INTELLIGENT ALGORITHMS FOR SOLVING CNOP AND THEIR APPLICATIONS IN ENSO PREDICTABILITY AND TROPICAL CYCLONE ADAPTIVE OBSERVATIONS

作者：穆斌,张琳琳,袁时金,钱一闻,温仕成,颜景豪,周菲凡

摘要：Some intelligent algorithms(IAs)proposed by us,including swarm IAs and single individual IAs,have been applied to the Zebiak-Cane(ZC)model to solve conditional nonlinear optimal perturbation(CNOP)for studying El Ni?o-Southern Oscillation(ENSO)predictability.Compared to the adjoint-based method(the ADJ-method),which is referred to as a benchmark,these IAs can achieve approximate CNOP results in terms of magnitudes and patterns.Using IAs to solve CNOP can avoid the use of an adjoint model and widen the application of CNOP in numerical climate and weather modeling.Of the proposed swarm IAs,PCA-based particle swarm optimization(PPSO)obtains CNOPs with the best patterns and the best stability.Of the proposed single individual IAs,continuous tabu search algorithm with sine maps and staged strategy(CTS-SS)has the highest efficiency.In this paper,we compare the validity,stability and efficiency of parallel PPSO and CTS-SS using these two IAs to solve CNOP in the ZC model for studying ENSO predictability.The experimental results show that CTS-SS outperforms parallel PPSO except with respect to stability.At the same time,we are also concerned with whether these two IAs can effectively solve CNOP when applied to more complicated models.Taking the sensitive areas identification of tropical cyclone adaptive observations as an example and using the fifth-generation mesoscale model(MM5),we design some experiments.The experimental results demonstrate that each of these two IAs can effectively solve CNOP and that parallel PPSO has a higher efficiency than CTS-SS.We also provide some suggestions on how to choose a suitable IA to solve CNOP for different models.

发文机构：School of Software Engineering Laboratory of Cloud-Precipitation Physics and Severe Storms University of Chinese Academy of Sciences

关键词：CNOPsingleindividualINTELLIGENTALGORITHMSswarmINTELLIGENTALGORITHMSENSOPREDICTABILITYtropicalcycloneadaptiveobservations

分类号： P732[天文地球—海洋科学]P444