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LAPS系统中两种资料融合方法对台风“海葵”的预报对比分析

Comparison of two LAPS data merging methods for typhoon "HaiKui" forecasting

  • 摘要: LAPS分析系统包含两种资料融合方法,一种是基于修正的Barnes插值方法(LAPS方法),另一种是基于连续变分的融合方法(STMAS方法)。本文以2012年11号台风“海葵”为例,对LAPS分析系统中这两种方法进行对比分析。结果表明: (1) 两种分析方法均能较好分析出台风的环流结构和中心;其中,LAPS方法分析的台风强度略偏弱,但包含小尺度系统较多;相比较而言,STMAS 方法分析的台风强度偏强,分析场更加连续、平滑。(2) 对高度场、湿度场以及中低层风场和温度场,STMAS方法分析场更加接近探空观测。(3) 以LAPS方法分析场为初始场,预报的台风路径误差较STMAS方法大,但对 100 mm以上的强降水中心的预报TS评分较高,0—6 h降水预报TS评分也明显高于STMAS方法;而以STMAS方法分析场为初始场,预报的台风强度强于实况,但对大于25 mm和50 mm的雨区预报优于LAPS方法,12—24 h降水预报TS评分也较优。(4) 在该台风个例中,对两种方法的降水预报进行平均,所得到的降水预报场具有相对较高的TS评分。

     

    Abstract: There are two kinds of data immerging methods in the LAPS system. The early method is based on the Barnes interpolation method, namely the "LAPS method". The latest method is based on the continuous variation method, namely the "STMAS method". These two methods have been applied in analyzing and forecasting a typhoon case (HaiKui, 201211) during 2012. The results are as follow. (1) The typhoon center and the circulation structure cloud be revealed accurately in the output fields of two analysis methods. For the analysis fields based on the "LAPS method", the analyzed intensity of the typhoon is weaker than observed, but more small-scale systems could be found within it. For the results of the "STMAS method", the analyzed intensity of the typhoon is stronger, and the analysis fields are more continuous and smooth than observations. (2) For geopotential height, humidity, low-level wind and temperature fields, the analyses based on the "STMAS method" are closer to soundings than those from the "LAPS method". (3) When WRF model is initialed with the "LAPS method" analysis field, typhoon path forecasting error was bigger than when it is initialed with the "STMAS method". But threat scores for 0-6 h lead time and heavy rainfall center (more 100 mm) 24 h forecasts based on the "LAPS method" are both better than those based on the "STMAS method". When WRF model is initialed with the "STMAS method" analysis field, the typhoon strength forecasting was slightly stronger than observation. Furthermore, the threat scores for 12-24 h lead time and middle rainfall (more than 25 mm and 50 mm) 24 h forecast are both better than traditional method. (4) The threat score of precipitation forecasting for the average of two methods is higher than any one of two methods.

     

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