两种空气质量数值模式的应用评估与集合改进研究  

APPLICATION EVALUATION AND ENSEMBLE IMPROVEMENT OF TWO AIR-QUALITY NUMERICAL MODELS

作　　者：孙弦 孙磊 聂会文 梁秀姬 苏烨康 王静 夏冬 SUN Xian;SUN Lei;NIE Huiwen;LIANG Xiuji;SU Yekang;WANG Jing;XIA Dong(Zhuhai Public Meteorological Service Center,Zhuhai,Guangdong 519000,China)

机构地区：[1]珠海市公共气象服务中心,广东珠海519000

出　　处：《热带气象学报》2023年第3期361-373,共13页Journal of Tropical Meteorology

基　　金：广东省气象局科学技术研究项目(GRMC2021M19);珠海市气象局科学技术研究项目(ZH202108)共同资助。

摘　　要：利用2018—2019年国控站观测,评估CAMx和CMAQ模式对广东珠海主要污染物时空分布与演变特征的预报能力,并引入多元线性回归和随机森林方法对预报结果进行集成,探究不同集合方法的改进能力。结果表明:CMAQ在各污染物浓度季节-日变化方面明显优于CAMx,但两者存在明显系统偏差,并对多数污染物(除O_(3)之外)的昼夜和空间变化的模拟能力仍存在明显缺陷。例如,CMAQ合理地还原了CO、PM_(2.5)、PM_(10)、SO_(2)、O_(3)和NO_(2)的季节变化,相关系数介于0.72~0.84,但NMB分别达到-0.58、-0.18、-0.30、1.52,-0.16和-0.20,RMSE分别达到0.40 mg/m^(3)、6.86、16.02、10.71、25.05和10.21μg/m^(3)。同时,基于不同污染物构建的两种集合方法均有效移除了系统偏差,加强了CMAQ的模拟优势,并且随机森林方法明显优于多元线性回归,但两者均对模式缺陷无明显改进。进一步分析发现,CMAQ与CAMx模型的重要性基本相当,表明集合方法的预报能力与集合成员的线性偏差无关,主要取决于不同成员的代表性。最后,本研究揭示以随机森林为代表的集合方法虽有效提高了污染物的预报能力,但改进数值模式自身能力和增加具有代表性的集合成员对预报水平的进一步提升十分关键。This study evaluated the performances of two numerical air-quality models(i.e.,CMAQ and CAMx)for the air pollutants forecast over Zhuhai based on the observations from national stations during 2018-2019.Moreover,two ensemble methods including Multiple Linear Regression(MLR)and Random Forest(RF)were employed to determine their improvement capabilities.The results show that the CMAQ outperformed the CAMx in reproducing the seasonal-daily variations of each air pollutant,but both of them showed obvious systematic biases,and presented poor performances in representing the diurnal and spatial variations of the air pollutants(except O_(3)).For example,the CMAQ reasonably reproduced the seasonal variations of CO,PM_(2.5),PM_(10),SO_(2),O_(3) and NO_(2),with correlation coefficients ranging from 0.72 to 0.84,while the NMB(RMSE)reached -0.58(0.40 mg/m^(3)),-0.18(6.86μg/m^(3)),-0.30(16.02μg/m^(3)),1.52(10.71μg/m^(3)),-0.16(25.05μg/m^(3))and-0.20(10.21μg/m^(3)),respectively.Meanwhile,the MLR and RF models,constructed based on all pollutants,effectively removed the systematic biases,and enhanced the strengths of the CAMx model,with the RF performing better.However,both of them did not improve these model defects much.Further analysis indicates that the impotency of CMAQ and CAMx was comparably important,suggesting that the prediction ability of the ensemble method is mainly related to the representativeness of different members,irrespective of their linear deviations.Finally,this study reveals that despite the effective improvement of the RF in predicting the pollutants,the key for further refinement of predicting capabilities is to improve numerical models’own abilities and incorporate more representative participating members.

关 键 词：空气质量模式 应用评估 集合方法 随机森林 

分 类 号：X16[环境科学与工程—环境科学]