基于混合回归模型的夏季高温日数预测  

TEST PREDICTON FOR MULTI-REGRESSION MODEL ON HIGH TEMPERATURE DAYS

作　　者：周后福[1] 王兴荣[1] 翟武全[1] 钱玉萍[1] 

机构地区：[1]安徽省气象局,合肥230031

出　　处：《气象科学》2005年第5期505-512,共8页Journal of the Meteorological Sciences

基　　金：安徽省科技攻关计划(编号:01013042);安徽省气象科技基金(编号:0307)共同资助

摘　　要：本文以变化比较激烈的高温日数为对象,通过加权叠加、周期波动以及尺度比较等理论分析提出一种短期气候预测观点,即长周期波动可用当地前期各种气象要素的长周期被动的某种函数表示,而短周期波动依然与大范围周边环境因子的变化有关.采用功率谱分析、逐步回归、方差分析等常用统计手段和二项式平滑方法,建立长周期波动和波动偏差比的预报方程,在此基础上得到高温日数的预报方程.应用该方法对合肥地区的高温日数进行了预测试验,利用滚动预报得到13个预报样本.在13个试测样本中,有11个样本试测较为准确,试测准确率达到84.6%.在试测不准确的2个样本中,误差有5～6 d,而且它们出现在高温日数相对较多的年份,因此试测效果令人满意.In the light of high temperature days of drastic variety, a new viewpoint is put forward to predict short-term climate by theoretical analysis such as weighing superposition, periodic change, scaling analysis etc. We found that long periodic change is often related to local forward long periodic change and that short periodic change is closely related to ambient environment factors. By using the means such as power analysis, binomial smoothing, step-by-step regression etc. , prediction equations of long periodic change and short periodic change are established. Based on them, prediction equation of high temperature days is found. Using the method, Hefei＇s high temperature days is tried out. 13 forecast samples are given with adopting rolling forecast. 11 forecasts among them are accurate, so forecast accuracy attains 84. 6%. Even in inaccurate 2 samples errors only amount 5-6 d. For this reason the forecast result is satisfying.

关 键 词：长周期波动 短周期波动 短期气候预测 高温日数 

分 类 号：P461[天文地球—大气科学及气象学]