Sensitivity analysis of SKEB method in Regional ensemble forecast system GRAPES-REPS

In order to solve the problem of excessive energy dissipation near the sub-grid scale in numerical weather model, the Stochastic Kinetic Energy Backscatter (SKEB) method is introduced into the GRAPES-REPS regional ensemble prediction system, and the first-order autoregressive stochastic process is used in the horizontal direction. Calculate the random pattern obtained by spherical harmonic expansion in the direction, calculate the local dynamic energy dissipation rate caused by the numerical diffusion scheme, construct the random flow function forcing, and convert it into horizontal wind speed disturbance, compensate the dissipated kinetic energy, and carry out A 10-day ensemble prediction test and a randomized time and space scale sensitivity test in September and October 2018 (choose 1st, 7th, 13th, 19th, and 25th), and evaluate the test results. The main conclusions of the research work are as follows: By comparing the ensemble prediction results of the test using the SKEB method and the test without the SKEB method, the use of the SKEB scheme increases the large aerodynamic energy of the GRAPES regional model in the small and medium-scale region, and improves the GRAPES model to the actual atmosphere to some extent. The simulation ability of kinetic energy spectrum; the introduction of SKEB scheme in regional ensemble prediction can significantly improve the dispersion of U and V in horizontal wind field of regional model, and the problem of insufficient dispersion of large-scale dynamic energy dissipation rate in Qinghai-Tibet Plateau region is improved. The SKEB program has improved the forecasting skills to a certain extent, such as reducing the CRPS scores of the horizontal wind fields U and V, reducing the outliers scores of the horizontal wind field, temperature, and 10 m wind speed; the introduction of the SKEB method can improve the light rain. The precipitation probability prediction skill score, but the improvement of the score did not pass the significance test, so it is considered that the SKEB method is difficult to effectively improve the probability prediction technique of precipitation.

Sensitivity tests based on the SKEB method for five time scales of random pattern (1h, 3h, 6h, 9h and 12h of the time series τ) show that the ensemble prediction is sensitive to the five time scales of the stochastic model of the SKEB method. And the 12h experiment show the best performance than the others.