Abstract
Abstract. 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. At the 120 km resolution, similarities between the CNOPs of the ADJ method and the ACPW algorithm are more than those at the 60 km resolution. Compared to the ADJ method, although the CNOPs of the ACPW method produce lower energies, they can have improved benefits gained from the reduction of the CNOPs not only across the entire domain but also in the identified sensitive regions. The sensitive regions identified by the CNOPs from the ACPW algorithm have the same influence on the improvements of the skill of TC-track forecasting as those identified by the CNOPs from the ADJ method. The ACPW method is more efficient than the ADJ method. All conclusions prove that the ACPW algorithm is a meaningful and effective method for solving CNOPs and can be used to identify sensitive regions of TC adaptive observations.
Highlights
Tropical cyclones (TCs) are one of the most frequent and influential natural hazards in the world
In this paper, we propose a novel approach, the adaptive cooperative coevolution of parallel particle swarm optimization (PSO) and Wolf Search algorithm (WSA) (ACPW) based on the PCA to solve conditional nonlinear optimal perturbations (CNOPs) for the sensitive regions identified by TCAOs
We further investigate the validity of the sensitive regions identified by the CNOPs through using a comparison of simulated tropical cyclone (TC) tracks predicted by the MM5 model for each case by inserting the CNOPs or W × CNOPs into the initial states
Summary
Tropical cyclones (TCs) are one of the most frequent and influential natural hazards in the world. An accurate forecast of TCs is conducive to the response of the government and people. It is essential to improve the skill of TC forecasting. One effective way is to identify the sensitive regions of TC adaptive observations (TCAOs) (Franklin and Demaria, 1992; Bergot, 1999; Aberson, 2003). Once observations in sensitive regions are identified and added to reduce initial errors, better forecasts will be expected L. Zhang et al.: ACPW to slove CNOP in TCAOs
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