利用改进的并行粒子群算法对变分资料同化的研究

近年来,为了提高同化精度和减少同化时间,粒子群算法(PSO)被引入到数值天气预报资料同化中来.粒子群算法虽然令同化精度有所提高,但同化时间仍然存在较大缺陷.基于此,首先设计了一种改进的并行粒子群算法(P2PSO),然后应用于含不连续"开关"过程的变分资料同化中,与时变双重压缩因子粒子群算法(PSOTVCF)和动态权重粒...

Research on variational data assimilation based on improved parallel particle swarm optimization

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.