The applicability of research on moving cut data-approximate entropy on abrupt climate change detection
In this study, the performance of moving cut data-approximate entropy (MC ApEn) to detect abrupt dynamic changes was investigated.Numerical tests in a time series model indicate that the MC-ApEn method is suitable for the detection of abrupt dynamic changes for three types of meteorological data: daily maximum temperature, daily minimum temperature, and daily precipitation.Additionally, the MCApEn method was used to detect abrupt climate changes in daily precipita6on data from Northwest China and the Pacific Decadal Oscillation (PDO) index.The results show an abrupt dynamic change in precipitation in 1980 and in the PDO index in 1976.The times indicated for the abrupt changes are identical to those from previous results.Application of the analysis to observational data further confirmed the performance of the MC-ApEn method.Moreover, MCApEn outperformed the moving t test (MTT) and the moving detrended fluctuation analysis (MDFA) methods for the detection of abrupt dynamic changes in a simulated 1000-point daily precipitation dataset.
Hongmei Jin Wenping He Qunqun Liu Jinsong Wang Guolin Feng
Gansu Meteorological Information and Technical Equip Safeguard Center, Gansu Meteorological Bureau, National Climate Center,China Meteorological Administration, Beijing, China College of Mathematics and Statistics,Nanjing University of Information Science and Technology,Nanji Institute of Arid Meteorology of CMA, Key Open Laboratory of Arid Climatic Change and Disaster Reduc
国内会议
天津
英文
1-12
2015-10-01（万方平台首次上网日期，不代表论文的发表时间）