1.School of Science， Hubei University of Technology， Wuhan 430068， China；2.Hubei Engineering Technology Research Center of Energy Photoelectric Device and System， Wuhan 430068， China
Abstract：In recent years， in order to improve assimilation precision and reduce assimilation time， Particle Swarm Optimization (PSO) algorithm has been introduced in data assimilation in numerical weather prediction. Although the convergence accuracy has been improved， the assimilation time still has defects. To solve the question， an improved Parallel Particle Swarm Optimization algorithm (P2PSO) was designed firstly， and then it was applied to variational data assimilation with discontinuous “on-off” process. Compared with Particle Swarm Optimization with Dynamic Inertia Weight and Acceleration Factor (PSODIWAF) and Particle Swarm Optimizer with Time Varying Constrict Factor (PSOTVCF) in assimilation speed， assimilation accuracy and convergence， the experimental results show that the designed improved Parallel Particle Swarm Optimization(P2PSO) algorithm reduces the assimilation time by half while maintaining certain advantages in convergence accuracy， and is obviously superior to PSODIWAF and PSOTVCF in the convergence speed.
2021, Vol. 55 
