Parameterization of urban subgrid scale processes in global atmospheric chemistry models
Parameterization of urban subgrid scale processes in global atmospheric chemistry models
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全球大气化学模型中城市亚网格尺度过程的参数化
DOI：
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发表时间：
1998
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通讯作者：
G. McRae
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作者：
J. Calbó;Wenwei Pan;M. Webster;R. Prinn;G. McRae
我们已经推导出一个参数化，该参数化由一组分析表达式组成，这些表达式近似于加州理工学院-卡内基梅隆大学（CIT）城市空气模型对环境净出口的预测（即，有效排放量）的几个化学物种，作为14个输入参数的函数。对于每一物种，有效排放量是该物种和其他物种的实际城市排放量以及其他城市领域属性（如气象学）的函数。有效排放可能是一次污染物的“老化”排放或二次污染物的实际产生。为了开发参数化，我们应用了概率配置方法，它使用的概率密度函数的输入生成一组正交多项式。然后，这些多项式被用作多项式混沌扩展的基础，该多项式混沌扩展近似CIT模型对其输入的实际响应。我们假设季节变化可以用正弦函数表示。参数化提供了实际模型行为的计算上非常有效的模拟。我们已经比较了CIT模型的输出的参数化的输出，我们的结论是，它给出了一个相当好的近似有效排放量，至少在该地区的输入参数的概率最高。这种参数化是适用于详细的不确定性和敏感性分析，并使计算效率高的城市规模的过程，作为亚网格尺度的现象，在全球范围内的模式。
We have derived a parameterization consisting of a set of analytical expressions that approximate the predictions by the California Institute of Technology - Carnegie-Mellon University (CIT) Urban Airshed Model for the net export to the environment (i.e., effective emissions) of several chemical species, as functions of 14 input parameters. For each species, effective emissions are a function of actual urban emissions of this and other species and of other urban domain properties such as meteorology. Effective emissions may be “aged” emissions of primary pollutants or actual production of secondary pollutants. To develop the parameterization we have applied the probabilistic collocation method, which uses the probability density functions of the inputs to generate a set of orthogonal polynomials. These polynomials are then used as the basis for a polynomial chaos expansion that approximates the actual response of the CIT model to its inputs. We assume that seasonal variations can be represented by sinusoidal functions. The parameterization provides a computationally very efficient simulation of the actual model behavior. We have compared the outputs of the parameterization with the outputs of the CIT model, and we conclude that it gives a quite good approximation for effective emissions, at least in the regions of highest probability of the input parameters. This parameterization is applicable to detailed uncertainty and sensitivity analyses and enables computationally efficient inclusion of urban-scale processes as subgrid scale phenomena in global-scale models.