WRF模式最优参数化方案在不同空气质量模式的应用

精准的气象场和适用的空气质量模式是优化大气污染模拟的重要途径.为提升四川盆地冬季大气污染模拟效果，利用WRF模式25组参数化方案组合，进行气象场模拟试验，基于最优方案数据，以四川盆地某大型钢铁厂为例，分别驱动AERMOD模式和CALPUFF模式，通过研究区域4个国控站数据对模拟结果进行对比验证.结果表明，WRF模式参数化方案选取对地面风场、高空风场和地面湿度场模拟效果影响较大，对地面温度场、高空温度场和高空湿度场模拟效果影响较小，SLAB陆面过程方案、 Dudhia短波辐射方案分别与YSU、 ACM2、 BouLac和MRF边界层方案的组合，均能较好地模拟四川盆地冬季地面风场、温度场和湿度场的变化趋势，结合高空风温湿统计参数综合分析可知，第1组方案适用于达州气象场模拟，第13组和第17组方案分别适用于成都白天和夜间时段气象场模拟.CALPUFF模拟结果与监测值的相关性整体优于AERMOD,从站点角度分析，CALPUFF在国控站3号的模拟效果相较AERMOD提升较大，在国控站2号的模拟效果提升较小，从大气污染物角度分析，4个国控站CALPUFF对NO_x和PM

Application of WRF Optimal Parameterization Scheme for Different Air Quality Models

Accurate meteorological fields and applicable air quality models are important ways to optimize air pollution simulations. To improve the accuracy of winter air pollution models in the Sichuan basin, we conducted a meteorological field simulation using 25 sets of parameterized scheme combinations in the Weather Research and Forecasting (WRF) Model. Based on the optimal parameters, the air pollution levels were simulated using AERMOD and CALPUFF models in a local large steel plant, and the data were verified by comparing the data from four National Ambient Air Monitoring Stations (NAAMS). The results indicated that the WRF model parameters had substantial effects on the simulation of the ground wind field, high-altitude wind field, and ground humidity field. In contrast, the parameters had no significant effect on the simulation of the ground temperature field, high-altitude temperature field, and high-altitude humidity field. The combination of the SLAB land surface process scheme and Dudhia shortwave radiation scheme with four boundary layer schemes, namely YSU, ACM2, BouLac, and MRF, could well-simulate the trends of winter surface wind, temperature, and humidity fields in Sichuan basin. The simulation results were analyzed by combining the statistical parameters of high-altitude wind, temperature, and humidity. The group 1 parameter scheme was applicable to simulate the meteorological field of Dazhou. Group 13 and Group 17 parameters were applicable to simulate the meteorological fields in Chengdu during the day and night, respectively. The correlation between CALPUFF simulation and monitoring value was better than that for AERMOD. CALPUFF was more accurate than AERMOD when referring to the monitoring data from NAAMS No.3. In addition, the simulation quality of CALPUFF was slightly better than that of AERMOD with reference to data from NAAMS No.2. Using air pollutant monitoring data from NAAMS as a reference, the simulated results of CALPUFF on NO_x and PM₁₀ were improved compared to AERMOD at all four stations. Data from the Q-Q diagram indicated that the simulated results of CALPUFF on SO₂, NO_x, and PM₁₀ were closer to the monitored values compared to those of AERMOD.