CMAQ模式及其修正技术在上海市PM_(2.5)预报中的应用检验

利用CMAQ空气质量数值预报模式对上海市PM2.5浓度进行预报,选取10个囯控站点监测数据对预报进行验证评估.结果表明,CMAQ模式开展能够较好地模拟出PM2.5的时间变化趋势及浓度水平,但总体处于低估的水平,偏低幅度约25%,尤其在高污染阶段,模式的低估更为突出,达32%,这与污染源清单的不确定性有关.为提高PM2.5预报准确度,采用学习型线性回归方法对PM2.5浓度的数值预报结果进行修正,统计检验结果显示修正预报准确率由原来的76.4%提高到了79.3%,污染预报成功指数由56.4%提高至72.1%,明显提高了PM2.5浓度的预报效果,反映了引入实际监测数据对空气质量数值预报模式进行修正的研究意义和可行性.

Implementation of a dynamic linear regression method on the CMAQ forecast of PM2.5 in Shanghai

Models-3/CMAQ air quality modeling system was applied to forecast PM_2.5 concentrations in Shanghai. Observation data from ten monitoring sites were chosen to evaluate the model performance. The results indicate that CMAQ model can well simulate the variation of PM_2.5 concentration. However, the simulated PM_2.5 concentration is underestimated by 25%. During the high pollution episode, the underestimation is even up to 32%, which could be caused by the uncertainty of emission inventory. In order to improve the accuracy of PM_2.5 forecast, the dynamic linear regression method is used. The statistical results show that the accuracy after revised forecast has been improved from 76.4% to 79.3%, and the crisis success index has been improved from 56.4% to 72.1%, which reflects the significance of this method.