云下湿清除作用对长三角PM2.5模拟的影响

为探讨云下湿清除作用对长三角地区PM_2.5浓度模拟的影响，在华东区域大气环境数值预报系统中新增了气溶胶云下湿清除过程参数化方案，对比分析了2018年12月1日至2019年2月28日降水对长三角地区PM_2.5的清除作用及其对数值模拟效果的改进.结果表明，模式对该时段内的降水和PM_2.5浓度均有很好的模拟效果，模拟的数值、空间分布和变化趋势均与观测有良好的一致性；增加云下湿清除作用后长三角地区PM_2.5浓度模拟效果得到明显改善：平均偏差、平均误差和均方根误差分别减小4.85，1.72，2.35μg/m³，归一化偏差和归一化误差分别降低0.14和0.08，约90%城市的相关系数、均方根误差、平均误差、归一化误差得到改进；云下湿清除作用使模拟PM_2.5浓度随雨强增大而降低，解决了模式湿清除过程的不足.同时，增加云下湿清除作用会加剧部分城市PM_2.5模拟浓度偏低的现象，这需要通过其他过程的完善予以改进.

Simulated impact of blow-cloud wet scavenging on PM2.5 over the YRD

A parameterization scheme for blow-cloud wet scavenging (BWS) was developed and implemented into the Regional Atmospheric Environmental Modeling System for eastern China (RAEMS) to analyze the effect of BWS on the simulation accuracy of PM_2.5 over the Yangtze River Delta (YRD) region. Simulations with RAEMS were conducted for the period of Dec. 1^st, 2018 through Feb. 28^th, 2019. The results showed that RAEMS succeeded in simulating the amount of precipitation and PM_2.5 concentration over the YRD region, as well as their spatial pattern and temporal trend. A better performance of PM_2.5 simulation was found with the BWS effect included. The mean bias (MB), mean error (ME) and root mean square error (RMSE) decreased by 4.85, 1.72 and 2.35μg/m³, respectively. The normalized mean bias (NMB) and normalized mean error (NME) reduced by 14% and 8%, respectively. The model performance is improved at about 90% cities. The introduction of BWS effect to RAEMS reduced the modeled PM_2.5 concentration with the increase of precipitation intensity, which improved RAEMS in terms of wet scavenging processes. The disadvantage was found that the BWS effect exacerbates underestimation of PM_2.5 concentration at some YRD cities and it may be related to model bias of other processes.