CAJ | 학술논문

　　利用NCEP常规和导出月平均资料，采用旋转EOF、典型相关分析等方法分析浙江梅汛期和汛期降水量的空间分布，提取典型相关变量作为预报初选因子。然后根据因子差异法选取三种建模方案，分别构建浙江省梅汛期和汛期雨量BP人工神经网络预报模型，并连续进行四年预报检验，结果显示最佳的预报方案平均ps评分分别达到69分和84分，比较好的预报出降水趋势；最后对预报因子空间进行分析表明：（1）NCEP导出资料在汛期降水预测中具有重要作用；（2）梅汛期和汛期因子在全球范围内均有五个集中区，其中四个区分别与ENSO、冰岛低压、阿留申低压等大气活动中心相对应；（3）较长时段的汛期雨量预报需要考虑南半球和北半球的大气环流因子影响；（4）大气能量收支因子对短期气候预测具有十分重要意义。
　　이용NCEP상규화도출월평균자료，채용선전EOF、전형상관분석등방법분석절강매신기화신기강수량적공간분포，제취전형상관변량작위예보초선인자。연후근거인자차이법선취삼충건모방안，분별구건절강성매신기화신기우량BP인공신경망락예보모형，병련속진행사년예보검험，결과현시최가적예보방안평균ps평분분별체도69분화84분，비교호적예보출강수추세；최후대예보인자공간진행분석표명：（1）NCEP도출자료재신기강수예측중구유중요작용；（2）매신기화신기인자재전구범위내균유오개집중구，기중사개구분별여ENSO、빙도저압、아류신저압등대기활동중심상대응；（3）교장시단적신기우량예보수요고필남반구화북반구적대기배류인자영향；（4）대기능량수지인자대단기기후예측구유십분중요의의。
Based on REOF and CCA methods, the rainfall between May and July and between May and September at 38 stations in flood season from 1961 to 2005 are analyzesd and the canonical variables are taken as prediction factors by use of NCEP monthly data. Then based on three schemes the BP artificial neural network prediction models are set up. The results of prediction tests from 2006 to 2009 show that the ps scores for the best scheme reach 69 and 84 corresponding to May to July and May to September respectively. The spatial analyses of prediction factors show that (1) The derived NCEP reanalysis data play an important role. (2) The factors of May to July and May to September have five concentration zones. The four zones are corresponding to atmospheric active centers of ENSO、Iceland depression and Aleutian depression. (3) The precipitation prediction of longer flood season needs to consider both the factors of circulation of north and south hemishere. (4) The budget factors of atmospheric energy are very important to climate prediction.