基于对流尺度集合模拟的江淮暴雨预报不确定来源分析

Understanding forecast uncertainties of heavy rainfall cases over the Yangtze-Huai river basin based on convective-scale ensemble simulations

  • 摘要: 选取发生在江淮地区暖季的一次系统性锋面暴雨(Frontal rainfall, FR)和一次局地暖区暴雨(Warm-sector rainfall, WR)过程作为研究对象,通过考虑初值(IC)、侧边界条件(LBC)和模式(MO)不确定性,设计了7组对流尺度集合预报试验,用于分析FR和WR的预报不确定来源,并评估不同来源扰动对于两类降水过程不确定性的表征能力。结果表明:联合扰动试验较单一扰动试验能够产生更大的降水离散度,其中引入MO扰动可以修正降水系统偏差,特别是体现在WR中。FR的预报不确定主要来自于天气尺度低空急流和冷、暖气流交汇处,其中低空急流的强度、位置和高度形成的三维结构决定了锋面降水的位置和强度,引入MO扰动能够提升集合预报对于冷、暖空气交汇处的预报不确定表征;相比之下,WR预报不确定主要来源于边界层及山脉背风坡附近的局地风辐合,其中模式物理参数的配置对于边界层热、动力要素较为敏感,合适的MO扰动能够更好地体现局地暖区暴雨的预报不确定,从而提升集合预报表现。

     

    Abstract: A systematic frontal rainfall (FR) event and a localized warm-sector rainfall (WR) event during the warm season in the Yangtze-Huai river basin (YHRB) in East China are selected as research subjects. Seven convective-scale ensemble forecast experiments involving the initial conditions (ICs), lateral boundary conditions (LBCs), and model (MO) perturbations have been conducted evaluate the performance of different ensemble experiments and investigate forecast uncertainties of the FR and WR, respectively. The results indicate that combined perturbation experiments produce greater precipitation dispersion than single perturbation experiments, and the introduction of MO perturbations can effectively modify the deviation of precipitation, especially in the WR scenario. The forecast uncertainties of FR primarily stem from the synoptic low-level jet (LLJ) and the convergence of cold and warm air masses from north and south. The three-dimensional structure of the LLJ such as its intensity, location and height determines the position and strength of the FR. It is important to note that introducing MO perturbations enhances the ability of ensemble simulation to represent the uncertainty in forecasting the convergence location of cold and warm air masses. In contrast, the forecast uncertainties of WR are mainly due to boundary layer dynamics and local wind convergence near the leeward sides of mountains, and the model physics configurations are sensitive to thermal and dynamic fields of boundary layer. Appropriate MO perturbations can more effectively represent the forecast uncertainty associated with localized WR process, and thereby improve the overall performance of ensemble forecasts.

     

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