摘要:本文基于WRF模式研究了2015年5月16～17日广东西南地区的一次暴雨过程的预报误差来源。首先比较了以NCEP_FNL为初始资料的WRF模式的模拟预报（记为WRF_FNL）和ECMWF（European Centre for Medium-Range Weather Forecasts）关于该次暴雨过程的确定性预报。结果表明，ECMWF具有较高的预报技巧，因此，认为ECMWF的模式和初始场都较为准确。进一步，以ECMWF的初值作为初始场，选用相同的物理参数化方案，再次用WRF模式进行预报（预报结果记为WRF_EC）。结果表明相对WRF_FNL，WRF_EC的预报结果有明显改善。这表明，初始场的改进对预报有较大的影响，初始误差是预报误差的重要来源。进一步，分析了初始误差的主要来源区域和来源变量。结果表明，南海北部湾至广西西南区域为本次暴雨预报初始误差的主要来源区域，而初始温度场和初始湿度场则为此次暴雨预报初始误差的主要来源变量。同时改进初始温度场和湿度场可以较大程度提高本次暴雨过程的预报技巧。
关键词:误差分析/
华南暴雨/
初始场/
敏感区/
敏感变量
Abstract:Based on the WRF model, in this study, the authors investigated the source of forecast errors for an extreme precipitation event in the southwest of Guangdong Province that occurred from 16 May to 17 May 2015. First, forecasts using the WRF model with the initials NCEP_FNL (hereinafter WRF_FNL) were compared with the deterministic forecasts generated by ECMWF (European Centre for Medium-Range Weather Forecasts), and the results showed that ECMWF had good forecast skills. Thus, the model and initials used by ECMWF are deemed to be accurate. Then, the authors generated a new forecast (hereinafter WRF_EC) using the WRF model with the ECMWF initials, keeping the same physical schemes as those in the previous WRF simulations. The results showed that WRF_EC has better forecast skills than WRF_FNL. This indicates that the improvement of initial states has a significant impact on forecasts, and thus the initial error is the main source of the forecast errors. The authors also analyzed the sensitive area and sensitive variables. The results showed that the initial errors mainly came from the North Gulf of the South China Sea and southwest of Guangxi Province. Both the initial temperature and the initial humidity played an important role in the heavy precipitation forecast. Thus, improving the accuracy of initial temperature and humidity could greatly improve the skills in forecasting this heavy precipitation phenomenon.
Key words:Error analysis/
Heavy precipitation in South China/
Initial states/
Sensitive areas/
Sensitive variables
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