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Geostatistical analysis of the temporal variability of ozone concentrations.Comparison between CHIMERE model and surface observations

机译:臭氧浓度随时间变化的地统计分析CHIMERE模型与地表观测值的比较

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Deterministic models have become essential tools to forecast and map concentration fields of atmospheric pollutants like ozone. Those models are regularly updated and improved by incorporating recent theoretical developments and using more precise input data. Unavoidable differences with in situ measurements still remain, which need to be better understood. This study investigates those discrepancies in a geostatistical framework by comparing the temporal variability of ozone hourly surface concentrations simulated by a chemistry-transport model, CHIMERE, and measured across France. More than 200 rural and urban background monitoring sites are considered. The relationship between modelled and observed data is complex. Ozone concentrations evolve according to various time scales. CHIMERE correctly accounts for those different scales of variability but is usually unable to reproduce the exact magnitude of each temporal component. Such difficulty cannot be entirely attributed to the difference in spatial support between grid cell averages and punctual observations. As a result of this exploratory analysis, the common multivariate geostatistical model, known as the linear model of cor-egionalization, is used to describe the temporal variability of ozone hourly concentrations and the relationship between simulated and observed values at each observation point. The fitted parameters of the model can then be interpreted. Their distribution in space provides objective criteria to delimitate the areas where the chemistry-transport model is more or less reliable.
机译:确定性模型已经成为预测和绘制臭氧等大气污染物浓度场的重要工具。通过结合最新的理论发展并使用更精确的输入数据,可以定期更新和改进这些模型。现场测量仍然存在不可避免的差异,需要更好地理解。这项研究通过比较由化学运输模型CHIMERE模拟并在法国范围内测量的每小时臭氧表面浓度的时变性,研究了地统计学框架中的这些差异。考虑了200多个农村和城市背景监测点。建模数据和观察数据之间的关系很复杂。臭氧浓度根据各种时间尺度而变化。 CHIMERE正确地解释了这些不同的可变程度,但通常无法重现每个时间分量的确切大小。这种困难不能完全归因于网格单元平均值和守时观测值之间的空间支持差异。作为此探索性分析的结果,常用的多元地统计模型(称为线性拟合模型)用于描述臭氧小时浓度的时间变化以及每个观测点的模拟值与观测值之间的关系。然后可以解释模型的拟合参数。它们在空间上的分布提供了客观标准,以界定化学或运输模型或多或少可靠的区域。

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