Abstract
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.
Highlights
Ensemble forecasting, which is an effective method to estimate forecast uncertainties, can achieve higher numerical forecast skill than deterministic forecasts [1]
In order to investigate the error growth modes of ET_EDR_ESA-generated initial perturbations and the resulting impact on ensemble forecasts, we performed a set of experiments on a squall line that occurred in the Jianghuai region of China by using the Weather Research and Forecasting (WRF) model to compare the results of Down, ensemble transform with 3D rescaling (ET_3DR), and ET_3DR_ESA
In order to develop ensemble generation methods for scale ensemble forecast (SSEF) systems, three ensemble forecast experiments for a squall line that occurred in the Jianghuai region of China were carried out as experiments for a squall line that occurred in the Jianghuai region of China were carried out as based based on the Down, ET_3DR, and ET_3DR_ESA methods
Summary
Ensemble forecasting, which is an effective method to estimate forecast uncertainties, can achieve higher numerical forecast skill than deterministic forecasts [1]. Ensemble generation methods seek to create a set of initial perturbations that can represent analysis errors in a numerical weather prediction system in order to improve its probabilistic forecast performance [8]. Wile et al [24] developed an ESA-based technique to directly control ensemble initial perturbations Such perturbations can effectively match the synoptic conditions and link to the environmental field, thereby verifying the ability of ESA to identify sensitive factors. In order to investigate the error growth modes of ET_EDR_ESA-generated initial perturbations and the resulting impact on ensemble forecasts, we performed a set of experiments on a squall line that occurred in the Jianghuai region of China by using the Weather Research and Forecasting (WRF) model to compare the results of Down, ET_3DR, and ET_3DR_ESA
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