基于奇异矢量的优化短期集合预报

The optimized short range ensemble forecast based on singular vector.

中文关键词:  集合预报，集合离差，奇异矢量

英文关键词:Ensemble forecast,Ensemble spread,Singular vector

中文摘要:

      在1—2 d的短期预报中，由奇异矢量构建的初始扰动主要是线性发展，为了防止在积分终止时刻，由同一奇异矢量导出的正负初始扰动的积分在集合平均时互相抵消，文中首先通过理论推导和实际计算证明了对集合成员进行优化的必要性，以及从不同奇异矢量导出的集合成员中，表现好于控制预报的一组成员相对于控制预报的离差恒大于或恒小于表现劣于控制预报的另一组成员，利用这个特征，在做集合预报时，把奇异矢量导出的正负两组预报分成离差相对大一组、离差相对小一组，就可以避免求集合平均时成员相互抵消，从而提出了一种优化基于奇异矢量的短期集合预报的方法。文中使用NCAR/PSU（美国国家大气研究中心/宾夕法尼亚大学）中尺度有限区域模式MM5第1版，及其对应切线性、伴随模式，对1999年夏季发生的两个梅雨锋低涡个例作了分析，在计算奇异矢量时采用了干能量模，分析结果表明：相对于正负两个初始扰动都入选的集合，严格按照这种方法挑选出来的优化集合可以有效地提高集合平均的精确度。在生成初始扰动的方法上，文中的计算表明，相对于用单个奇异矢量生成初始扰动，把正交的多个奇异矢量累加起来导出的初始扰动具有更大的增长率，能有效地增大集合成员间的离差，提高集合成员的预报精度。

英文摘要:

      In ensemble forecast, by summing up ensemble members, filtering the uncertainty, and remaining the common component, the ensemble mean with better results can be obtained. However, filtering works only when the perturbation develops nonlinearly, if perturbations propagate in a linear space approximately, the positive and negative couple members will counteract each other, lead to little difference between ensemble mean and control forecast, thus making the ensemble insignificant finally. In 1-2 days ensemble forecast based on singular vector calculation, to avoid this mutual cancellation, the couple members originated from the same singular vector should not be all both into the ensemble system, the only candidate should be the one with better forecast. Based on the ingredient analysis of initial perturbation development, one method for selecting ensemble member is presented, which can satisfy the above requirement. The regional model MM5V1 of NCAR/PSU (the National Center of Atmosphere Research/the University of Pennsylvania) and its corresponding tangent and adjoint models are used. The ensemble spread and forecast errors are calculated with dry energy norm. Two mesoscale vortex cases on the meiyu front in the Yangtze River basin are used. The initial perturbation putting onto the control forecast develops with nonlinear mode integral, the practical calculating indicates that the perturbation is mainly linear, and most of those linear components could be interpreted by the tangent model. This ingredient analysis shows the necessariness for choosing the optimized ensemble member in short forecast. According to the theory analysis of perturbation ingredient, among a couple of members from different singular vectors, those members performing better always have smaller or greater spreads compared with other members. Following this principle, one optimized ensemble and one inferior ensemble are formed. Results of practical calculation show that the ensemble mean of optimized ensemble is more accurate than that of inferior ensemble, and also performs better than the traditional ensemble with positive and negative member simultaneously. As for generating the initial perturbations, the initial perturbations derived from multiple orthogonal singular vectors grow much quicker than those from the single singular vector, the former can effectively enlarge the range of spread of ensemble numbers, and thus raise the forecast accuracies of ensemble members.