The strong cold pool is pivotal in the genesis of severe gales associated with squall lines, and its intensity simulation is closely related to parameter settings of cloud microphysical and boundary layer processes in the model. Despite parameter uncertainties, it remains challenging to apply reasonable parameter perturbations to squall line systems. To improve the performance of convective-scale numerical models in the forecast of squall line systems, based on the WRF (The Weather Research and Forecasting Model) model, five key parameters are selected from the cloud microphysical process and the boundary layer process to carry out sensitivity tests for the weak simulation of the cold pool associated with squall lines. 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 indicate that adjusting parameters that influence evaporation can significantly affect 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 overcomes the problem of weak simulation of the cold pool. In addition, the joint perturbation of CONSTB and VF1R parameters has a notable 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.

For numerical weather prediction (NWP), data assimilation (DA) combines short-term forecasts and various atmospheric observations to achieve optimal initial conditions, based on which subsequent forecasts are launched. With the rapid advancements in numerical models and observing systems, DA has been significantly evolved. Modern methods now can account for uncertainties of state variables across various spatiotemporal scales, incorporate multiscale observation error statistics, and enforce dynamical constrains and model balances. Meanwhile, observations from various platforms, such as ground-based, aircraft, and satellite, have been assimilated. These include data from polar-orbiting and geostationary satellites, radar-derived radial winds and reflectivity, Global Navigation Satellite System (GNSS) radio occultations, etc. To further utilize the advanced observing systems and DA techniques for high-impact weather predictions, target observation strategies have been developed to identify areas where additional observations can yield the greatest predict improvements. Based on the advancements of DA theories and methods, China’s operational systems have made significant progress, establishing advanced operational DA systems. Over the past decade, the forecast skill of 5-day global weather prediction has improved by approximately 15%. The article reviews a century of development in DA, and discusses future directions, including the advanced DA methods, operational frameworks, integration of novel observations, and the synergy between DA and artificial intelligence.

This paper reviews the development of ensemble weather forecast and the primary techniques employed in the main ensemble prediction systems (EPSs) designed by China and other countries. Here, the emphasis is placed on the advancements in the China Meteorological Administration (CMA) global and regional ensemble prediction systems (i.e., CMA-GEPS and CMA-REPS), with particular attention to operational technologies such as initial and model perturbation methods and the applications of ensemble forecast. Through comparative verification with EPSs from other leading international numerical weather prediction (NWP) centers, CMA’s EPSs demonstrate forecast skills comparable to its global counterparts. As EPSs progress to convective scales and coupled systems between sea, land, air, and ice, the paper addresses some key challenges in ensemble forecast technologies across the aspects of operation, science, integration of artificial intelligence (AI), merging of weather and climate models, and challenging user requirements. Finally, a summary of conclusions and future perspectives on ensemble forecast are provided.