Understanding the Predictability within Convection-Allowing Ensemble Forecasts in East China: Meteorological Sensitivity, Forecast Error Growth and Associated Precipitation Uncertainties Across Spatial Scales

Abstract

This study investigates the practical predictability of two simulated mesoscale convective systems (MCS1 and MCS2) within a state-of-the-art convection-allowing ensemble forecast system. The two MCSs are both controlled by the synoptic Meiyu-front but differ in mesoscale orographic forcing. An observation system simulation experiment (OSSE) setup is first built, which includes flow-dependent multiple-scale initial and lateral boundary perturbations and a 12 h 30-member ensemble forecast is thereby created. In combination with the difference total energy, the decorrelation scale and the ensemble sensitivity analysis, both forecast error evolution, precipitation uncertainties and meteorological sensitivity that describe the practical predictability are assessed. The results show large variabilities of precipitation forecasts among ensemble members, indicative of the practical predictability limit. The study of forecast error evolution shows that the error energy in the MCS1 region in which the convection is blocked by the Dabie Mountains exhibits a simultaneous peak pattern for all spatial scales at around 6 h due to strong moist convection. On the other hand, when large-scale flow plays a more important role, the forecast error energy in the MCS2 region exhibits a stepwise increase with increasing spatial scale. As a result of error energy growth, the precipitation uncertainties evolve from small scales and gradually transfer to larger scales, implying a strong relationship between error growth and precipitation across spatial scales, thus explaining the great precipitation variability within ensemble members. These results suggest the additional forcing brought by the Dabie Mountains could regulate the predictability of Meiyu-frontal convection, which calls for a targeted perturbation design in convection-allowing ensemble forecast systems with respect to different forcing mechanisms.

Growth and Interactions of Multi-Source Perturbations in Convection-Allowing Ensemble Forecasts

“…Due to the unique climatic background and the complex underlying surface of the Yangtze-Huai River Basin (YHRB) in East China, extreme rainfall and associated flash floods occur frequently during the Meiyu season, which poses a great risk to lives and property (Ding [1] ; Sun and Zhang [2] ; Luo et al [3,4] ; Luo and Chen [5] ; Min and Fang [6] ; Shen et al [7] ). There are various forcing backgrounds in the YHRB, such as the synoptic-scale forcing brought by the Meiyu front and the southwest low-level jet and the complex multi-scale topography in the warm zone south of the front (Zhao et al [8] ; Zhang and Zhang [9] ; Fu et al [10] ; Fu et al [11] ; Wang et al [12] ; Zheng et al [13] ; Chen et al [14] ; Zhang et al [15] ; Zhuang et al [16] ). Under such diverse forcing backgrounds, the numerical weather prediction of these events is challenging because simply increasing the model resolution cannot improve the forecast accuracy (Mass et al [17] ; Walser and Schär [18] ; Lean et al [19] ; Johnson et al [20] ; Judt et al [21] ; Shen et al [22] ).…”

“…Yang et al [54] found that the IC and LBC greatly influence the location of SF rainfall, while the MO has a great impact on convection triggering of WF rainfall. Since the impact of the synoptic-scale forcing on the perturbation growth varies with the geographic regions, it is worth investigating the perturbation growth for the YHRB, where the forcing background is complex (Sun and Zhang [2] ; Luo et al [3] ; Zhao et al [8] ; Zhang and Zhang [9] ; Fu et al [10] ; Zhang et al [15] ; Zhuang et al [16] ; Shen et al [55] ).…”

Impact of Optimally Growing Initial Errors on the Mesoscale Predictability of Heavy Precipitation Events along the Mei-Yu Front in China

“…Thus, it is essential to study the predictability of this type of event. However, compared with the extensive level of mechanism research, studies on the predictability are lacking and those that have been conducted suggest a strong sensitivity to the errors in the initial conditions (Bei and Zhang 2007;Luo and Chen 2015;Zhuang et al 2020).…”