Warm‐sector torrential rainfall (WR) in the South China monsoon region has long been a forecasting challenge because of the limited capability of numerical models in heavy rainfall without strong synoptic forcing. Through convection‐allowing ensemble forecasts, this study explores both the intrinsic and practical predictability of a coastal WR event on 19–20 May 2015 during SCMREX (the Southern China Monsoon Rainfall Experiment). The results show a large variability in forecast performance among different members, indicating the practical limit of predictability. In general, GOOD members tend to have a stronger low‐level southerly wind over the sea (monsoon flow) and a considerable surface cooling over the northern mountains (associated with land/mountain breeze). Further investigation via ensemble‐based sensitivity analysis shows that the occurrence of WR is closely related to the nighttime strengthened (cooling) southerly wind (temperatures) over the sea (mountains), 1–3 hr prior to the convection initiation. In contrast, spatial scaling of the initial perturbations has little impact on the forecast after 3 hr and the meso‐γ‐scale rainfall is fully decorrelated after 12 hr, suggesting an intrinsic predictability limit for lead times as short as 6–12 hr. Sensitivity experiments are conducted with the initial‐condition differences reduced by almost an order of magnitude smaller than typical ensemble perturbations, with the results demonstrating that the rainstorm might be near the point of bifurcation, where predictability is intrinsically limited. The limits of both intrinsic and practical predictability highlight the need for rapidly updated and probabilistic convection‐allowing ensemble forecasts for events of this type.

This study investigated the characteristics of extreme precipitation events associated with mesoscale convective systems (MCSs) in East China (the area east of 96°E) during 2016–17. Over the entire region, 204 events were first identified and classified into synoptic, tropical, MCS, small-scale-storm (SSS), and unclassified types. For 73 MCS-type events, further division and analysis were conducted according to the organizational modes. Results show that MCS-related events occurred most frequently near southern Fujian Province and from April to October with a peak in July. The area of occurrence shifted from the south in spring to the north in summer before going back to the south in autumn. The events occurred most commonly from afternoon to early evening, matured around late afternoon, and ended before dark. Among MCS subcategories, the longest average duration was seen in the multiple-MCS cases. Of the 15 selected multiple-MCS events, 11 were defined as early-maturing type with peak rainfall occurrence before the midpoint of duration while the others were late maturing. Although multiple-MCS events were accompanied by a southwest low-level jet, strong warm-air advection, and convective instability, early-maturing cases had stronger synoptic-scale ascent, moister environments, and smaller surface-based convective available potential energy (SBCAPE) and convection inhibition (SBCIN) at the most extreme rainfall-occurrence point. Compared to the MCS type within all extreme precipitation events over the United States, the percentage was lower in China. However, the events in China exhibit more pronounced seasonal cycle.

The impact of stochastically perturbed parameterizations on short-term tornadic supercell ensemble forecasts (EFs) was evaluated using two tornado cases that occurred in eastern China. The initial condition (IC) perturbations of EFs were generated by a three-dimensional variational data assimilation system with perturbed radar data. The parameterization perturbations of EFs were produced by a stochastic procedure that was applied to diffusion and microphysics parameterizations. This procedure perturbed tendencies from both parameterizations and intercept parameters (INTCPs) of the microphysics parameterizations. In addition to individually perturbing these quantities, a combination of perturbations of diffusion and INTCPs was also examined. A resampling method was proposed to handle perturbations that vary substantially, and a vertical localization was applied to the microphysics tendency perturbations. The results indicated that combining perturbations of diffusion and INTCPs produced the intensity and path forecasts of the low-level vortex (LLV) that better match observations for a weak tornado case; this combination also had a positive impact on the LLV intensity forecast for a much stronger tornado case. This combination outperformed the stochastic procedures that perturbed only diffusion or INTCPs, which indicated that it is better to use both error representations. The vertical localization prevented the temperature tendency perturbations of microphysics from always suppressing storms in negative perturbation (<0.0) areas. The negative INTCP and diffusion perturbations benefited the strong LLV, which is consistent with that of the idealized case. The current stochastic procedure could not address the LLV displacement error that is caused by the IC error.

In order to further investigate the influence of ensemble generation methods on the storm-scale ensemble forecast (SSEF) system, a new ensemble sensitivity analysis-based ensemble transform with 3D rescaling (ET_3DR_ESA) method was developed. The Weather Research and Forecasting (WRF) Model was used to numerically simulate a squall line that occurred in the Jianghuai region in China on 12 July 2014. In this study, initial perturbations were generated via ET_3DR_ESA, and the ensemble forecast performance was compared to that of the dynamical downscaling (Down) method and the ensemble transform with 3D rescaling (ET_3DR) method. Results from a set of experiments indicate that ET_3DR_ESA linked to multi-scale environmental fields generates initial perturbations that can not only capture analysis uncertainties, but also match the actual synoptic conditions. Such perturbations produce faster ensemble spread growth, lower root-mean-square error, and a lower percentage of outliers, especially during the peak period of the squall line. In addition, ET_3DR_ESA can effectively reduce the energy dissipation on different scales through the analysis of the power spectrum. Moreover, the intensity and distribution forecasts of heavy rainfall from the ET_3DR_ESA ensemble forecast system were demonstrated to better match the observation. Furthermore, according to results of the relative operating characteristic (ROC) test, Brier score (BS), and equitable threat score (ETS), ET_3DR_ESA significantly improved the forecast skills for heavy rain (15–30 mm/12 h) and extreme rain (>30 mm/12 h), which are critical to the realization of accurate storm-scale system precipitation forecasts. In general, these results suggest that ET_3DR_ESA can be effectively applied to SSEF systems.

Error growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 14 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the NW, NE, SE and SW quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by 4 representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has strong link to large scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within own scale plays different roles in different regions/cases.When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall least organized and has the weakest diurnal variations. Its associated errors at the γ– and β-scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have short response time to convection while increasingly larger scale errors have longer response times and delayed phase within the diurnal cycle.

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This paper analyzes the weather background and triggering mechanism before the occurrence of an EF3 tornado on 14 May 2021, in southern Jiangsu, using multi-source data. Results show that 1) the tornado occurred in the warm and humid area inside the low-level shear line and the inverted trough at the surface, the southeast wind field to the east of the surface convergence line and high value area of water vapor flux on the left side of low altitude jet axis exit zone. 2) The intensity of the heavy rainfall supercell storm was strengthened after entering the Taihu Lake, and mesocyclones were detected with decreasing heights and increasing shear strengths which leading to the formation of the tornado parent storm. Tornado vortex storm bottom height dropped to 0.3km, the shear value increased to 169.1 × 10−3 s-1, strong echoes above 60 dBZ reached to the ground, the difference between the lowest elevation Radial velocity (LLDV) and the maximum Radial velocity (MXDV) reached up to the largest value when tornado occurred. 3) The strong convergence of the ground and the enhancement of mesoscale frontal zone resulted in the strong development of thunderstorm cells. The deep rear inflow behind the heavy precipitation supercell storm passed through the stratiform cloud area and produced strong updraft due to the convergence of wind direction and wind speed. The updrafts generated vertical vorticity under the influence of wind shear, which was further stretched in the middle layer of the troposphere to form the core of the tornado. 4) The flat underlying surface, abundant water vapor, lake surface thermal boundary, and strong wind convergence on the east bank of Taihu Lake provided favorable conditions for the occurrence of the tornado. The centers of the mesocyclones in the lower and middle layers were close to each other in the horizontal direction, which provided stable and vertical updrafts for the mesocyclones and strengthened the suction effect of the mesocyclones on the low-level vortices, facilitating the formation of the tornado.

The application of lateral boundary perturbations (LBPs) helps to restore dispersion in convection-allowing ensemble forecasts (CAEFs). However, the applicability of LBPs remains unclear because of the differences between convection systems. Short-range (24 h) ensemble forecasts are carried out to explore this issue with a strong-forcing (SF) case and a weak-forcing (WF) case in East China. The dependence of LBPs on the forcing types of severe convection is investigated regarding the forecast error growth caused by the lateral boundary conditions (LBCs). The results show that the LBPs mainly influence the SF case rather than the WF case, especially after a 12-h forecast. The large-scale errors dominate in the SF case because the change in the synoptic-scale system affects the forecast error evolution. In contrast, the large-scale errors are mainly derived from the upscaling of the small-scale errors in the WF case, indicating that using LBPs is only insufficient in such a case. In sensitivity experiments that vary the magnitude of LBPs from 10% to 150% of its original value, CAEFs demonstrate more sensitive to LBPs in the SF case than in the WF case, indicating that the WF case has intrinsically limited predictability. Overall, LBPs are more suitable for the SF case, while additional perturbations from other sources are required for CAEFs in the WF case because of the limits of intrinsic predictability.