利用2007年全球降水气候计划GPCP(the Global Precipitation Climatology Project)卫星红外窗口导出的全球降水指数GPI(the Global Precipitation Index)的日降水资料及频率-波数分析方法,分析2007年南海夏季风季节内振荡(Intraseasonal...利用2007年全球降水气候计划GPCP(the Global Precipitation Climatology Project)卫星红外窗口导出的全球降水指数GPI(the Global Precipitation Index)的日降水资料及频率-波数分析方法,分析2007年南海夏季风季节内振荡(Intraseasonal Oscillation,ISO)的传播特征,并使用美国国家环境预报中心(NCEP)/美国大气研究中心(NCAR)再分析的逐日资料,探讨影响其传播的主要因子。结果表明,南海夏季风ISO有明显的北传趋势,并且明显比南传分量占优。影响南海夏季风ISO北传的主要因子是平均纬向风垂直切变和平均经向风对异常水汽的输送。之所以异常经向风对平均水汽的输送及海-气相互作用的影响在南海地区不重要,而在印度季风区有一定的贡献,是因为平均水汽和纬向风分布在两个地区的差异。展开更多
It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting ...It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.展开更多
文摘利用2007年全球降水气候计划GPCP(the Global Precipitation Climatology Project)卫星红外窗口导出的全球降水指数GPI(the Global Precipitation Index)的日降水资料及频率-波数分析方法,分析2007年南海夏季风季节内振荡(Intraseasonal Oscillation,ISO)的传播特征,并使用美国国家环境预报中心(NCEP)/美国大气研究中心(NCAR)再分析的逐日资料,探讨影响其传播的主要因子。结果表明,南海夏季风ISO有明显的北传趋势,并且明显比南传分量占优。影响南海夏季风ISO北传的主要因子是平均纬向风垂直切变和平均经向风对异常水汽的输送。之所以异常经向风对平均水汽的输送及海-气相互作用的影响在南海地区不重要,而在印度季风区有一定的贡献,是因为平均水汽和纬向风分布在两个地区的差异。
基金Project(50374079) supported by the National Natural Science Foundation of China
文摘It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.
