最优子集神经网络在武威气温客观预报中的应用

The Application of Optimal Subset Neural Network to Temperature Objective Forecast in Wuwei

选取2003年3月1日至2008年12月31日20时的逐日ECMWF(欧洲中期天气预报中心)数值预报产品实况格点资料,使用差分法、天气诊断、因子组合等方法,构造出能反映本地天气动力学特征的预报因子库,采用PRESS(预测平方和)准则初选因子,逐步回归复选因子,最优子集回归精选因子,建立分月、分站点逐日最高、最低温度BP神经网络预报模型。模型业务试用结果表明:该BP神经网络预报模型具有较强的非线性处理能力,能较好地反映日极端温度的变化,0～120 h内的最高、最低温度平均预报准确率达较高水平,且对明显的升降温过程反应灵敏,升降温趋势和幅度预报较为准确,为0～120 h的城镇精细化温度预报提供了重要的技术支撑,同时也为ECMWF数值预报产品在温度的释用提供了一种好的思路和方法。

Selecting daily ECMWF（European Center for Medium-Range Weather Forecasts） numerical forecast grid field data at 20：00 BT from March 1,2003 to December 31,2008,the forecast factor database that can reflect the local weather dynamic characteristics is constructed by using such methods as difference method,weather diagnosis and factor combination.And a BP neural network prediction model of the daily highest and minimum temperatures of various months and stations is established by first,roughly checking factors with PRESS（prediction square sum） criteria,second,checking again factors with stepwise regression, and finally,careful checking factors with optimal subset regression,thus the 1—5 day test forecast of maximum and minimum temperatures is done.The result of operational model trial shows that,the BP neural network prediction model has a strong nonlinear processing capability,and can better reflect the changes of daily extreme temperature,thus the average forecast accuracy of 1—5 day maximum and minimum temperatures reaches to higher levels.It is sensitive to warming and cooling processes.The trend and range forecasts of warming and cooling are more correct.It provides an important technical support to the precise town temperature forecast within 1—5 days.Meanwhile,it is a good idea and method of the application of the ECMWF numerical forecast products to temperature forecast.