Zhang Lu, Zhuang Xiaoran, Min Jinzhong, Zhang Zhendong, Yang Xixi, Xu Yuan. 2025. Understanding forecast uncertainties of heavy rainfall cases over the Yangtze-Huai river basin based on convective-scale ensemble simulations. Acta Meteorologica Sinica, 83(2):1-18. DOI: 10.11676/qxxb2025.20240141
Citation: Zhang Lu, Zhuang Xiaoran, Min Jinzhong, Zhang Zhendong, Yang Xixi, Xu Yuan. 2025. Understanding forecast uncertainties of heavy rainfall cases over the Yangtze-Huai river basin based on convective-scale ensemble simulations. Acta Meteorologica Sinica, 83(2):1-18. DOI: 10.11676/qxxb2025.20240141

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

  • 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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