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Colder-Winter Monthly-Temperature Forecasting in General Trend of Global Warming Via Multiple Sine Functions Decomposition

Yunong Zhang  Maotai Zou  Shuo Yang  Yingbiao Lin  Chengxu Ye  
【摘要】:Over the past 100 years,especially in the recent 20 years,the global average surface temperature has increased significantly,and global warming has become an important issue of global concern.In this paper,multiple sine functions decomposition(MSFD) method is presented to forecast the colder-winter years of Guangzhou.MSFD method is an effective forecasting method,which has been applied to long-term temperature trend forecasting as well as temperature data restoration.Generally,the MFSD method firstly decomposes a historical temperature sequence into multiple sine functions.Then,by adding up those learning or fitting sine functions,a temperature model is constructed to forecast future temperature.Based on the data of the monthly-temperature(MT) from 1951 to 2014,a sequence(or say,set) of future temperature data is shown in this paper via the MSFD method.The result exhibits Guangzhou's temperature trend in the next three decades,and it matches the trend of global warming well.At the same time,several potential colder-winter years are pointed out.
【作者单位】:School of Information Science and Technology,Sun Yat-sen University(SYSU) School of Computer Science,Qinghai Normal University
【基金】:supported by the Foundation of Key Laboratory of Autonomous Systems and Networked Control,Ministry of Education,China(with number 2013A07) the Science and Technology Program of Guangzhou,China(with number 2014J4100057) the Science and Technology Program of Qinghai,China(with number 2016-ZJ-739)
【分类号】:P467;X16
【正文快照】:
1Introduction The issue of global warming is increasingly concerned by the whole world.In September 2013,the Intergovernmental Panel on Climate Change(IPCC)released the fifth scientific assessment report of Working Group I,and in 2014,the fifth assessm

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