基于快速更新同化的滞后短时集合预报试验及检验

Time-Lag Ensemble Forecasting Experiment and Evaluation Based on SMB-WARR

本文基于华东区域中尺度快速更新同化数值预报模式系统(Shanghai Meteorological Bureau-WRFADAS Rapid Refresh System,SMB-WARR),应用时间滞后法进行集合预报试验,试验由7个集合预报成员组成,进行逐小时预报,预报时效为6 h。本文通过对2011年6月17日至9月30日期间降水过程进行集合预报试验,引入面雨量对降水预报进行检验。结果表明:(1)小-大雨量级,集合平均优于集合成员预报,但暴雨量级集合平均不及部分集合成员预报。(2)不同的降水量级,并非都是最接近预报时刻,技巧最高,小-大两量级的降水可以提前6 h预报。(3)降水概率预报优于集合平均预报,具有很好的指示作用。可关注最接近预报时刻,小-中雨以上的降水,预报概率较大时,其可用性较大,而大-暴雨以上的降水,预报概率较小时,可用性较大。

Based on East China regional mesoscale numerical forecast model system （Shanghai Meteorological Bureau WRF ADAS Rapid Refresh, SMB WARR）, the time lag ensemble forecasting experiment with 7 ensemble members is conducted and routinely updated every hour with hourly output through 6 h forecast length. Evaluation of the hourly area rain of Shanghai from June 17 to September 30, 2011 shows that ensemble mean has better performance than any other members at light to heavy rain levels but worse performance than some members at rainstorm level. Meanwhile, the skill of the latest forecast is not the best and the light to heavy rain could be well forecasted 6 h in ahead. Furthermore, the probability forecast has an advantage over the ensemble mean, and has good directions for the happening of rain in the very short range, also the large （little） probability is more useful at light to moderate rain levels （heavy rain to rainstorm levels） especially the latest forecast.