR, ERA-Interim, JRA-55, MERRA2, NCEP-1 and NCEP-2), MERRA2 is closest to NIC131 in reproducing the mean climatology and interannual variability and has the smallest biases and root-mean-square error (RMSE) in describing the observed wind fields in the lower- and middle-troposphere, and the two NCEP reanalyses can better capture geopotential height fields than the other reanalyses. Overall, the reanalyses capture the major features of the rainfall seasonal cycle and the seasonal evolution in the reference data but demonstrate an evident spread of spatiotemporal characteristics. By examining the moisture transport, we find that the differences in the lower- and middle-tropospheric circulation can reasonably explain the differences in the rainfall climatology among the reanalyses. Considering the large differences in horizontal and vertical wind fields among the seven reanalyses, we need to use the best reanalysis wind and moisture fields to explain the observed rainfall and associated circulation changes over CEA.


Central Equatorial Africa Reanalysis Rainfall Hydrological cycle 



This study was supported by National Science Foundation (NSF AGS-1535426 and AGS-1535439), as well as project supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We would like to thank three reviewers for their suggestions and comments.

Supplementary material

382_2018_4604_MOESM1_ESM.docx (2.1 mb)
Supplementary material 1 (DOCX 2112 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Wenjian Hua
    • 1
    • 2
  • Liming Zhou
    • 2
    Email author
  • Sharon E. Nicholson
    • 3
  • Haishan Chen
    • 1
  • Minhua Qin
    • 1
    • 2
  1. 1.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)Nanjing University of Information Science and TechnologyNanjingChina
  2. 2.Department of Atmospheric and Environmental SciencesUniversity at Albany, State University of New YorkAlbanyUSA
  3. 3.Earth, Ocean and Atmospheric ScienceFlorida State UniversityTallahasseeUSA

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