【摘要】 通过对局地集合变换卡尔曼滤波(LETKF)算法的计算时间复杂度的完整分析,发现计算集合空间分析场误差协方差的逆矩阵这一过程计算量最大,耗时最长。且在并行计算环境下,该步骤CPU计算量分配不均是影响计算效率的直接原因。为解决这一问题,采用"贪心算法"设计了一套新的负载均衡策略,并使用该策略开发了一个基于LETKF和并行海洋模块2(POP2)的高性能并行海洋资料同化系统。将2004年1～2月日平均的最优插值海表温度资料(OISST)和同时期的Argo温盐剖面资料同化进入POP2。结果表明,同化有效降低了温度和盐度的均方根误差。同时,在不改变计算结果的前提下,相比原始同化系统,新系统计算性能提升1倍。在更高分辨率(0.1°×0.1°)下,该系统的计算性能仍然可以提升1倍,说明新设计的负载均衡方案稳定可靠。该方案具有很强的可扩展性和移植性,在业务预报中有广泛的应用前景。更多还原

【Abstract】 An integrated analysis about computational time complexity of the Local Ensemble Transform Kalman Filter(LETKF) was performed. It is found that the calculation step of inverse matrix of the error covariance in ensemble space is the most computationally intensive and time consuming. In a parallel computing environment,the uneven distribution of CPU calculations in this step directly leads to low computational efficiency. To solve this problem,a new load balancing strategy was designed based on the "greedy algorithm". A high-performance parallel ocean data assimilation system based on the LETKF was developed and tested using this strategy. This system was based on the Parallel Ocean Program 2(POP2)of the Community Earth System Model(CESM). The optimal interpolated sea surface temperature data(OISST)and Argo temperature profile data from January to February,2004 were assimilated into the POP2. The results show that data assimilation effectively reduces the root mean square error of temperature and salinity. Using the new strategy,the exact same results are obtained but the computation time is reduced by half. At higher resolution(0.1°×0.1°),the computing performance is still doubled,indicating that this load balancing scheme is stable and reliable. In addition,the new method has high scalability and portability with great potential to be applied in operational forecasting.更多还原