为更好地改进提高模式预报性能，评估了新一代WRF-CMAQ（Weather Research and Forecasting model-Community Multi-scale Air Quality model）模式系统的不同网格分辨率预报产品对2018年北京市城六区空气质量预报结果的影响。分析表明：（1）基于首要污染物为PM2.5的预报数据集，模式系统1 km网格分辨率（BJ01）和3 km网格分辨率（BJ03）等级准确率优于官方预报结果，模式系统BJ01和BJ03区域4天内预报等级准确率均达到50%以上，24 h内准确率达60%以上，官方预报24 h内等级准确率为59%。本文引入预报综合评分法，基于IAQI（Individual Air Quality Index）和等级级别正确性双因素的预报综合评分结果显示，模式系统BJ03得分75.0分最高，BJ01次之，优于官方预报结果，模式9 km网格分辨率（BJ09）得分69.1分最低。（2）基于模式系统2018年长时间序列预报结果分析表明：模式系统预报的PM2.5浓度与实测的变化趋势较为一致，其中模式系统BJ03结果与实测PM2.5浓度相关系数达0.76，覆盖区域较大的BJ03和BJ09对PM2.5浓度峰值模拟较好。中重度污染过程的PM2.5浓度峰值模式预测误差表明，不同分辨率模式预报峰值误差的变化趋势基本一致，覆盖区域更大的粗分辨率模式预报结果高于覆盖区域小的细分辨率模式预报结果。与预报综合评分结果一致，统计分析结果也表明BJ03区域预报效果最好，平均偏差为0.83 μg/m3；而BJ01区域预报整体偏低，BJ09区域预报整体偏高。（3）基于不同网格分辨率预报效果的空间差异性分析表明：同一站点在不同分辨率上表现不一致，BJ01区域中农展馆站表现最好，BJ03区域中万柳站表现最好，BJ09区域中东四站表现最好。
In order to improve model performance, the impact of the new generation WRF-CMAQ (Weather Research and Forecasting model-Community Multi-scale Air Quality model) air quality model system performance of different resolutions for the main district of Beijing was evaluated in 2018. Based on the data set, with PM2.5 as the primary pollutant, forecast grade accuracy of BJ01 (resolution of 1 km) and BJ03 (resolution of 3 km) domains were found to be better compared to that of the official forecast. More than 50% accuracy rate was achieved with BJ01 and BJ03 domains. Compared with the accuracy rate on the first day of official forecast (59%), accuracy rate using the proposed system reached over 60%. A comprehensive scoring method based on the IAQI (Individual Air Quality Index) accuracy and the grade accuracy is adopted. Results show that BJ03 domain achieved the highest score (75.0 points) followed by BJ01 domain. The official forecast scored 70.6 points while BJ09 (resolution of 9 km) domain achieved the lowest score of 69.1 points. Based on the analysis of the prediction results of 2018 long time series of the model system, the model’s predicted PM2.5 concentration is observed to be consistent with that of the observation trend. In addition, the analysis reveals that the correlation coefficient between the model result of BJ03 domain and that of the observation is 0.76. Good peak value simulation performances are achieved in BJ03 and BJ09 domains where there are large area coverages. Similar error trends in peak value simulation of the three model domains are observed. It is evident that the results from the model with coarse resolution are higher than that of the model with fine resolution, which covers a smaller area. Consistent with the forecast comprehensive score, the statistical analysis results reveal that BJ03 domain prediction has the best performance with an average deviation of 0.83 μg/m3. Compared with the observation forecast, BJ09 domain forecast is generally higher whereas BJ01 domain forecast is observed to be lower. Spatial difference analysis of different resolutions from the same site yields inconsistent results. This study shows that best performance is achieved by BJ01, BJ03, and BJ09 areas for the Nongzhanguan, Wanliu, and Dongsi stations, respectively.
徐旗,吴其重,李冬青,王晓彦,王辉,王蓉蓉,肖晗,陈焕盛.2020.北京市城六区2018年空气质量数值预报效果评估[J].气候与环境研究,25(6):616-624. XU Qi, WU Qizhong, LI Dongqing, WANG Xiaoyan, WANG Hui, WANG Rongrong, XIAO Han, CHEN Huansheng.2020. Assessment of the Air Quality Numerical Forecast in the Main District of Beijing (2018)[J]. Climatic and Environmental Research (in Chinese],25(6):616-624.复制