Application of multi-parameter combined perturbation method in ensemble forecast of squall line system

The strong cold pool is pivotal in the genesis of severe gales associated with squall lines, and its simulation intensity is closely related to the parameter settings of cloud microphysical processes and boundary layer processes in the model. Despite parameter uncertainty, it remains challenging to apply reasonable parameter perturbations to the squall line system. To improve the performance of the convective-scale numerical model in the forecast of the squall line system, based on the WRF (The Weather Research and Forecasting Model) model, five key parameters were selected from the cloud microphysical process and the boundary layer process to carry out the sensitivity test for the weak simulation of the cold pool of the squall line. Subsequently, the joint perturbation of the sensitive parameters is carried out, and the influence of this method on the simulation of a squall line process in Jiangsu is discussed. The results demonstrate that adjusting parameters that influence evaporation can significantly alter the estimation of the cold pool. Specifically, the parameter CONSTB, which reflects the impact of raindrop size on its terminal velocity, and the parameter VF1R, which accounts for the influence of surrounding airflow on raindrop behavior, exhibit the highest sensitivity to the cold pool dynamics. In the single-parameter and multi-parameter combined perturbation experiments, the simulated 2 m temperature in the cold zone of the squall line is 1-2 ℃ lower than that of the control experiment, which effectively improves the problem of weak simulation of the cold pool. In addition, the joint perturbation of CONSTB and VF1R parameters had a notably positive impact on forecast accuracy, with the simulated 10 m maximum wind speed being the most accurate in comparison to actual observations. Results show that the multi-parameter joint perturbation method for squall line cold pools effectively captures the uncertainty of parameters within physical parameterization schemes and improves cold pool simulation, thereby enhancing the accuracy of squall line gale predictions.