A model tendency perturbation method that combines systematic bias of potential temperature and random errors in global ensemble prediction

The traditional model perturbation method of ensemble prediction is usually used to describe random errors of physical processes, but the model inevitably has systematic bias. Therefore, in order to reduce the impact of systematic bias on ensemble prediction, the CMA-GEPS is employed to obtain systematic bias tendency using the empirical orthogonal function (EOF) method. In the integration process, the systematic bias correction method and the traditional Stochastically Perturbed Parameterization Tendency (SPPT) are combined to build a model perturbation method (Bias correction of bias tendency based on SPPT, SPPT-B) that combines systematic bias and random errors of ensemble forecast. Ensemble forecasting experiments are designed and carried out to explore the impact of SPPT-B on global ensemble forecasting. The conclusions are as follow: (1) The first EOF mode of the systematic bias can reflect the main characteristics of the systematic bias well. It shows that basically the systematic bias in the upper troposphere is larger than that in the middle and lower troposphere and increases linearly with forecast lead time. (2) The systematic bias correction method and SPPT-B can effectively reduce the systematic bias in upper and lower levels in the southern and northern Hemispheres and in the tropics, and SPPT-B can significantly improve Spread in the tropics. (3) The effect of the two schemes on the improvement of ensemble prediction skill in the upper troposphere is better than that in the lower troposphere. The above results indicate that the model perturbation method that considers both systematic bias and random errors can effectively improve global ensemble forecasting skill, and can provide a scientific basis for the development of global ensemble forecasting model perturbation method considering both systematic bias and random errors.