Application of Nonparametric Methods in Short-range Precipitation Forecasting

摘要：

Short-range precipitation forecasting plays a key role in developing public affairs. Seasonal autoregressive integrated moving average (ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been proposed as well suited for application to short-range precipitation forecasting. In this paper, the method of the k-nearest neighbor estimation in the nonparametric regression is discussed, and this method is used to establish the day-by-day rainfall forecast of southeastern of Guangxi during the period from May to June. Results show that forecasts from the nonparametric regression scheme are high stability, with good prospects in operational weather forecast.

作者: Jifu Nong

作者单位: College of Mathematics and Computer Science Guangxi University for Nationalities Guangxi, Nanning, 530006, China

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 1106-1108

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）