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ASSESSING DISPERSION MODEL PERFORMANCE TO SIMULATE AVERAGE CENTERLINE CONCENTRATION VALUES

机译:评估分散模型性能以模拟平均中心线浓度值

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Most operational atmospheric simulation models are deterministic. They provide estimates of the average time- and space-variations in the conditions (e.g., mesoscale meteorological models), or they provide estimates of the average time- and space-variations of effects (e.g., air quality models). The observations used to test the performance of these models are individual realizations (which can be envisioned as coming from some ideal ensemble), and are affected by stochastic variations unaccounted for within the simulation model. If we believe this, then it makes sense to ask the models to replicate average patterns seen in the observations, but it does not make sense to ask the models to replicate the effects of stochastic variations unaccounted for within the simulation model (e.g., observed maxima, or total variance). The American Society for Testing and Materials (ASTM), published in December 2000 D 6589, entitled, "Standard Guide for Statistical Evaluation of Atmospheric Dispersion Model Performance", to provide a framework for developing techniques that are useful for comparison of modeled and observed concentrations, that addresses the concern that models provide deterministic estimates of ensemble averages, and observations are individual realizations from imperfectly defined ensembles. The Guide suggest a two step process: Step 1) analyze the observations to provide average patterns for comparison with modeled patterns, and Step 2) employ bootstrap resampling when comparing these patterns, which accounts for uncertainties in performing Step 1, and provides a means for objectively testing whether differences seen are statistically significant. An example procedure is provided in the Annex of D 6589 for evaluating performance of models to estimate the average maximum ground-level centerline concentration. In the example procedure, observations having similar meteorological conditions are grouped together at each downwind distance. In the following discussion, we summarize the results obtained in applying the example procedure to test the performance of four plume dispersion models: ADMS 3.1( Carruthers et al., 1994), AERMOD (version 01247, Cimorelli et al., 1996), HPDM (version 4.3, level 920605, Hanna and Paine, 1989), and ISCST3 (version 00101, U.S. Environmental Protection Agency, 1995), with tracer field data from three studies: Prairie Grass (Barad, 1958; Haugen, 1959), Kincaid (Bowne et al., 1983), and Indianapolis (Murray and Bowne, 1988).
机译:大多数运营大气模拟模型是确定性的。它们提供了条件下的平均时间和空间变化的估计(例如,Mescre Mobeorogtom模型),或者它们提供估计的效果的平均时间和空间变化(例如,空气质量模型)。用于测试这些模型性能的观察结果是个人的实现(可以通过来自一些理想的集合的形式),并且受到模拟模型内未计算的随机变化的影响。如果我们相信这一点,请询问模型以复制观察中看到的平均模式是有意义的,但是要求模型复制在模拟模型内未占用的随机变化的影响(例如,观察到的最大值,或总方差)。美国的检测和材料协会(ASTM)于2000年12月D 6589发布,标题为“大气分散模型性能的统计评估标准指南”,提供了一种开发用于比较模型和观察浓度的技术的框架,这解决了模型提供了集合平均值的确定性估计的关注,并且观察是从不完美定义的集合的个人实现。该指南提出了两个步骤处理:步骤1)分析观察,以提供与建模模式进行比较的平均模式,并且步骤2)在比较这些模式时使用引导重采样,该模式考虑执行步骤1的不确定性,并提供一种手段客观地测试是否有统计学意义的差异。在D 6589的附件中提供了一个示例程序,用于评估模型的性能,以估计平均最大地面中心线浓度。在示例过程中,具有类似气象条件的观察结果在每个向下时距离一起分组。在下面的讨论中,我们总结了在应用示例过程中获得的结果,以测试四个羽流分散模型的性能:ADMS 3.1(Carruthers等,1994),Aermod(版本01247,Cimorelli等,1996),HPDM (版本4.3,920605级,汉纳和潘恩,1989年)和ISCST3(版本00101,美国环境保护局,1995),三项研究的示踪现场数据:草原草(巴拉德,1958; Haugen,1959),金属( Bowne等,1983年)和印第安纳波利斯(Murray和Bowne,1988年)。

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