A spectral model based on atmospheric self-memorization principle

Based on the atmospheric self-memorization principle, a complex memory function was introduced and the spectral form of atmospheric self-memorial equation was derived. Setting up and solving the equation constitute a new approach of the numerical weather prediction. Using the spectral model T42L9 as a dynamic kernel, a global self-memorial T42 model (SMT42) was established, with which twelve cases of 30-d integration experiments were carried out. Compared with the T42L9, the SMT42 is much better in 500 hPa forecast not only for daily circulation but also for monthly mean circulation. The anomaly correlation coefficient (ACC) of forecast for monthly mean circulation has been improved to 0.42, increased by 0.05, and the root-mean-square error (RMSE) has been reduced from 6.09 to 4.03 dagpm.