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Surface wind speed forecast from an operational WRF Ensemble Prediction System (WEPS) was verified, and the system-bias representations of the WEPS were investigated. Results indicated that error characteristics of the ensemble 10-m wind speed forecast were diurnally variated and clustered with the usage of the planetary boundary layer (PBL) scheme. To correct the error characteristics of the ensemble wind speed forecast, three system-bias representations with decaying average algorithms were studied. One of the three system-bias representations is represented by the forecast error of the ensemble mean (BC01), and others are assembled from each PBC group (BC03) as well as an independent member (BC20). System bias was calculated daily and updated within a 5-month duration, and the verification was conducted in the last month, including 316 gauges around Taiwan. Results show that the mean of the calibrated ensemble (BC03) was significantly improved as the calibrated ensemble (BC20), but both demonstrated insufficient ensemble spread. However, the calibrated ensemble, BC01, with the best dispersion relation could be extracted as a more valuable deterministic forecast via the probability matched mean method (PMM).

A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses. [ABSTRACT FROM AUTHOR]