Abstract:
Due to the residual circulation of Typhoon Doksuri (2305), heavy rainstorm events occurred in North China during July 29 to August 1, 2023. This study evaluated the performance of three operational models at varying forecast lead time, including the Integrated Forecast System of European Centre for Medium-Range Weather Forecasts (EC-IFS), the Global Forecast System of National Centers for Environmental Prediction (NCEP-GFS) and the China Meteorological Administration Global Forecast System (CMA-GFS). The possible causes of the forecast biases are also analyzed. Results show that: (1) The forecast capability of the three models decreases with the increasing forecast lead time. Among all, EC-IFS outperforms the other two models with averaged spatial correlation coefficient (SCC) over 0.5 at lead time 24-120 h, SCC of NCEP-GFS approximates EC-IFS when leads 24-48 h but significantly decreases and fluctuates after 72 h, whereas that decreases to negative values in CMA-GFS. (2) In terms of rainfall location, EC-IFS well reproduced the distribution pattern of the heavy rain, while the forecasted rainfall in NCEP-GFS locates to the south of the heavy rain due to the eastward location of the forecasted subtropical high and weakened south flow; CMA-GFS predicts rainfall noticeably eastward, which is mainly caused by shifted subtropical high and continental high, allowing the residual vortex to move northward. As for intensity, all three models commonly underestimated the extreme value of heavy precipitation. (3) Vertical structures of diabatic heating indicate that the difference of model performance is mainly due to the uncertainties of parameterization for model physical process. Almost all models are unable to accurately simulate the precipitation-relative humidity tilting structure as in observation, implying that models still have deficiencies in parameterizing sub-grid physical processes such as convection and clouds, which are the main causes of model forecast bias.