Abstract: Analog Ensemble (AnEn) is a statistical interpretation method that is based on similarity theory,big data mining and ensemble forecasting. The basic principle of this method is introduced first. It is then applied to revise the ground elements predicted by BJ-RUCv3.0. The results show that the root-mean-square-errors (RMSEs) of 10 m wind speed and 2 m temperature are significantly decreased during the forecast lead times of 0-36 h after using AnEn. The RMSE of 10 m wind speed is decreased by 44%,and the RMSE of 2 m temperature is decreased by 22%. Comparing horizontal distribution of prediction errors at stations,it is found that the application of the AnEn method has more obvious effects for 10 m wind speed prediction at non-urban stations and 2 m temperature prediction at complex terrain area stations. AnEn and Support Vector Machines (SVM) with the same predictors have significant and similar effects on Numerical Weather Prediction (NWP) model predictions of 10 m wind speed and 2 m temperature. However,the AnEn method has the advantage of needing less computing resource and less manual intervention. The pattern of average error growth can be well simulated by the AnEn method,and the RMSE of AnEn mean and average ensemble spread show ideal statistical consistency. It not only yields deterministic predictions,but also provides uncertainty or probability information for the prediction factors. Therefore,the AnEn method will have a broad application prospect in NWP model interpretation prediction.