全球和区域分析的混合方案对区域预报的影响试验

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

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。

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 kinetic 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.