%0 Journal Article
%T Dealing with Imperfect Data to Improve EstimationPrecision of Turbulence Flux<br>处理时间序列提高计算湍流通量的精度
%A Chen Hongyan
%A Hu Fei
%A Zeng Qingcun
%A <br>陈红岩
%A 胡非
%A 曾庆存
%J 气候与环境研究
%D 2000
%I 
%X The observational data are seldom perfect, they are usually short noisy with wild points and nonlinear trends. In this paper, we talk about how to delete noise, wild points and filter nonlinear trends, how nonlinear trends contribute fake turbulence flux by having influence on self-relation func- tion and integral scale.
%K time series analysis
%K nonlinear trend
%K turbulence flux<br>时间序列分析
%K 非线性趋势
%K 湍流通量
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=28A2F569B2458C17&jid=484C10BAB5333C14751916EACFF8295F&aid=AFCFD31A8798F35B&yid=9806D0D4EAA9BED3&vid=94C357A881DFC066&iid=38B194292C032A66&sid=8ED630AD8C61FAE8&eid=A2745AA1110798CA&journal_id=1006-9585&journal_name=气候与环境研究&referenced_num=10&reference_num=4