考虑尺度敏感性的长江中下游暖区暴雨初始扰动方案比较分析

COMPARATIVE ANALYSIS OF INITIAL CONDITION PERTURBATION SCHEMES FOR WARM-SECTOR RAINFALL OVER THE MIDDLE-LOWER REACHES OF THE YANGTZE RIVER CONSIDERING SCALE SENSITIVITY

关键词:  尺度敏感性  暖区暴雨  初始扰动  局地增长模繁殖法  混合扰动法

Key words:  scale sensitivity  warm-sector rainstorm  initial condition perturbation  local BGM  blend

摘要:

弱天气尺度强迫背景下的长江中下游暖区暴雨突发性强，高度非线性，难以准确预报，这时考虑不确定因素的集合预报成为重要选项，而对流尺度集合预报核心问题是积分一段时间后离散度偏低，会导致预报失败。比较包含不同尺度扰动信息的对流尺度集合预报方案间的差异性并据此优化初始扰动方案，针对2018年5月4—5日一次典型长江中下游暖区暴雨过程，分别采用动力降尺度（DOWN）、增长模繁殖法（BGM）、局地增长模繁殖法（LBGM）和混合扰动法（BLEND）等四种方法进行集合预报试验，以期探讨对离散度和预报效果的影响。结果表明，在模式积分0~6 h，具有中小尺度扰动信息的BGM和LBGM的离散度优于DOWN，其中LBGM相比于BGM具有一定程度上的改进，说明具有更准确中尺度特征的扰动能够在积分初始阶段获得有效增长，即考虑了中小尺度天气系统局地性的LBGM能弥补BGM的不足；但是，在模式积分12 h以后，具有更多大尺度特征扰动的DOWN优于区域模式中的增长模繁殖法BGM和LBGM，说明经过初始误差快速增长一段时间后，大尺度扰动开始起主要作用。而具有不同尺度扰动信息的BLEND方案则兼具LBGM和DOWN的优势，几乎在整个预报时段离散度较高且概率预报评分较好，体现出混合扰动的优越性。以上结果进一步说明，初始扰动的尺度特征在暖区暴雨的集合预报效果中具有关键性的作用，因而通过调整初始扰动的尺度信息来优化集合预报性能的混合扰动思想，在业务上具有一定的指导意义和推广价值。

Abstract:

Warm-sector rainfall event under the background of weak synoptic-scale forcing is difficult to predict accurately due to its abruptness and nonlinear, thus ensemble forecasts have become one of the crucial options considering uncertain factors. However, the core problem of convection-allowing ensemble forecast is that the spread is low after integration for a period, which will lead to prediction failure. Therefore, this paper compares the differences among the convection-allowing ensemble forecast schemes with different scale information for perturbations, and optimizes the initial perturbation scheme for the warm-sector rainstorm over the middle and lower reaches of the Yangtze River. Four convection-allowing ensemble forecast experiments with different initial perturbation schemes including dynamic downscaling (DOWN), breeding of the growing mode (BGM), Local BGM (LBGM), and BLEND, were carried out for a typical warm-sector rainfall on May 4—5, 2018. The aim is to explore the impact on spread and forecast. The results show that the ensemble forecast results of LBGM and BGM are better than that of DOWN in 0~6 h in the early stage of model integration, and LBGM has a certain degree of improvement compared with BGM, which indicates that accurate small and medium scale perturbations can obtain effective growth at this stage. After 12 hours of integration, the forecast effect of DOWN is better than that of BGM and LBGM instead, which indicates that after the initial error increases rapidly for a period, the large-scale perturbations begin to play a major role. However, BLEND possesses the advantages of both LBGM and DOWN, and has a good forecast effect in almost the whole forecast period, reflecting the superiority of multi-scale blend initial condition perturbations. Since the convection-allowing ensemble forecast for this type of events are sensitive to the scale characteristics of the initial condition perturbations, adjusting the scale of the initial condition perturbation has certain significance in operation for the development of high-quality convection-allowing ensemble forecast system.