Abstract
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%.
Highlights
Data assimilation has used to create a best estimate of the state of the atmosphere at a given time using information from observations as well as a background, typically a previous model forecast
The characteristics of the background error modeling via the National Meteorological Center (NMC) method are investigated for the variational data assimilation system of the Grid-point Statistical Interpolation (GSI) system (Kleist et al, 2009; Descombes et al.,2015)
The increments field from the experiment with the regional Background error covariance (BEC) (Fig.6b) shows more local spatial variety than that with the global BEC (Fig.6a).There are two stronger temperature increment filed in Hohhot and Shuozhou with the regional BEC than that with the global BEC, respectively
Summary
Data assimilation has used to create a best estimate of the state of the atmosphere at a given time using information from observations as well as a background, typically a previous model forecast. Despite this wealth of observational information available, data assimilation needs to use a background state. BEC is a key component in data assimilation improvements. It spreads information between variables, imposes balance across different analysis variables and gives proper weight to the background term in defining the analysis cost function. Most knowledge about forecast error covariances in weather forecasting models has been derived from the NMC method (Parrish and Derber, 1992)
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