A Blending Method for Storm-Scale Ensemble Forecast and Its Application to Beijing Extreme Precipitation Event on July 21, 2012

In order to overcome the under-dispersive problem in the storm-scale ensemble forecast system (SSEFs), a new blending method to generate initial perturbation is designed and tested for the WRF SSEFs. This new scheme is based on the combination of the ETKF (Ensemble Transform Kalman Filter) and the Barnes filter for scale decomposition. This scheme is applied to the simulation of the Beijing extreme precipitation event on July 21, 2012and the neighborhood methods is employed to verify the performance of this new scheme. Results indicate that the blending method can effectively solve the scale mismatch problem in the lateral boundary in storm-scale ensemble prediction system, in which ETKF80 (with wavelength scale of 180 km) and Down (Dynamical downscaling) show the best overall performance. The Dispersion Fractions Skill Score (DFSS) shows that the ETKF has a larger spread in small scales during the period of warm area precipitation while Down produces a larger spread at large scales during period of frontal precipitation. The experiments with initial perturbations generated by the blending method take advantage of both the ETKF and Down. The ETKF180 (with wavelength scale of 180 km) generates the most reasonable ensemble spreads. Results also indicate that in order to get better ensemble spread in SSEFs, not only the lateral scale mismatch but also some other elements (such as the interaction between different scales of initial perturbation) should be considered. The blending method ETKF180 also improves the precipitation probability forecast.