The Impact of Stochastically Perturbed Parameterizations on Tornadic Supercell Cases in East China

Abstract

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.

Impact of Stochastically Perturbed Terminal Velocities on Convective-Scale Ensemble Forecasts of Precipitation

“…Terminal velocities were separately perturbed because their error characteristics may not be identical, which will be examined later. e perturbation resampling procedure proposed by Wang et al [28] was adopted in this work. is procedure was designed to transform the Gaussian perturbations (0, 1) to an asymmetric distribution in which half of the samples have values greater than 1.0 and the other half have values less than 1.0.…”

“…e number of vertical levels was 43 with the highest resolution of 20 m near the surface and an average resolution of 500 m. e model top is at approximately 20 km above ground level (AGL). Twenty initial ensemble members at 00 UTC were generated by the approach used by Wang et al [28]. In this approach, the three-dimensional variational (3DVar) data assimilation (DA) system [59] was run in parallel using the Global Forecasting System (GFS) analysis data at a horizontal resolution of 0.5°and the perturbed radar data from KBMX, KHTX, KDDC, and KUEX, the locations of which are marked by radar icons in Figure 2.…”

“…In this approach, the three-dimensional variational (3DVar) data assimilation (DA) system [59] was run in parallel using the Global Forecasting System (GFS) analysis data at a horizontal resolution of 0.5°and the perturbed radar data from KBMX, KHTX, KDDC, and KUEX, the locations of which are marked by radar icons in Figure 2. All settings, including both the 3DVar and smooth random perturbation steps for generating the initial members, were identical to those of Wang et al [28], whose model configurations were also used in this work except for those involved in the stochastic perturbation.…”

“…erefore, it is not always valid to attribute the same error characteristics to all parameterizations [17]. Even in one parameterization, perturbing tendencies of different variables in the same way may not improve ensemble forecast skills [28]. Considering the different error characteristics, Sanchez et al [26] assigned different error standard deviations for parameterizations and obtained a larger spread in the tropics.…”

“…To date, however, terminal velocity perturbations have not been involved in stochastic approaches. Previous studies have perturbed the intercept parameter [22,25,28], the rain evaporation rate and accretion [22,23], and the tendencies of temperature and water vapor [4,5,15,16,21,25,28]. Although the intercept parameter perturbation synchronously adjusts the terminal velocity, the latter is not fully determined by the former.…”