• 关键词：MM5  STEM  珠三角  空气质量  数值模拟  数据同化  EnOI

• 基金项目：广东省自然基金重点项目（No.S2012020011044）；国家高技术研究发展计划（No.2013AA122002）；江苏省2011计划（气候变化协同创新中心）

• 张金谱
• 1. 中山大学环境科学与工程学院, 广州 510275;2. 广州市环境监测中心站, 广州 510030

• 胡嘉镗
• 中山大学环境科学与工程学院, 广州 510275

• 王雪梅
• 中山大学环境科学与工程学院, 广州 510275

• 摘要：本文旨在探讨集合最优插值（EnOI）同化方法对MM5-STEM空气质量模式污染物浓度预报场的修正能力，先从局地化尺度（L）及经验系数（α）的敏感性试验中获得NO₂、SO₂和PM₁₀各自的“最优L 和α组合”，然后对此参数设置下的同化结果进行分析.研究结果显示，EnOI在NO₂、SO₂及PM₁₀的同化试验中均取得较好的效果，检验站点均方根误差（RMSE）的平均下降比例分别可达33%、32%和42%，RMSE值下降的站点占检验站点总数的比例分别为86%、84%和91%.表明该方法能够有效地应用在珠三角空气质量模拟中，产生与实际更为接近的污染物浓度预报场.

• Abstract：This paper mainly evaluated the ability of Ensemble Optimal Interpolation Data Assimilation Method (EnOI) in modifying the pollutants concentration forecast field in MM5-STEM air quality numerical model. Optimal settings of localization scale (L) and empirical coefficient (α) of NO₂, SO₂ and PM₁₀ were acquired by sensitivity tests respectively. Assimilation results under these coefficient settings were analyzed, the results showed that EnOI had a good performance in the data assimilation experiments of NO₂, SO₂ and PM₁₀, with RMSE decreasing percentage of 33%, 32% and 42%, respectively. The proportion of verification stations with decreased RMSE were 86%, 84% and 91%, respectively, which proved that EnOI produced a pollutant concentration forecast field closer to the true situation, therefore can be effectively applied in air quality modeling in the PRD.

• Key words：MM5  STEM  Pearl River Delta Region  air quality  numerical modeling  data assimilation  EnOI

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