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A novel approach for solving CNOPs and its application in identifying sensitive regions of tropical cyclone adaptive?observations

机译：解决CNOP的新方法及其在识别热带气旋适应性观测敏感区域中的应用

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In this paper, a novel approach is proposed for solving conditional nonlinear optimal perturbations?(CNOPs), called the adaptive cooperative coevolution of parallel particle swarm optimization (PSO) and the Wolf Search algorithm (WSA) based on principal component analysis (ACPW). Taking Fitow?(2013) and Matmo?(2014), two tropical cyclone?(TC) cases, CNOPs solved by the ACPW algorithm are used to investigate the sensitive regions identified by TC adaptive observations with the fifth-generation Mesoscale Model?(MM5). Meanwhile, the 60 and 120 km resolutions are adopted. The adjoint-based method (short for the ADJ method) is also applied to solve CNOPs, and the result is used as a benchmark. To evaluate the advantages of the ACPW algorithm, we run the PSO, WSA and ACPW programs 10 times and then compare the maximum, minimum and mean objective values as well as the RMSEs. The analysis results prove that the hybrid strategy and cooperative coevolution are useful and effective. To validate the ACPW algorithm, the CNOPs obtained from the different methods are compared in terms of the patterns, energies, similarities and simulated TC tracks with perturbations. The results of our study may be summarized as follows: The ACPW algorithm can capture similar CNOP patterns as the ADJ method, and the patterns of TC Fitow are more similar than TC Matmo.
机译：本文提出了一种解决条件非线性最优摄动的新方法，即基于主成分分析（ACPW）的并行粒子群优化（PSO）和沃尔夫搜索算法（WSA）的自适应协同协进化。以Fitow？（2013）和Matmo？（2014）这两个热带气旋（TC）案例为例，利用ACPW算法求解的CNOP被用于调查由第五代中尺度模型（TC5）进行的TC自适应观测所识别的敏感区域。 ）。同时，采用了60和120 km的分辨率。基于伴随的方法（ADJ方法的缩写）也用于求解CNOP，并将结果用作基准。为了评估ACPW算法的优势，我们运行了10次PSO，WSA和ACPW程序，然后比较了最大，最小和平均目标值以及RMSE。分析结果表明，混合策略和协同协同进化是有效的。为了验证ACPW算法，比较了从不同方法获得的CNOP的模式，能量，相似性和带有扰动的模拟TC轨迹。我们的研究结果可以归纳如下：ACPW算法可以捕获与ADJ方法相似的CNOP模式，并且TC Fitow的模式比TC Matmo更相似。

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