全球和区域分析的混合方案对区域预报的影响试验

区域分析采用局地高密观测资料,往往分析的中小尺度信息较丰富;全球分析同化全球覆盖的卫星观测资料,而且不受侧边界条件影响,对天气系统大尺度部分描述得更好。基于以上原因,为了提高区域模式的分析预报水平,本文尝试在区域模式中引入全球分析中的大尺度部分,通过DCT方法对全球T639模式分析和区域GRAPES模式分析进行谱分解,获得综合全球大尺度和区域中小尺度信息的混合分析。动能谱分析结果表明,GRAPES分析场的动能谱能量在中小尺度部分比T639全球分析略大,经过混合分析后,动能谱能量更接近理想曲线。一个月的分析预报试验结果表明,混合分析的高度、温度和风场的分析质量有明显提高,风场6 h内的预报也比区域分析有改进;而且全国平均ETS降水评分在各个降水量级上都有明显改进。

Impact Experiments for Regional Forecast Using Blending Method of Global and Regional Analyses

The highly dense observations are used in the regional analysis, so the regional data assimilation may produce more accurate small scale analysis. However, the large scale aspect of a global analysis is superior to that of a regional analysis for assimilating more observations of the satellite observations and is not affected by lateral boundaries. To improve the forecast qualities of regional model, a blending method to merge the T639 global analysis with the regional analysis from the GRAPES Meso analysis system is implemented using discrete cosine transform (DCT) filter. The experiments show that the simulated kine tic energy spectrum of GRAPES analysis is a little bigger than T639 analysis in the small meso scale. Meanwhile, the simulated kinetic energy spectrum of blending analysis is the closest to the atmospheric kinetic energy spectrum in the small meso scale. The results indicate the qualities of geopotential height, temperature and wind of blending analysis are obviously improved, the forecast of wind within 6 h is improved, and the ETS verification of accumulated precipitation of blending analysis is higher than the regional analysis.