Application of a convection-permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

As a preliminary attempt to cope with the low predictability of heavy rainfall over southern China in the pre-summer rainy season, an experimental convection-permitting ensemble prediction system (GM-CPEPS) based on the Global/Regional Assimilation and Prediction System (GRAPES) is developed. GM-CPEPS produces 12 h forecasts at 0.03 degrees horizontal resolution based on 16 perturbed members. Perturbations from downscaling, ensemble of data assimilation, time-lagged scheme and topography are combined to generate the initial perturbations. Sea-surface temperature is perturbed and a combination of downscaling and balanced random perturbations is used to perturb the lateral boundary conditions. Stochastically perturbed parametrization tendencies, multi-physics, and perturbed parameters are all implemented. In this study, GM-CPEPS was verified over a 15-day period during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014. It was indicated that GM-CPEPS provided estimates of forecast uncertainty that are comparable to some international peers. Compared with the control forecasts (DET), some deterministic guidance, including the forecast distribution with 90th percentile, probability-matched mean, and linear combination of both (NPM), showed advantages in forecasting moderate and heavy rainfall; and the optimal-member technique was superior in reducing bias. Probabilistic guidance demonstrated the value over DET in detecting potential threats of severe weather, with both the optimal-probability and neighbourhood-probability technique leading to improvements in predicting lighter rainfall. Two cases were used to display the deterministic and probabilistic guidance intuitively and to illustrate corresponding advantages and drawbacks.