Insights into Convective-scale Predictability in East China: Error Growth Dynamics and Associated Impact on Precipitation of Warm-Season Convective Events

Abstract

This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin (YHRB) of East China. The scale-dependent error growth (ensemble variability) and associated impact on precipitation forecasts (precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale, while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing. Meanwhile, larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale. Consequently, these results can be used to explain under-dispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.

摘要

本文基于对流尺度集合模拟系统性地研究了江淮地区不同天气型控制下的强对流天气可预报性的问题. 将 13 个强对流个例分类为强强迫和弱强迫两类, 并就此展开预报误差增长和降水不确定性的机制分析. 结果表明在强强迫类中预报误差呈现“阶梯状”随空间尺度增加而滞后增长的特征, 而弱强迫类中误差展现出午后峰值, 且这一特征主要由小尺度凸显. 两类天气型下误差增长的不同使得降水不确定性在不同尺度上亦呈现出明显差异, 揭示了对流尺度模拟对两类天气型强对流预报技巧的差异. 此外, 对不同来源和不同尺度误差影响的研究发现, 侧边界误差能够通过影响大尺度部分使得误差振幅准线性增长, 表明大尺度强迫能够控制预报误差和降水不确定性的演变, 尤其对强强迫类. 另一方面, 通过对不同尺度初始误差的影响研究发现小尺度误差升尺度增长和较大尺度误差的降尺度传播等尺度相互作用现象均非常明显. 具体来说, 小尺度初始误差对弱强迫类天气型更为敏感, 而较大尺度的初始误差则能够控制 4 小时后的误差增长过程以及降水不确定性发展. 总结来说, 本文的结论可用于解释对流尺度集合预报中常见的“欠发散”问题, 并为集合初始扰动的设计提供科学反馈.