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高守亭, 冉令坤, 李娜, 张昕. 2013: 集合动力因子暴雨预报方法研究. 暴雨灾害, 32(4): 289-302. DOI: 10.3969/j.issn.1004-9045.2013.04.001
引用本文: 高守亭, 冉令坤, 李娜, 张昕. 2013: 集合动力因子暴雨预报方法研究. 暴雨灾害, 32(4): 289-302. DOI: 10.3969/j.issn.1004-9045.2013.04.001
GAO Shouting, RAN Lingkun, LI Na, ZHANG Xin. 2013: The“Ensemble Dynamic Factors”approach to predict rainstorm. Torrential Rain and Disasters, 32(4): 289-302. DOI: 10.3969/j.issn.1004-9045.2013.04.001
Citation: GAO Shouting, RAN Lingkun, LI Na, ZHANG Xin. 2013: The“Ensemble Dynamic Factors”approach to predict rainstorm. Torrential Rain and Disasters, 32(4): 289-302. DOI: 10.3969/j.issn.1004-9045.2013.04.001

集合动力因子暴雨预报方法研究

The“Ensemble Dynamic Factors”approach to predict rainstorm

  • 摘要: 介绍了广义位温、湿热力平流参数、热力螺旋度、热力散度垂直通量、广义湿位涡、力管涡度、热力力管涡度、二级位涡、对流涡度矢量和波作用密度等宏观物理量的定义及其物理意义。个例分析表明,这些动力因子与降水系统发展演变密切相关,对地面观测降水有一定的指示作用。这主要是因为: (1) 这些因子能够描述降水系统的动、热力垂直结构等共性特征;(2) 这些因子大部分包含广义位温,而广义位温又与凝结潜热和相对湿度有关,因而这些因子也能描述降水系统的水汽场结构特点。以这些动力因子为基础建立了集合动力因子预报方法,该方法首先建立以GFS 预报场资料为基础的单动力因子降水预报方程,然后根据其与观测降水的相关性,定义权重函数,对多个动力因子的降水预报进行权重平均,最后得到集合动力因子的降水预报。该预报方法可以充分发挥多个动力因子的优势,比较全面地反映暴雨过程的共性特征。长时间序列的统计检验表明,集合动力因子的降水预报评分略高于全球预报系统(GFS)模式自身的降水预报评分,表现在降水落区预报方面,集合动力因子的预报效果略优于GFS 模式的自身预报,然而,在降水强度预报方面,集合动力因子和GFS 模式都略有过度预报。集合动力因子预报方法计算量小,容易移植,可以提供降水预报产品,为预报员做暴雨预报提供支持。

     

    Abstract: In this paper, we describe the definition and physical meaning of several important physical variables, such as the generalized potential temperature, the moist thermodynamic advection parameter, the thermodynamic helicity, the vertical flux of the thermodynamic divergence,the moist potential vorticity, the solenoidal vorticity and the thermodynamic solenoidal vorticity, the second-order potential vorticity,the convective vorticity vector, the wave-activity density and so on. Case studies show that these parameters have close correlation to the evolution of precipitating systems and can detect the occurrence and development of rainfall. This is mainly due to the following two reasons. First, these dynamic parameters can describe the common dynamic and thermodynamic features of precipitating systems. Second, since most of these parameters contain the generalized potential temperature which is related to the condensation latent heating and relative humidity,they implicitly reflect the structure of atmospheric moisture. Based on these parameters, an“ensemble dynamic factors”approach to predictheavy rainfall is developed. In this approach, the precipitation forecasting equation as a function of a single dynamic factor is built first using the GFS reanalysis data. Then, according to the correlation coefficients between the analyzed precipitation from different parameters and the observed precipitation, weighting functions, which measure the contribution of the precipitation obtained from a single parameter to the totalprecipitation, are developed. Based on these weighting functions, a weighted average of the precipitations from all the dynamic parameters is conducted, which gives the final precipitation forecast. This approach combines the advantages of multiple dynamic parameters, and can reflect the common characteristics of the rainfall processes. Statistical verification with a long time series shows that the precipitation forecastscore of the ensemble dynamic factors is higher than that of the GFS model, although both of them overestimate the precipitation intensity. The “ensemble dynamic factors”approach to predict precipitation is able to generate the product of precipitation forecast, and thus can provideassistance to forecasters.

     

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