雷达反射率三维拼图观测资料在北方区域数值模式预报系统中的同化应用研究

A study of three-dimensional radar reflectivity mosaic assimilation in the regional forecasting model for North China

以业务应用为目标,开展雷达反射率三维拼图观测资料在北方区域数值预报系统中的同化应用研究。采用雷达反射率间接同化方法同化北方雷达反射率拼图观测资料,重点关注其对降水、湿度、温度及风的预报能力影响。首先,基于2017年8月雷达拼图观测资料批量同化和对比试验,对雷达拼图资料同化应用效果进行定量评估,结果表明雷达拼图资料同化虽然加大了地面风场预报误差,但在降水预报和湿度、温度预报等方面有明显的改善作用。其次,选择在业务中预报难度较大的强降水个例开展分析研究,分析表明:(1)同化雷达拼图观测资料有效提高了模式降水预报性能,临近降水发生的循环起报时次预报效果更好;(2)对于短时间多次强降水过程发生的预报,循环同化雷达拼图资料可及时弥补模式中由于前次降水导致的水汽、能量等消耗及热/动力条件削弱,持续支持降水系统发展。最后,通过考察雷达反射率的不同同化方案,发现同化反演水凝物或者估计水汽均能改善模式降水预报性能,但是同化估计水汽对降水预报性能的改善更为明显,联合使用两方案能同时对水凝物分布、热力场等进行调整,可提高模式降水预报性能。

The three-dimensional(3 D) radar reflectivity mosaic covering North China is assimilated into the regional numerical weather forecasting system RMAPS-ST via an indirect radar reflectivity assimilation method of WRF-3 DVAR to improve the model forecasting skill with focuses on the influences on precipitation, specific humidity, temperature and wind forecasting. Firstly, experiments with/without radar reflectivity mosaic assimilation have been performed from 1 to 31 August 2017, and quantitative verification is conducted based on the batch experiments. The results show that the radar reflectivity mosaic assimilation significantly improves the skill for precipitation, specific humidity and temperature forecasting but increases the forecasting error in the wind field. Secondly, how the radar reflectivity mosaic assimilation improves the forecasting skill of RMAPS-ST is displayed based on a heavy rainfall case, which shows:(1) Precipitation forecasting skill is greatly improved and the cycle that is closer to the beginning of rainfall has higher forecasting skill by assimilating radar mosaic.(2) Water vapor, energy and thermal condition which are consumed and weakened by the previous rainfall can be boosted and re-organized to trigger new rainfall by assimilating radar mosaic reflectivity in the cycling way, which plays an important role in the situation of multiple rainfalls occurring during a short time period. Finally, two schemes of the WRF-3 DVAR indirect radar reflectivity assimilation method are tested. Results indicate that the retrieved hydrometeor assimilation scheme and the derived water vapor assimilation scheme both can improve precipitation forecasting, but the latter one plays a more important role and using two schemes together can make reasonable adjustments for the rainwater, snow water, water vapor and thermal condition, which are critical for the improvement of precipitation forecasting.