%0 Journal Article
%T Application of Statistical Downscaling in Minimum Temperature Prediction over Qinghai Province in Winter<br>统计降尺度方法在青海省冬季最低温度预测中的应用
%A ZENG Xiao-qing
%A WANG Shi-gong
%A LIU Huan-zhu
%A SHANG Ke-zhen
%A <br>曾晓青
%A 王式功
%A 刘还珠
%A 尚可政
%J 高原气象
%D 2009
%I 
%X Using the daily numerical weather prediction output products from National Meteorological Center global medium-term model(T213L31) from 2003 to 2007 and observation data of 25 meteorological stations in Qinghai region as test data.The grid output products include 14 day-to-day weather elements,13 levels and 5 forecast times for each elements.The correlation coefficient and stepwise regression are used as methods of preprocessing for building model. Single hidden layer neural network is used as the statistical downscaling methods. The train of network employs the back-propagation that performs a gradient descent algorithm. Hidden layer function is Sigmoid function and output activation function is the line function. According to the six kinds of options,24 h,48 h and 72 h forecast models are established for the minimum temperature at 25 weather stations of Qinghai Province in winter. Using December of 2006 and from January to February of 2007 as 24 h,48 h,72 h are as daily minimum temperature forecast test period. The results showed that the forecasting hit ratio in the northern part of Qinghai is better than that in the southern Plateau. Among the four kinds of options,selecting the numerical prediction results of grids around the site as a major factor combined with the history observation data is relatively optimum method. With the extension of forecast time,the effect of 24 h-history observation as predictor grow gradually weaker. However,No one scheme can completely replace all the other schemes in six kinds of schemes for all stations. In actual operations,the different schemes for different stations are taken to forecast the real forecast.
%K Qinghai Province
%K Statistical downscaling
%K ANN
%K Minimum temperature
%K Hate rate<br>青海省
%K 统计降尺度
%K 神经网络
%K 最低温度
%K 命中率
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=28A2F569B2458C17&jid=FCD959CAD9EF92C38C4323B51D54F3DB&aid=3FAF195A1B534292B6733B03CFAD4A0F&yid=DE12191FBD62783C&vid=D3E34374A0D77D7F&iid=B31275AF3241DB2D&sid=4BA709A0F998E415&eid=E9CEAC7B2ECF0E2F&journal_id=1000-0534&journal_name=高原气象&referenced_num=2&reference_num=20