Arid Zone Research ›› 2021, Vol. 38 ›› Issue (6): 1624-1636.doi: 10.13866/j.azr.2021.06.14

• Weather and Climate • Previous Articles     Next Articles

Validation of land data assimilation and reanalysis precipitation datasets over Inner Mongolia

SONG Haiqing1,2(),ZHU Zhongyuan1(),LI Yunpeng2   

  1. 1. Water Conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China
    2. Ecology and Agrometeorology Center of Inner Mongolia, Hohhot 010051, Inner Mongolia, China
  • Received:2021-01-05 Revised:2021-03-09 Online:2021-11-15 Published:2021-11-29
  • Contact: Zhongyuan ZHU E-mail:haiqingsong@emails.imau.edu.cn;nmgzzy@tom.com

Abstract:

Monthly precipitation data observed at 115 weather stations in Inner Mongolia during 1982-2018 was compared with land data assimilation and reanalysis precipitation datasets [Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), the Fifth Generation of ECMWF Reanalysis (ERA5), China Meteorological Administration, Global Land surface Reanalysis (CRA40/Land), and Global Land Data Assimilation System (GLDAS)]. The reliability of the four precipitation datasets was analyzed and accuracy of monthly variation was also evaluated. The results showed that the four precipitation datasets captured features of precipitation distribution. Precipitation levels decreased from the northeast to west and were lower in winter and higher in summer. By analyzing correlation coefficients, mean absolute errors, mean bias errors, root mean square errors, and Nash-Sutcliffe efficiency coefficients, the four precipitation datasets showed the best performance in summer and the worst performance in winter, and better values in eastern semi-humid and semiarid areas than western arid and extremely arid areas. Compared with ERA5 and GLDAS, FLDAS, and CRA40/Land showed the lowest differences from observed values and the best statistical characteristics. ERA5 data mostly overestimated precipitation in Inner Mongolia during most of the study period, while GLDAS underestimated it. Particularly in winter, GLDAS showed almost no observation capability for snow precipitation, while the newly released FLDAS data performed better. Overall, FLDAS and CRA40/Land precipitation datasets performed best with better statistical power.

Key words: precipitation, land assimilation data, reanalysis data, applicability