CAJ | 학술논문

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。
개소료엄의위온、습열력평류삼수、열력라선도、열력산도수직통량、엄의습위와、력관와도、열력력관와도、이급위와、대류와도시량화파작용밀도등굉관물리량적정의급기물리의의。개례분석표명，저사동력인자여강수계통발전연변밀절상관，대지면관측강수유일정적지시작용。저주요시인위：(1)저사인자능구묘술강수계통적동、열력수직결구등공성특정；(2)저사인자대부분포함엄의위온，이엄의위온우여응결잠열화상대습도유관，인이저사인자야능묘술강수계통적수기장결구특점。이저사동력인자위기출건립료집합동력인자예보방법，해방법수선건립이GFS예보장자료위기출적단동력인자강수예보방정，연후근거기여관측강수적상관성，정의권중함수，대다개동력인자적강수예보진행권중평균，최후득도집합동력인자적강수예보。해예보방법가이충분발휘다개동력인자적우세，비교전면지반영폭우과정적공성특정。장시간서렬적통계검험표명，집합동력인자적강수예보평분략고우전구예보계통(GFS)모식자신적강수예보평분，표현재강수락구예보방면，집합동력인자적예보효과략우우GFS모식적자신예보，연이，재강수강도예보방면，집합동력인자화GFS모식도략유과도예보。집합동력인자예보방법계산량소，용역이식，가이제공강수예보산품，위예보원주폭우예보제공지지。
In this paper, we describe the definition and physical meaning of several important physical variables, such as the generalized po-tential temperature, the moist thermodynamic advection parameter, the thermodynamic helicity, the vertical flux of the thermodynamic diver-gence, 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 evo-lution 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 predict heavy 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 total precipitation, 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 re-flect the common characteristics of the rainfall processes. Statistical verification with a long time series shows that the precipitation forecast score 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 provide assistance to forecasters.