Evaluation of the Added Value of Probabilistic Nowcasting Ensemble Forecasts on Regional Ensemble Forecasts

Ensemble forecasting systems have become an important tool for estimating the uncertainties in initial conditions and model formulations and they are receiving increased attention from various applications. The Regional Ensemble Prediction System (REPS), which has operated at the Beijing Meteorological Service (BMS) since 2017, allows for probabilistic forecasts. However, it still suffers from systematic deficiencies during the first couple of forecast hours. This paper presents an integrated probabilistic nowcasting ensemble prediction system (NEPS) that is constructed by applying a mixed dynamic-integrated method. It essentially combines the uncertainty information (i.e., ensemble variance) provided by the REPS with the nowcasting method provided by the rapid-refresh deterministic nowcasting prediction system (NPS) that has operated at the Beijing Meteorological Service (BMS) since 2019. The NEPS provides hourly updated analyses and probabilistic forecasts in the nowcasting and short range (0-6 h) with a spatial grid spacing of 500 m. It covers the three meteorological parameters: temperature, wind, and precipitation. The outcome of an evaluation experiment over the deterministic and probabilistic forecasts indicates that the NEPS outperforms the REPS and NPS in terms of surface weather variables. Analysis of two cases demonstrates the superior reliability of the NEPS and suggests that the NEPS gives more details about the spatial intensity and distribution of the meteorological parameters.