华中区域模式三维变分中夏季背景误差协方差统计与对比试验

Statistics and comparative experiments for summer background error covariance in 3DVAR of central China regional model

利用2012年6月12日—8月31日华中区域中尺度业务数值预报模式(WRF)一日两次的预报结果,采用NMC方法对背景误差协方差(B)进行了统计,得到了基于华中区域业务模式框架、分辨率和区域地理特征的夏季背景误差协方差矩阵的回归系数、特征向量、特征值以及特征长度尺度,并对模式三重嵌套各区域B的统计结构特征进行了对比,结果表明不同区域B的统计结构特征差异明显,表明B与模式区域地理特征和分辨率等关系密切。为探讨不同B对模式预报的影响,采用WRF模式自带的通用B矩阵(CV3-B)及本文统计得到的本地化B矩阵两种方案对2013年6—8月进行了批量试验和统计检验,结果表明:采用本地化B后,24 h小雨、中雨、大雨和48 h中雨、大雨、暴雨降水预报TS评分皆有所提高。850 h Pa风、温度及2 m温度等要素场预报的均方根误差减小,但500 h Pa高度场均方根误差略有加大。暴雨个例的分析表明:不同B方案,对初值影响非常显著,本地化B方案分析的初值场更趋合理,因而改进了降水预报。

Based on twice-a-day forecast products of central China operational numerical model, a statistic calculation of the background error covariance（B） is conducted using the NMC method to obtain the regression coefficients, eigenvectors, eigenvalues and length-scales of summer B matrix with features of China central operational model framework, resolution and regional geography. The contrast analyses of the statistical structure characteristics of B in each triple-nested model domain denotes that B in different domain is of obvious difference, which indicates B has a close relationship with the regional geographical feature and model resolution. In order to investigate the impact of different upon the model forecasts, batch experiments and statistical verifications in the period from June to August 2013 are performed with the universal background error covariance CV3-B of the WRF model and the statistically-obtained localized background error covariance. The results indicate that, when using the localized, the threshold scores of the 24-h forecasts of light rain, moderate rain, and heavy rain and the48-h forecasts of moderate rain, heavy rain, and torrential rain are higher than those when using the CV3-B. The root mean square error of meteorological element fields forecast such as 850-h Pa wind and temperature and 2-meter temperature is decreased. The root mean square error of 500-h Pa height is, however, increased. Analysis for a torrential rain case shows that different B has significant influence on model ini-tial fields. The localized gives a more reasonable initial field and therefore improves the rainfall forecasts.