Volume XLII-3/W5 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W5, 17-23, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W5-17-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 Oct 2018

29 Oct 2018

THE INFLUENCE OF BACKGROUND ERROR COVARIANCE OF GSI THREE-DIMENSIONAL VARIATIONAL DATA ASSIMILATION ON METEOROLOGY AND AEROSOL PREDICTION

Y. Hu1, M. Zhang2, Y. Liang3, L. Ye4, D. Zhao5, and Z. Zang3 Y. Hu et al.
  • 1Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing , China
  • 2College of Sciences, Nanjing Agricultural University, Nanjing, China
  • 3Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing, China
  • 4Henan Meteorological Observatory, Zhengzhou, China
  • 5No.75839 Unit of PLA, Guangzhou, China

Keywords: Background error covariance, NMC method, Data assimilation, GSI, WRF/Chem

Abstract. Background error covariance (BEC) plays a key role in a variational data assimilation system. It determines variable analysis increments by spreading information from observation points. In order to test the influence of BEC on the GSI data assimilation and prediction of aerosol in Beijing-Tianjin-Hebei, a regional BEC is calculated using one month series of numerical forecast fields of November 2017 based on the National Meteorological Center (NMC) method, and compared with the global BEC.The results show that the standard deviation of stream function of the regional BEC is larger than that of the global BEC. And the horizontal length-scale of the regional BEC is smaller than that of the global BEC, white the vertical length-scale of the regional BEC is similar with that of the global BEC. The increments of the assimilation experiment with the regional BEC present more small scale information than that with the global BEC. The forecast skill of the experiment with the regional BEC is higher than that with the global BEC in the stations of Beijing, Tianjin, Chengde and Taiyuan, and the average root-mean-square errors (RMSE) reduces by over 13.4%.