摘要
以均生函数表征预报量自身周期变化,结合500hPa月平均高度场和月平均海温场预报因子,采用神经网络方法建立了一种新的短期气候预报模型。分别以广西桂北、桂中和桂南6月降水量作为预报对象进行预报试验,结果表明,这种新的预报方法比均生函数回归预报模型及高度场、海温场预报因子的回归预报模型,具有更好的物理基础和预报能力。
Based on itselfperiod change of predictand characterized by mean generating function method, 500 hPa monthly mean height field and the predictor of monthly mean SST, a new shortterm forecast pattern is established by the artficial neural. June rainfall in north, center and southparts of Guangxi, respectively, as predicative object is carried out predicative experiment. The results show that the new method has more better prediction ability and physical foundation than the regression prediction patterns of mean genrating function, 500 hPa height field and SST.
出处
《高原气象》
CSCD
北大核心
2003年第6期618-623,共6页
Plateau Meteorology
基金
国家自然科学基金项目(40075021)资助
关键词
混合模型
月降水量
人工神经网络
Mixed forecast model
Monthly rainfall
Artificial neural network
