空气质量多模式系统在广州应用及对PM10预报效果评估  

Application of Air Quality Multi-Model Forecast System in Guangzhou: Model Description and Evaluation of PM10 Forecast Performance

作　　者：陈焕盛[1,2] 王自发[1] 吴其重[3] 吴剑斌[1,2] 晏平仲[1] 唐晓[1] 王哲[1,2] 

机构地区：[1]中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室,北京100029 [2]中国科学院研究生院,北京100049 [3]北京师范大学全球变化与地球系统科学学院,北京100875

出　　处：《气候与环境研究》2013年第4期427-435,共9页Climatic and Environmental Research

基　　金：国家高技术研究发展计划2006AA06A306

摘　　要：介绍了广州空气质量多模式系统并评估其对2010年9月广州市的气象要素和PM10日均浓度的24h的预报效果。评估结果表明:模式系统较好地预测了气象要素的变化,但高估了风速;各空气质量模式能合理预测广州PM10浓度的时空变化,预报效果均处于可接受范围内(平均分数偏差MFB小于±60%且平均分数误差MFE小于75%),部分模式可达到优秀水平(MFB小于±30%且MFE小于50%),但同时各模式在郊区均预测偏高而在市区偏低;总体上,模式在广州郊区的PM10预报效果优于市区。模式间对比表明,在本次业务预报实践中,不存在最优的单模式,同一模式对不同的统计指标、不同的站点,其预报效果可能存在差异,基于算术平均集成各模式结果未能获得最优的预报效果。优化排放源空间分布并引进更好的集成预报方法(如权重平均、神经网络、多元回归等)是未来改进广州空气质量多模式系统预报效果的可能途径。The air quality multi-model forecast system was introduced and its 24-h forecast performance for meteorological parameters and PM10 daily mean concentration in Guangzhou during September 2010 was evaluated. The results show that although wind speed is overestimated, the model system can effectively predict variation in the meteorological parameters. All air quality models analyzed are shown to reasonably predict temporal and spatial variations of PM10 daily mean concentration in Guangzhou. In addition, all model forecasts satisfy the performance criteria such that mean fractional bias errors are less than or equal to ±60% and 75%, respectively, and several even reachedperformance goals of less than or equal to ±30% and 50%, respectively. However, all model forecasts overestimate PM10 daily mean concentration in suburban Guangzhou while underestimating the value in the urban region. An optimal model in this operational air quality forecast is not detected through model intercomparison. Variety in stations and statistical indicators may result in significant differences in forecast performance for the same model. Moreover, model ensemble based on arithmetic average does not reveal optimal forecast performance. Optimization of spatial distribution of the emission and usage of improved model ensemble forecast methods such as weighted average, neural network, and multiple regressions may improve forecast performance of the air quality multi-model system.

关 键 词：空气质量 多模式 PM10 广州 亚运会 

分 类 号：X16[环境科学与工程—环境科学]