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Meteorological Factors of Ozone Predictability at Houston , Texas

机译:德克萨斯州休斯顿臭氧可预测性的气象因素

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Several ozone modeling approaches were investigated to determine if uncertainties in the meteorological data would be sufficiently large to limit the application of physically realistic ozone (O3) forecast models. Three di- agnostic schemes were evaluated for the period of May through September 1997 for Houston, TX. Correlations between measured daily maximum and model calculated O3 air concentrations were found to be 0.70 using a linear regression model, 0.65 using a non-advective box model, and 0.49 using a three-dimensional (3-D) transport and dispersion model. Although the regression model had the highest correlation, it showed substantial underestimates of the highest concentrations. The box model results were the most similar to the regression model and did not show as much underestimation. The more complex 3-D mod- eling approach yielded the worst results, likely resulting from O3 maxima that were driven by local factors rather O3 maxima that were driven by local factors rather than by the transport of pollutants from outside of the Houston domain. The highest O3, concentrations at Hous- ton were associated with light winds and meandering tra- jectories. A comparison of the gridded meteorological data used by the 3-D model to the observations showed that the wind direction and speed values at Houston dif- fered most on those days on which the O3 underestima- tions were the greatest. These periods also tended to correspond with poor precipitation and temperature es- timates. It is concluded that better results are not just obtained through additional modeling complexi
机译:研究了几种臭氧建模方法,以确定气象数据中的不确定性是否足够大,从而限制了实际的臭氧(O3)预报模型的应用。对德克萨斯州休斯敦市从1997年5月到1997年9月的三个诊断方案进行了评估。使用线性回归模型,每日最高测量值与模型计算的O3空气浓度之间的相关性被发现为0.70,使用非平流框模型的相关性为0.65,而使用三维(3-D)传输和扩散模型的相关性为0.49。尽管回归模型具有最高的相关性,但它显示出最高浓度的明显低估。 Box模型的结果与回归模型最相似,并且没有显示出太多的低估。更为复杂的3-D建模方法产生的结果最糟,可能是由于O3最大值受局部因素驱动,而不是O3最大值受局部因素驱动,而不是由休斯顿域外的污染物迁移引起。休斯顿(Houston)的最高O3浓度与微风和曲折轨迹有关。将3-D模型使用的栅格化气象数据与观测值进行的比较表明,在O3低估量最大的那一天,休斯顿的风向和风速值差异最大。这些时期也往往与降水和温度估计差有关。结论是,不仅仅是通过额外的建模复杂度可以获得更好的结果

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