首页> 外文会议>NATO/CCMS international technical meeting on air pollution modelling and its application >SENSITIVITY OF OZONE AND AEROSOL PREDICTIONS TO THE TRANSPORT ALGORITHMS IN THE MODELS-3 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODELING SYSTEM
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SENSITIVITY OF OZONE AND AEROSOL PREDICTIONS TO THE TRANSPORT ALGORITHMS IN THE MODELS-3 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODELING SYSTEM

机译:臭氧和气溶胶预测对运输算法的敏感性-3群落多尺度空气质量(CMAQ)建模系统中的运输算法

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We have demonstrated that the choice of modules in transport processes interacts with other model configurations. Comparison with observations, especially with secondary species such as O_3, may not be sufficient to allow selection of the best modules. For example, we have compared first layer ozone concentrations with those from EPA's AIRS database. Figure 5 shows that the configuration F36 has least bias compared with observations. However, this alone is not sufficient to determine which transport algorithms are superior. Factors such as the representation of emissions inputs, the treatment of plume-in-grid, the use of different chemical mechanisms, the selection of different chemical solvers, and the model grid structure (i.e., vertical and horizontal resolutions and domain size), all contribute to different model results. Establishment of the best configuration of science process modules in a comprehensive AQM requires balanced representations of transport algorithms with other physical and chemical processes.
机译:我们已经证明,运输过程中的模块选择与其他模型配置相互作用。与观察结果的比较,特别是与诸如O_3的次生物种,可能不足以允许选择最佳模块。例如,我们将第一层臭氧浓度与来自EPA的Airs数据库的浓度进行了比较。图5显示了与观察结果相比的配置F36最小偏差。然而,单独的这种情况不足以确定哪种运输算法优于上色。诸如排放投入的代表等因素,夹杂网格的处理,使用不同的化学机制,选择不同的化学求解器,以及模型网格结构(即垂直和水平分辨率和域尺寸),所有有助于不同的模型结果。在全面的AQM中建立最佳科学过程模块的配置需要与其他物理和化学过程的运输算法的平衡表示。

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