文献类型：会议论文

英文题名：Extraction of sea surface temperature variation regime at the short-time scale

作者：Zhang, Ying[1]; Zheng, Yanzi[2]; Mo, Haoming[1]

机构：[1] School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, China; [2] School of Management, Guangdong Ocean University, Zhanjiang, China

会议论文集：Proceedings of 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

会议日期：October 23, 2021 - October 25, 2021

会议地点：Manchester, United kingdom

语种：英文

外文关键词：Climate change - Submarine geophysics - Surface waters - Atmospheric temperature - Time measurement - Oceanography

外文摘要：The characteristic of periodic vibration existing in the process of sea surface temperature (SST) changing with time is called sea surface temperature change mode. Research on the patterns of SST variability on different time scales is meaningful to understand the air-sea interaction and climate change. For the temperature of the sea surface is a non-linear, non-stationary signal, it should not be based on any function, but the Ensemble Empirical Mode Decomposition suitable for natural signals should be selected. Thus in this study, the analysis of high-spatial and merged SST productions over a 33-year period (1981~2014) are used to extract the patterns at different time scales, based on the EEMD. The results are not only the modes with a longer time scale, such as inter-annual and inter-decadal and so on, but also the short time scale modes, such as day, week, ten days and month. These patterns are the response of SST to short-time plus compulsion, which are real and inherent, thus providing an important complement to the methods and theories of SST patterns. ? 2021 ACM.