不同大尺度强迫条件下考虑初始场与侧边界条件不确定性的对流尺度集合预报试验

研究了不同大尺度强迫条件下的暴雨个例中,考虑不同尺度特征的初始扰动与侧边界扰动相互作用构造对流尺度集合预报的可行性,为进一步构建“自适应”于不同强对流天气的对流尺度集合预报系统提供依据。结果表明,在大尺度强迫显著的个例1中,以大尺度扰动信息为主的动力降尺度的增长趋势较集合转换卡尔曼滤波(ETKF)更为显著,且总扰动能量在预报中后期超过集合转换卡尔曼滤波,而在大尺度强迫较弱的个例2中,集合转换卡尔曼滤波扰动能量始终高于动力降尺度。此外,当大尺度强迫显著时,初始扰动与侧边界扰动相匹配会产生相互促进的作用,而不匹配时初始扰动会在预报中后期抑制侧边界扰动的发展,当大尺度强迫较弱时,即使是互相间不匹配的初始扰动与侧边界扰动也能在大部分预报时段起到相互促进的作用,说明初始扰动与侧边界扰动的相互作用机制不仅与天气形势相关,也与二者是否匹配挂钩,另外,扰动的发展特征同样依赖于天气形势;从集合离散度的角度来看,当大尺度强迫明显时,侧边界扰动的作用会在更短的时间内取代初始扰动,从而对离散度起到主导地位;两种初始扰动方法相比,集合转换卡尔曼滤波在多数情况下对总离散度的贡献均大于动力降尺度;从降水量预报及概率预报情况来看,大尺度强迫明显的个例可预报性更高,且各集合成员间的差异较小,大尺度强迫较弱的个例则相反,且当两种初始扰动方案与侧边界扰动相结合时,较仅侧边界扰动均有一定提高。 

Convective-scale ensemble prediction experiments under different large-scale forcing with consideration of uncertainties in initial and lateral boundary condition

As the first effort to develop a self-adaptive convective scale ensemble prediction system, this study examines two different initial perturbation methods for the simulation of two different types of heavy rain case and their impacts on precipitation prediction under the same lateral boundary perturbations. A suite of ensemble experiments has been conducted: In the first two experiments, both initial perturbation (ICs, include ETKF and DOWN) and lateral boundary perturbation (LBCs) are considered, while in others experiments only ICs or LBCs are considered. Results show that under strong large scale forcing and when both ICs and LBCs are considered, the ETKF method provides more net perturbation energy in the early hours while the DOWN method provides more energy in the later hours of the integration. However, when the large scale forcing is weak, the ETKF method results in more perturbation energy than the DOWN method during the entire forecast period, indicating the importance of consistency between initial and lateral perturbations. Note that initial perturbation is always dominant at the first several hours of integration and LBCs is dominant in the following hours. The effect of initial perturbations is more distinct under strong large scale forcing than under weak large scale forcing. Meanwhile, the synoptic-scale forcing has significant impacts on quantitative precipitation forecast (QPF) and probability forecast skill.