集合预报产品在台风麦莎预报中的应用

Application of Ensemble Prediction Products to the Forecast of Typhoon Masta

为了提高集合预报效果,利用T106L19全球谱模式和增长模繁殖法对台风麦莎制作了13个成员的集合预报,并对其结果进行集合预报产品解释应用研究。首先在离散距离分簇法中,引入方差分析的方法确定分簇基础,然后对集合预报的形势场进行了分簇,相对于以往采用96小时样本为分簇基础进行分簇的方法,分簇效果提高明显,分簇结果能给出台风移动的几种可能路径及其概率;其次对要素随时间的演变利用系统聚类法进行了分类,根据分簇平均图可以确定几个可能的台风登陆地点,制作了这些特殊点要素分类烟羽图,通过分析要素随时间的演变特征,提高对台风登陆地点和时间的预报精度;最后制作了盒须图,通过盒须图中数据组主体以外的数据点,确认出值得注意的一些特殊集合成员的预报结果,减少了小概率事件的漏报率。研究结果表明,将方差分析引入离散距离分簇法,要素烟羽聚类法以及盒须图的应用有利于提高和改善集合预报效果,集合预报产品解释应用效果得到改进。

To improve the effect of ensemble prediction,a 13-member ensemble forecast of Masta was generated by using T106L19 model and breeding growth method（BGM）,and the research of the application of ensemble prediction products from the results was carried out.Firstly,the pattern fields of ensemble forecast were clustered through variance analysis according to discrete distance between members.Through this method,the pattern fields are grouped into three different clusters,especially the several possible tracks and the proportions of every cluster member are provided.The 96 h forecast results were used as the basis of clustering in the previous method of discrete distance clustering.In comparison with the previous method,the results of variance analysis are better.Secondly,the evolvement of variables is categorized by using the method of systemic classifying and several possible typhoon landing positions can be concluded from clustering averaged plots.Based on these special points,the plumes plot with systemic classifying of variables can be used to analyze the sea level pressure of the special points.Through discussing the evolvement characteristics of these points,the prediction results of the landing time and position of typhoon are more similar to observations than the cluster average.In addition,box-whisker plot has been presented.Several special ensemble members have been identified through the analysis of data beyond the main distribution of data.The information provided by the special points from box-whisker plot can reduce the missing rate of severe weather event.The research results indicate that the effects of prediction and the application of ensemble prediction products were improved by using the method of the clustering basis determined by using variance analysis,systemic classifying and box-whisker plot.